WorldWideScience

Sample records for monitor machine condition

  1. Support vector machine in machine condition monitoring and fault diagnosis

    Science.gov (United States)

    Widodo, Achmad; Yang, Bo-Suk

    2007-08-01

    Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

  2. Monitoring machining conditions by infrared images

    Science.gov (United States)

    Borelli, Joao E.; Gonzaga Trabasso, Luis; Gonzaga, Adilson; Coelho, Reginaldo T.

    2001-03-01

    During machining process the knowledge of the temperature is the most important factor in tool analysis. It allows to control main factors that influence tool use, life time and waste. The temperature in the contact area between the piece and the tool is resulting from the material removal in cutting operation and it is too difficult to be obtained because the tool and the work piece are in motion. One way to measure the temperature in this situation is detecting the infrared radiation. This work presents a new methodology for diagnosis and monitoring of machining processes with the use of infrared images. The infrared image provides a map in gray tones of the elements in the process: tool, work piece and chips. Each gray tone in the image corresponds to a certain temperature for each one of those materials and the relationship between the gray tones and the temperature is gotten by the previous of infrared camera calibration. The system developed in this work uses an infrared camera, a frame grabber board and a software composed of three modules. The first module makes the image acquisition and processing. The second module makes the feature image extraction and performs the feature vector. Finally, the third module uses fuzzy logic to evaluate the feature vector and supplies the tool state diagnostic as output.

  3. Proactive condition monitoring of low-speed machines

    CERN Document Server

    Stamboliska, Zhaklina; Moczko, Przemyslaw

    2015-01-01

    This book broadens readers’ understanding of proactive condition monitoring of low-speed machines in heavy industries. It focuses on why low-speed machines are different than others and how maintenance of these machines should be implemented with particular attention. The authors explain the best available monitoring techniques for various equipment and the principle of how to get proactive information from each technique. They further put forward possible strategies for application of FEM for detection of faults and technical assessment of machinery. Implementation phases are described and industrial case-studies of proactive condition monitoring are included. Proactive Condition Monitoring of Low-Speed Machines is an essential resource for engineers and technical managers across a range of industries as well as design engineers working in industrial product development. This book also: ·         Explains the practice of proactive condition monitoring and illustrates implementation phases ·   ...

  4. Thermal Analysis for Condition Monitoring of Machine Tool Spindles

    International Nuclear Information System (INIS)

    Clough, D; Fletcher, S; Longstaff, A P; Willoughby, P

    2012-01-01

    Decreasing tolerances on parts manufactured, or inspected, on machine tools increases the requirement to have a greater understanding of machine tool capabilities, error sources and factors affecting asset availability. Continuous usage of a machine tool during production processes causes heat generation typically at the moving elements, resulting in distortion of the machine structure. These effects, known as thermal errors, can contribute a significant percentage of the total error in a machine tool. There are a number of design solutions available to the machine tool builder to reduce thermal error including, liquid cooling systems, low thermal expansion materials and symmetric machine tool structures. However, these can only reduce the error not eliminate it altogether. It is therefore advisable, particularly in the production of high value parts, for manufacturers to obtain a thermal profile of their machine, to ensure it is capable of producing in tolerance parts. This paper considers factors affecting practical implementation of condition monitoring of the thermal errors. In particular is the requirement to find links between temperature, which is easily measureable during production and the errors which are not. To this end, various methods of testing including the advantages of thermal images are shown. Results are presented from machines in typical manufacturing environments, which also highlight the value of condition monitoring using thermal analysis.

  5. Monitoring machining conditions by analyzing cutting force vibration

    Energy Technology Data Exchange (ETDEWEB)

    Piao, Chun Guang; Kim, Ju Wan; Kim, Jin Oh; Shin, Yoan [Soongsl University, Seoul (Korea, Republic of)

    2015-09-15

    This paper deals with an experimental technique for monitoring machining conditions by analyzing cutting-force vibration measured at a milling machine. This technique is based on the relationship of the cutting-force vibrations with the feed rate and cutting depth as reported earlier. The measurement system consists of dynamic force transducers and a signal amplifier. The analysis system includes an oscilloscope and a computer with a LabVIEW program. Experiments were carried out at various feed rates and cutting depths, while the rotating speed was kept constant. The magnitude of the cutting force vibration component corresponding to the number of cutting edges multiplied by the frequency of rotation was linearly correlated with the machining conditions. When one condition of machining is known, another condition can be identified by analyzing the cutting-force vibration.

  6. Monitoring machining conditions by analyzing cutting force vibration

    International Nuclear Information System (INIS)

    Piao, Chun Guang; Kim, Ju Wan; Kim, Jin Oh; Shin, Yoan

    2015-01-01

    This paper deals with an experimental technique for monitoring machining conditions by analyzing cutting-force vibration measured at a milling machine. This technique is based on the relationship of the cutting-force vibrations with the feed rate and cutting depth as reported earlier. The measurement system consists of dynamic force transducers and a signal amplifier. The analysis system includes an oscilloscope and a computer with a LabVIEW program. Experiments were carried out at various feed rates and cutting depths, while the rotating speed was kept constant. The magnitude of the cutting force vibration component corresponding to the number of cutting edges multiplied by the frequency of rotation was linearly correlated with the machining conditions. When one condition of machining is known, another condition can be identified by analyzing the cutting-force vibration

  7. Electric machines modeling, condition monitoring, and fault diagnosis

    CERN Document Server

    Toliyat, Hamid A; Choi, Seungdeog; Meshgin-Kelk, Homayoun

    2012-01-01

    With countless electric motors being used in daily life, in everything from transportation and medical treatment to military operation and communication, unexpected failures can lead to the loss of valuable human life or a costly standstill in industry. To prevent this, it is important to precisely detect or continuously monitor the working condition of a motor. Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis reviews diagnosis technologies and provides an application guide for readers who want to research, develop, and implement a more effective fault diagnosis and condi

  8. ANN Based Tool Condition Monitoring System for CNC Milling Machines

    Directory of Open Access Journals (Sweden)

    Mota-Valtierra G.C.

    2011-10-01

    Full Text Available Most of the companies have as objective to manufacture high-quality products, then by optimizing costs, reducing and controlling the variations in its production processes it is possible. Within manufacturing industries a very important issue is the tool condition monitoring, since the tool state will determine the quality of products. Besides, a good monitoring system will protect the machinery from severe damages. For determining the state of the cutting tools in a milling machine, there is a great variety of models in the industrial market, however these systems are not available to all companies because of their high costs and the requirements of modifying the machining tool in order to attach the system sensors. This paper presents an intelligent classification system which determines the status of cutt ers in a Computer Numerical Control (CNC milling machine. This tool state is mainly detected through the analysis of the cutting forces drawn from the spindle motors currents. This monitoring system does not need sensors so it is no necessary to modify the machine. The correct classification is made by advanced digital signal processing techniques. Just after acquiring a signal, a FIR digital filter is applied to the data to eliminate the undesired noisy components and to extract the embedded force components. A Wavelet Transformation is applied to the filtered signal in order to compress the data amount and to optimize the classifier structure. Then a multilayer perceptron- type neural network is responsible for carrying out the classification of the signal. Achieving a reliability of 95%, the system is capable of detecting breakage and a worn cutter.

  9. Combining discrepancy analysis with sensorless signal resampling for condition monitoring of rotating machines under fluctuating operations

    CSIR Research Space (South Africa)

    Heyns, T

    2012-12-01

    Full Text Available This paper proposes a novel framework for monitoring the condition of a rotating machine (for example a gearbox or a bearing) that may be subject to load and speed fluctuations. The methodology is especially relevant in situations where no (or only...

  10. Machine and lubricant condition monitoring for extended equipment lifetimes and predictive maintenance

    Energy Technology Data Exchange (ETDEWEB)

    Lukas, M; Anderson, D P [Spectro Incorporated, Littleton, Massachusetts (United States)

    1998-12-31

    Predictive maintenance has gained wide acceptance as a cost cutting strategy in modern industry. Condition monitoring by lubricant analysis is one of the basic tools of a predictive maintenance program along with vibration monitoring, performance monitoring and thermography. In today`s modern power generation, manufacturing, refinery, transportation, mining, and military operations, the cost of equipment maintenance, service, and lubricants are ever increasing. Parts, labor, equipment downtime and lubricant prices and disposal costs are a primary concern in a well run maintenance management program. Machine condition monitoring based on oil analysis has become a prerequisite in most maintenance programs. Few operations can afford not to implement a program if they wish to remain competitive, and in some cases, profitable. This presentation describes a comprehensive Machine Condition Monitoring Program based on oil analysis. Actual operational condition monitoring programs will be used to review basic components and analytical requirements. Case histories will be cited as examples of cost savings, reduced equipment downtime and increased efficiencies of maintenance programs through a well managed oil analysis program. (orig.)

  11. Machine and lubricant condition monitoring for extended equipment lifetimes and predictive maintenance

    Energy Technology Data Exchange (ETDEWEB)

    Lukas, M.; Anderson, D.P. [Spectro Incorporated, Littleton, Massachusetts (United States)

    1997-12-31

    Predictive maintenance has gained wide acceptance as a cost cutting strategy in modern industry. Condition monitoring by lubricant analysis is one of the basic tools of a predictive maintenance program along with vibration monitoring, performance monitoring and thermography. In today`s modern power generation, manufacturing, refinery, transportation, mining, and military operations, the cost of equipment maintenance, service, and lubricants are ever increasing. Parts, labor, equipment downtime and lubricant prices and disposal costs are a primary concern in a well run maintenance management program. Machine condition monitoring based on oil analysis has become a prerequisite in most maintenance programs. Few operations can afford not to implement a program if they wish to remain competitive, and in some cases, profitable. This presentation describes a comprehensive Machine Condition Monitoring Program based on oil analysis. Actual operational condition monitoring programs will be used to review basic components and analytical requirements. Case histories will be cited as examples of cost savings, reduced equipment downtime and increased efficiencies of maintenance programs through a well managed oil analysis program. (orig.)

  12. Investigation into the effect of fixturing systems on the design of condition monitoring for machining operations

    OpenAIRE

    Abbas, JK

    2013-01-01

    The global market competition has drawn the manufacturer’s attention on automated manufacturing processes using condition monitoring systems. These systems have been used for improving product quality, eliminating inspection, and enhancing manufacturing productivity. Fixtures are essential devices in machining processes to hold the tool or workpiece, hence they are influenced directly by the stability of the cutting tool. Therefore, tool and fixturing faults play an important part in the inac...

  13. A New Application of Support Vector Machine Method: Condition Monitoring and Analysis of Reactor Coolant Pump

    International Nuclear Information System (INIS)

    Meng Qinghu; Meng Qingfeng; Feng Wuwei

    2012-01-01

    Fukushima nuclear power plant accident caused huge losses and pollution and it showed that the reactor coolant pump is very important in a nuclear power plant. Therefore, to keep the safety and reliability, the condition of the coolant pump needs to be online condition monitored and fault analyzed. In this paper, condition monitoring and analysis based on support vector machine (SVM) is proposed. This method is just to aim at the small sample studies such as reactor coolant pump. Both experiment data and field data are analyzed. In order to eliminate the noise and useless frequency, these data are disposed through a multi-band FIR filter. After that, a fault feature selection method based on principal component analysis is proposed. The related variable quantity is changed into unrelated variable quantity, and the dimension is descended. Then the SVM method is used to separate different fault characteristics. Firstly, this method is used as a two-kind classifier to separate each two different running conditions. Then the SVM is used as a multiple classifier to separate all of the different condition types. The SVM could separate these conditions successfully. After that, software based on SVM was designed for reactor coolant pump condition analysis. This software is installed on the reactor plant control system of Qinshan nuclear power plant in China. It could monitor the online data and find the pump mechanical fault automatically.

  14. Fault Diagnosis and Condition Monitoring of an All Geared Lathe Machine Using Piezoelectric Sensor

    Directory of Open Access Journals (Sweden)

    Amiya BHAUMIK

    2008-12-01

    Full Text Available Undesired vibrations are serious problems that affect and deteriorate the quality of the product. This paper investigates dynamic and vibrational characteristics of a newly installed All Geared Lathe Machine with piezoelectric sensor. A comparison is drawn with the data measured and acceptable data as per ISO 10816 and thus concluded that the machine is in working condition.

  15. An illustration of new methods in machine condition monitoring, Part I: stochastic resonance

    International Nuclear Information System (INIS)

    Worden, K.; Antoniadou, I.; Marchesiello, S.; Mba, C.; Garibaldi, L.

    2017-01-01

    There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damage-sensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-of-the-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The first paper in the pair will deal with feature extraction. Although some papers have appeared in the recent past considering stochastic resonance as a means of amplifying damage information in signals, they have largely relied on ad hoc specifications of the resonator used. In contrast, the current paper will adopt a principled optimisation-based approach to the resonator design. The paper will also show that a discrete dynamical system can provide all the benefits of a continuous system, but also provide a considerable speed-up in terms of simulation time in order to facilitate the optimisation approach. (paper)

  16. The development of condition monitoring for the safety of rotating machine in PWR using motor current signature analysis

    International Nuclear Information System (INIS)

    Syaiful Bakhri

    2013-01-01

    Condition monitoring of rotating machine is essential to guarantee the safety operation as well as to improve the efficiency of nuclear power plants operations. One of the promising condition monitoring techniques which has been preferred currently since it is simple, non-invasive and inexpensive is Motor Stator Signature Analysis (MCSA). However, the investigation of the MCSA technique using a compact, low cost, and having industrial class hardware which is capable for nuclear power plant applications has been limited. The research is aimed to develop condition monitoring method based on MCSA utilizing a compact industrial class for nuclear power plant. The investigation includes development of condition monitoring based on real-time FPGA-CompatRIO hardware, development of a custom built display module for early warning system, testing of the monitoring hardware, fault frequency analysis of electric motors including the performances of fault detections. The condition monitoring system is able to execute a fault detection task around 164 ms, to recognize accurately fault frequencies of stator shorted turn for about 75%, broken rotor bar around 95%, eccentricity 65%, mechanical misalignment 85%, including supply voltage unbalances 100%. The condition monitoring system based on its performance assessments could become a suitable alternative not only for rotating machines but also condition monitoring for other nuclear reactor components. (author)

  17. An intelligent condition monitoring system for on-line classification of machine tool wear

    Energy Technology Data Exchange (ETDEWEB)

    Pan, Fu; Hope, A D; Javed, M [Systems Engineering Faculty, Southampton Institute (United Kingdom)

    1998-12-31

    The development of intelligent tool condition monitoring systems is a necessary requirement for successful automation of manufacturing processes. This presentation introduces a tool wear monitoring system for milling operations. The system utilizes power, force, acoustic emission and vibration sensors to monitor tool condition comprehensively. Features relevant to tool wear are drawn from time and frequency domain signals and a fuzzy pattern recognition technique is applied to combine the multisensor information and provide reliable classification results of tool wear states. (orig.) 10 refs.

  18. An intelligent condition monitoring system for on-line classification of machine tool wear

    Energy Technology Data Exchange (ETDEWEB)

    Fu Pan; Hope, A.D.; Javed, M. [Systems Engineering Faculty, Southampton Institute (United Kingdom)

    1997-12-31

    The development of intelligent tool condition monitoring systems is a necessary requirement for successful automation of manufacturing processes. This presentation introduces a tool wear monitoring system for milling operations. The system utilizes power, force, acoustic emission and vibration sensors to monitor tool condition comprehensively. Features relevant to tool wear are drawn from time and frequency domain signals and a fuzzy pattern recognition technique is applied to combine the multisensor information and provide reliable classification results of tool wear states. (orig.) 10 refs.

  19. A comparison study of support vector machines and hidden Markov models in machinery condition monitoring

    International Nuclear Information System (INIS)

    Miao, Qiang; Huang, Hong Zhong; Fan, Xianfeng

    2007-01-01

    Condition classification is an important step in machinery fault detection, which is a problem of pattern recognition. Currently, there are a lot of techniques in this area and the purpose of this paper is to investigate two popular recognition techniques, namely hidden Markov model and support vector machine. At the beginning, we briefly introduced the procedure of feature extraction and the theoretical background of this paper. The comparison experiment was conducted for gearbox fault detection and the analysis results from this work showed that support vector machine has better classification performance in this area

  20. On-line Cutting Tool Condition Monitoring in Machining Processes Using Artificial Intelligence

    OpenAIRE

    Vallejo, Antonio J.; Morales-Menéndez, Rub&#;n; Alique, J.R.

    2008-01-01

    This chapter presented new ideas for monitoring and diagnosis of the cutting tool condition with two different algorithms for pattern recognition: HMM, and ANN. The monitoring and diagnosis system was implemented for peripheral milling process in HSM, where several Aluminium alloys and cutting tools were used. The flank wear (VB) was selected as the criterion to evaluate the tool's life and four cutting tool conditions were defined to be recognized: New, half new, half worn, and worn conditio...

  1. Condition monitoring of PARR-1 rotating machines by vibration analysis technique

    Directory of Open Access Journals (Sweden)

    Qadir Javed

    2014-01-01

    Full Text Available Vibration analysis is a key tool for preventive maintenance involving the trending and analysis of machinery performance parameters to detect and identify developing problems before failure and extensive damage can occur. A lab-based experimental setup has been established for obtaining fault-free and fault condition data. After this analysis, primary and secondary motor and pump vibration data of the Pakistan Research Reactor-1 were obtained and analyzed. Vibration signatures were acquired in horizontal, vertical, and axial directions. The 48 vibration signatures have been analyzed to assess the operational status of motors and pumps. The vibration spectrum has been recorded for a 2000 Hz frequency span with a 3200 lines resolution. The data collected should be helpful in future Pakistan Research Reactor-1 condition monitoring.

  2. Prediction of Machine Tool Condition Using Support Vector Machine

    International Nuclear Information System (INIS)

    Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

    2011-01-01

    Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

  3. Process Condition Monitoring of Micro Moulding Using a Two-plunger Micro Injection Moulding Machine

    DEFF Research Database (Denmark)

    Tosello, Guido; Hansen, Hans Nørgaard; Guerrier, Patrick

    2010-01-01

    The influence of micro injection moulding (µIM) process parameters (melt and mould temperature, piston injection speed and stoke length) on the injection pressure was investigated using Design of Experiments. Direct piston injection pressure measurements were performed and data collected using...... a micro injection moulding machine equipped with a two-pluger injection unit. Miniaturized dog-bone shaped speciments on polyoxymethylene (POM) were moulded over a wide range of processing cpnditions in order to characterize the process and assess its capability. Experimental results obtained under...

  4. Tool wear and breakage monitoring in machining

    International Nuclear Information System (INIS)

    Madl, J.

    1992-01-01

    Risk minimization of metal cutting operations is one of the main problems of metal cutting technology. This paper describes some aspects in monitoring and control of machining processes. Tool monitoring is the fokus of machining process monitoring. Tool breakage and tool life recognition are the main problems of tool monitoring. All problems of this type of monitoring have not yet been fully solved. (orig.)

  5. Machinery condition monitoring principles and practices

    CERN Document Server

    Mohanty, Amiya Ranjan

    2015-01-01

    Find the Fault in the MachinesDrawing on the author's more than two decades of experience with machinery condition monitoring and consulting for industries in India and abroad, Machinery Condition Monitoring: Principles and Practices introduces the practicing engineer to the techniques used to effectively detect and diagnose faults in machines. Providing the working principle behind the instruments, the important elements of machines as well as the technique to understand their conditions, this text presents every available method of machine fault detection occurring in machines in general, an

  6. Improved thermal monitoring of rotating machine insulation

    International Nuclear Information System (INIS)

    Stone, G.C.; Sedding, H.G.; Bernstein, B.S.

    1991-01-01

    Aging of motor and generator insulation is most often induced as a result of operation at high temperatures. In spite of this knowledge, stator and rotor temperatures are only crudely monitored in existing machines. In EPRI project RP2577-1, three new means of detecting machine temperatures were successfully developed. Two of the techniques, the Electronic Rotor Temperature Sensor and the Passive Rotor Temperature Sensor, were specifically developed to give point temperature readings on turbine generator rotor windings. The Insulation Sniffer allows operators to determine when any electrical insulation in a motor is overheating. Another electronic device, called the Thermal Life Indicator, helps operators and maintenance personnel determine how accumulated operation has affected the remaining life of the insulation in rotating machines. These new devices permit nuclear station operators to avoid hazardous operating conditions and will help to determine priorities for maintenance and plant life extension programs

  7. Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)

    2014-08-15

    The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.

  8. Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

    International Nuclear Information System (INIS)

    Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka

    2014-01-01

    The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology

  9. Monitoring of large rotating machines at EDF

    International Nuclear Information System (INIS)

    Chevalier, R.; Oswald, G.P.; Morel, J.

    1993-09-01

    The purpose of equipment surveillance is the prevention of major risks, the early detection of abnormal conditions and post-incident analysis to correct faults observed. At EDF, overall vibration monitoring in the control room was supplemented by a special vibration monitoring system. However, in order to satisfy more elaborate, real time detection requirements and benefit from the new possibilities offered by computer-based systems, EDF has developed the PSAD concept (Surveillance and Diagnosis-aid Station) which groups surveillance processing, organized on surveillance functions including turbogenerator and reactor coolant pump surveillance. The purpose of the present paper is to describe the turbogenerator and reactor coolant pump surveillance functions and present the first examples of reactor coolant pump behaviour feedback using a PSAD mockup (Automated Surveillance of Rotating Machines). In the first place, surveillance implies determining exactly what has to be monitored. This entails considering incidents liable to affect machine behaviour and, of course, specifying both the vibration quantities and those defining the operating condition of the machine considered which are necessary to be able to interpret the vibrations. Data processing requirements concern detection of faults and diagnosis aids. Faults detection must be automatic, but not the diagnosis function. Data can be processed to evidence one or several faults, using the most appropriate data display system. Interpretation is then entrusted to experts. To satisfy the above requirements, the PSAD system integrates two new concepts: distributed surveillance, involving depth distribution (different layers of software organized for increasingly sophisticated and gradually narrowing data processing) and space distribution (the work is performed in the most appropriate place, whether this be the plant, with automatic real time processing, or elsewhere if the complexity of the diagnosis so requires

  10. Monitoring large rotating machines at EDF

    International Nuclear Information System (INIS)

    Chevalier, R.; Bourgeois, P.; Le Reverend, D.

    1992-09-01

    At Electricite de France (EDF), since 1978, the operating instruments which ensure the DETECTION function, have been completed on turbogenerators by a specialized ''off-line'' vibration monitoring system, which allows a posteriori DIAGNOSIS analysis. However because of a need of a real time and more elaborated DETECTION function, the concept of the Monitoring and Diagnosis Aid Station (Poste de Surveillance et d'Aide au Diagnostic: PSAD) has been developed. It federates the processing of monitoring, organized into several functions, and includes the monitoring of turbogenerators (TGS) and reactor coolant pumps (RCP). The purpose of this paper is to present, on the one hand, the monitoring functions of TGS and RCP and on the other, the first experimental results on the behaviour of three RCP, obtained through a SAMT (Surveillance Automatisee des Machines Tournantes - Automatic monitoring of rotating machines) prototype. (authors). 2 figs., 4 tabs., 4 refs

  11. Moved range monitor of a refueling machine

    International Nuclear Information System (INIS)

    Nakajima, Masaaki; Sakanaka, Tadao; Kayano, Hiroyuki.

    1976-01-01

    Purpose: To incorporate light receiving and emitting elements in a face monitor to thereby increase accuracy and reliability to facilitate handling in the refueling of a BWR power plant. Constitution: In the present invention, a refueling machine and a face monitoring light receiving and emitting elements are analogously coupled whereby the face monitoring light receiving and emitting elements may be moved so as to be analogous to a route along which the refueling machine has moved. A shielding plate is positioned in the middle of the light receiving and emitting elements, and the shielding plate is machined so as to be outside of action. The range of action of the refueling machine may be monitored depending on the light receiving state of the light receiving element. Since the present invention utilizes the permeating light as described above, it is possible to detect positions more accurately than the mechanical switch. In addition, the detection section is of the non-contact system and the light receiving element comprises a hot cell, and therefore the service life is extended and the reliability is high. (Nakamura, S.)

  12. Indirect Tire Monitoring System - Machine Learning Approach

    Science.gov (United States)

    Svensson, O.; Thelin, S.; Byttner, S.; Fan, Y.

    2017-10-01

    The heavy vehicle industry has today no requirement to provide a tire pressure monitoring system by law. This has created issues surrounding unknown tire pressure and thread depth during active service. There is also no standardization for these kind of systems which means that different manufacturers and third party solutions work after their own principles and it can be hard to know what works for a given vehicle type. The objective is to create an indirect tire monitoring system that can generalize a method that detect both incorrect tire pressure and thread depth for different type of vehicles within a fleet without the need for additional physical sensors or vehicle specific parameters. The existing sensors that are connected communicate through CAN and are interpreted by the Drivec Bridge hardware that exist in the fleet. By using supervised machine learning a classifier was created for each axle where the main focus was the front axle which had the most issues. The classifier will classify the vehicles tires condition and will be implemented in Drivecs cloud service where it will receive its data. The resulting classifier is a random forest implemented in Python. The result from the front axle with a data set consisting of 9767 samples of buses with correct tire condition and 1909 samples of buses with incorrect tire condition it has an accuracy of 90.54% (0.96%). The data sets are created from 34 unique measurements from buses between January and May 2017. This classifier has been exported and is used inside a Node.js module created for Drivecs cloud service which is the result of the whole implementation. The developed solution is called Indirect Tire Monitoring System (ITMS) and is seen as a process. This process will predict bad classes in the cloud which will lead to warnings. The warnings are defined as incidents. They contain only the information needed and the bandwidth of the incidents are also controlled so incidents are created within an

  13. Acoustic monitoring of rotating machine by advanced signal processing technology

    International Nuclear Information System (INIS)

    Kanemoto, Shigeru

    2010-01-01

    The acoustic data remotely measured by hand held type microphones are investigated for monitoring and diagnosing the rotational machine integrity in nuclear power plants. The plant operator's patrol monitoring is one of the important activities for condition monitoring. However, remotely measured sound has some difficulties to be considered for precise diagnosis or quantitative judgment of rotating machine anomaly, since the measurement sensitivity is different in each measurement, and also, the sensitivity deteriorates in comparison with an attached type sensor. Hence, in the present study, several advanced signal processing methods are examined and compared in order to find optimum anomaly monitoring technology from the viewpoints of both sensitivity and robustness of performance. The dimension of pre-processed signal feature patterns are reduced into two-dimensional space for the visualization by using the standard principal component analysis (PCA) or the kernel based PCA. Then, the normal state is classified by using probabilistic neural network (PNN) or support vector data description (SVDD). By using the mockup test facility of rotating machine, it is shown that the appropriate combination of the above algorithms gives sensitive and robust anomaly monitoring performance. (author)

  14. Beam Loss Monitoring for LHC Machine Protection

    Science.gov (United States)

    Holzer, Eva Barbara; Dehning, Bernd; Effnger, Ewald; Emery, Jonathan; Grishin, Viatcheslav; Hajdu, Csaba; Jackson, Stephen; Kurfuerst, Christoph; Marsili, Aurelien; Misiowiec, Marek; Nagel, Markus; Busto, Eduardo Nebot Del; Nordt, Annika; Roderick, Chris; Sapinski, Mariusz; Zamantzas, Christos

    The energy stored in the nominal LHC beams is two times 362 MJ, 100 times the energy of the Tevatron. As little as 1 mJ/cm3 deposited energy quenches a magnet at 7 TeV and 1 J/cm3 causes magnet damage. The beam dumps are the only places to safely dispose of this beam. One of the key systems for machine protection is the beam loss monitoring (BLM) system. About 3600 ionization chambers are installed at likely or critical loss locations around the LHC ring. The losses are integrated in 12 time intervals ranging from 40 μs to 84 s and compared to threshold values defined in 32 energy ranges. A beam abort is requested when potentially dangerous losses are detected or when any of the numerous internal system validation tests fails. In addition, loss data are used for machine set-up and operational verifications. The collimation system for example uses the loss data for set-up and regular performance verification. Commissioning and operational experience of the BLM are presented: The machine protection functionality of the BLM system has been fully reliable; the LHC availability has not been compromised by false beam aborts.

  15. Monitoring coordinate measuring machines by calibrated parts

    International Nuclear Information System (INIS)

    Weckenmann, A; Lorz, J

    2005-01-01

    Coordinate measuring machines (CMM) are essential for quality assurance and production control in modern manufacturing. Due to the necessity of assuring traceability during the use of CMM, interim checks with calibrated objects carried out periodically. For this purpose usually special artefacts like standardized ball plates, hole plates, ball bars or step gages are measured. Measuring calibrated series parts would be more advantageous. Applying the substitution method of ISO 15530-3: 2000 such parts can be used. It is less cost intensive and less time consuming than measuring expensive special standardized objects in special programmed measurement routines. Moreover, the measurement results can directly compare with the calibration values; thus, direct information on systematic measurement deviations and uncertainty of the measured features are available. The paper describes a procedure for monitoring horizontal-arm CMMs with calibrated sheet metal series parts

  16. Condition Indicators for Gearbox Condition Monitoring Systems

    Directory of Open Access Journals (Sweden)

    P. Večeř

    2005-01-01

    Full Text Available Condition monitoring systems for manual transmissions based on vibration diagnostics are widely applied in industry. The systems deal with various condition indicators, most of which are focused on a specific type of gearbox fault. Frequently used condition indicators (CIs are described in this paper. The ability of a selected condition indicator to describe the degree of gearing wear was tested using vibration signals acquired during durability testing of manual transmission with helical gears. 

  17. Quaternion Based Thermal Condition Monitoring System

    Science.gov (United States)

    Wong, Wai Kit; Loo, Chu Kiong; Lim, Way Soong; Tan, Poi Ngee

    In this paper, we will propose a new and effective machine condition monitoring system using log-polar mapper, quaternion based thermal image correlator and max-product fuzzy neural network classifier. Two classification characteristics namely: peak to sidelobe ratio (PSR) and real to complex ratio of the discrete quaternion correlation output (p-value) are applied in the proposed machine condition monitoring system. Large PSR and p-value observe in a good match among correlation of the input thermal image with a particular reference image, while small PSR and p-value observe in a bad/not match among correlation of the input thermal image with a particular reference image. In simulation, we also discover that log-polar mapping actually help solving rotation and scaling invariant problems in quaternion based thermal image correlation. Beside that, log-polar mapping can have a two fold of data compression capability. Log-polar mapping can help smoother up the output correlation plane too, hence makes a better measurement way for PSR and p-values. Simulation results also show that the proposed system is an efficient machine condition monitoring system with accuracy more than 98%.

  18. Tool path strategy and cutting process monitoring in intelligent machining

    Science.gov (United States)

    Chen, Ming; Wang, Chengdong; An, Qinglong; Ming, Weiwei

    2018-06-01

    Intelligent machining is a current focus in advanced manufacturing technology, and is characterized by high accuracy and efficiency. A central technology of intelligent machining—the cutting process online monitoring and optimization—is urgently needed for mass production. In this research, the cutting process online monitoring and optimization in jet engine impeller machining, cranio-maxillofacial surgery, and hydraulic servo valve deburring are introduced as examples of intelligent machining. Results show that intelligent tool path optimization and cutting process online monitoring are efficient techniques for improving the efficiency, quality, and reliability of machining.

  19. Vibration-based condition monitoring industrial, aerospace and automotive applications

    CERN Document Server

    Randall, Robert Bond

    2010-01-01

    ""Without doubt the best modern and up-to-date text on the topic, wirtten by one of the world leading experts in the field. Should be on the desk of any practitioner or researcher involved in the field of Machine Condition Monitoring"" Simon Braun, Israel Institute of Technology Explaining complex ideas in an easy to understand way, Vibration-based Condition Monitoring provides a comprehensive survey of the application of vibration analysis to the condition monitoring of machines. Reflecting the natural progression of these systems by presenting the fundamental material

  20. Acoustic multivariate condition monitoring - AMCM

    Energy Technology Data Exchange (ETDEWEB)

    Rosenhave, P E [Vestfold College, Maritime Dept., Toensberg (Norway)

    1998-12-31

    In Norway, Vestfold College, Maritime Department presents new opportunities for non-invasive, on- or off-line acoustic monitoring of rotating machinery such as off-shore pumps and diesel engines. New developments within acoustic sensor technology coupled with chemometric data analysis of complex signals now allow condition monitoring of hitherto unavailable flexibility and diagnostic specificity. Chemometrics paired with existing knowledge yields a new and powerful tool for condition monitoring. By the use of multivariate techniques and acoustics it is possible to quantify wear and tear as well as predict the performance of working components in complex machinery. This presentation describes the AMCM method and one result of a feasibility study conducted onboard the LPG/C `Norgas Mariner` owned by Norwegian Gas Carriers as (NGC), Oslo. (orig.) 6 refs.

  1. Acoustic multivariate condition monitoring - AMCM

    Energy Technology Data Exchange (ETDEWEB)

    Rosenhave, P.E. [Vestfold College, Maritime Dept., Toensberg (Norway)

    1997-12-31

    In Norway, Vestfold College, Maritime Department presents new opportunities for non-invasive, on- or off-line acoustic monitoring of rotating machinery such as off-shore pumps and diesel engines. New developments within acoustic sensor technology coupled with chemometric data analysis of complex signals now allow condition monitoring of hitherto unavailable flexibility and diagnostic specificity. Chemometrics paired with existing knowledge yields a new and powerful tool for condition monitoring. By the use of multivariate techniques and acoustics it is possible to quantify wear and tear as well as predict the performance of working components in complex machinery. This presentation describes the AMCM method and one result of a feasibility study conducted onboard the LPG/C `Norgas Mariner` owned by Norwegian Gas Carriers as (NGC), Oslo. (orig.) 6 refs.

  2. Tolkku - a toolbox for decision support from condition monitoring data

    International Nuclear Information System (INIS)

    Saarela, Olli; Lehtonen, Mikko; Halme, Jari; Aikala, Antti; Raivio, Kimmo

    2012-01-01

    This paper describes a software toolbox (a software library) designed for condition monitoring and diagnosis of machines. This toolbox implements both new methods and prior art and is aimed for practical down-to-earth data analysis work. The target is to improve knowledge of the operation and behaviour of machines and processes throughout their entire life-cycles. The toolbox supports different phases of condition based maintenance with tools that extract essential information and automate data processing. The paper discusses principles that have guided toolbox design and the implemented toolbox structure. Case examples are used to illustrate how condition monitoring applications can be built using the toolbox. In the first case study the toolbox is applied to fault detection of industrial centrifuges based on measured electrical current. The second case study outlines an application for centralized monitoring of a fleet of machines that supports organizational learning.

  3. Effect of the Machined Surfaces of AISI 4337 Steel to Cutting Conditions on Dry Machining Lathe

    Science.gov (United States)

    Rahim, Robbi; Napid, Suhardi; Hasibuan, Abdurrozzaq; Rahmah Sibuea, Siti; Yusmartato, Y.

    2018-04-01

    The objective of the research is to obtain a cutting condition which has a good chance of realizing dry machining concept on AISI 4337 steel material by studying surface roughness, microstructure and hardness of machining surface. The data generated from the experiment were then processed and analyzed using the standard Taguchi method L9 (34) orthogonal array. Testing of dry and wet machining used surface test and micro hardness test for each of 27 test specimens. The machining results of the experiments showed that average surface roughness (Raavg) was obtained at optimum cutting conditions when VB 0.1 μm, 0.3 μm and 0.6 μm respectively 1.467 μm, 2.133 μm and 2,800 μm fo r dry machining while which was carried out by wet machining the results obtained were 1,833 μm, 2,667 μm and 3,000 μm. It can be concluded that dry machining provides better surface quality of machinery results than wet machining. Therefore, dry machining is a good choice that may be realized in the manufacturing and automotive industries.

  4. Monitoring Thermal Conditions in Footwear

    Science.gov (United States)

    Silva-Moreno, Alejandra. A.; Lopez Vela, Martín; Alcalá Ochoa, Noe

    2006-09-01

    Thermal conditions inside the foot were evaluated on a volunteer subject. We have designed and constructed an electronic system which can monitors temperature and humidity of the foot inside the shoe. The data is stored in a battery-powered device for later uploading to a host computer for data analysis. The apparatus potentially can be used to provide feedback to patients who are prone to having skin breakdowns.

  5. Preliminary Development of Real Time Usage-Phase Monitoring System for CNC Machine Tools with a Case Study on CNC Machine VMC 250

    Science.gov (United States)

    Budi Harja, Herman; Prakosa, Tri; Raharno, Sri; Yuwana Martawirya, Yatna; Nurhadi, Indra; Setyo Nogroho, Alamsyah

    2018-03-01

    The production characteristic of job-shop industry at which products have wide variety but small amounts causes every machine tool will be shared to conduct production process with dynamic load. Its dynamic condition operation directly affects machine tools component reliability. Hence, determination of maintenance schedule for every component should be calculated based on actual usage of machine tools component. This paper describes study on development of monitoring system to obtaining information about each CNC machine tool component usage in real time approached by component grouping based on its operation phase. A special device has been developed for monitoring machine tool component usage by utilizing usage phase activity data taken from certain electronics components within CNC machine. The components are adaptor, servo driver and spindle driver, as well as some additional components such as microcontroller and relays. The obtained data are utilized for detecting machine utilization phases such as power on state, machine ready state or spindle running state. Experimental result have shown that the developed CNC machine tool monitoring system is capable of obtaining phase information of machine tool usage as well as its duration and displays the information at the user interface application.

  6. Machine monitoring via current signature analysis techniques

    International Nuclear Information System (INIS)

    Smith, S.F.; Castleberry, K.N.; Nowlin, C.H.

    1992-01-01

    A significant need in the effort to provide increased production quality is to provide improved plant equipment monitoring capabilities. Unfortunately, in today's tight economy, even such monitoring instrumentation must be implemented in a recognizably cost effective manner. By analyzing the electric current drawn by motors, actuator, and other line-powered industrial equipment, significant insights into the operations of the movers, driven equipment, and even the power source can be obtained. The generic term 'current signature analysis' (CSA) has been coined to describe several techniques for extracting useful equipment or process monitoring information from the electrical power feed system. A patented method developed at Oak Ridge National Laboratory is described which recognizes the presence of line-current modulation produced by motors and actuators driving varying loads. The in-situ application of applicable linear demodulation techniques to the analysis of numerous motor-driven systems is also discussed. The use of high-quality amplitude and angle-demodulation circuitry has permitted remote status monitoring of several types of medium and high-power gas compressors in (US DOE facilities) driven by 3-phase induction motors rated from 100 to 3,500 hp, both with and without intervening speed increasers. Flow characteristics of the compressors, including various forms of abnormal behavior such as surging and rotating stall, produce at the output of the specialized detectors specific time and frequency signatures which can be easily identified for monitoring, control, and fault-prevention purposes. The resultant data are similar in form to information obtained via standard vibration-sensing techniques and can be analyzed using essentially identical methods. In addition, other machinery such as refrigeration compressors, brine pumps, vacuum pumps, fans, and electric motors have been characterized

  7. Beam loss monitor system for machine protection

    CERN Document Server

    Dehning, B

    2005-01-01

    Most beam loss monitoring systems are based on the detection of secondary shower particles which depose their energy in the accelerator equipment and finally also in the monitoring detector. To allow an efficient protection of the equipment, the likely loss locations have to be identified by tracking simulations or by using low intensity beams. If superconducting magnets are used for the beam guiding system, not only a damage protection is required but also quench preventions. The quench levels for high field magnets are several orders of magnitude below the damage levels. To keep the operational efficiency high under such circumstances, the calibration factor between the energy deposition in the coils and the energy deposition in the detectors has to be accurately known. To allow a reliable damage protection and quench prevention, the mean time between failures should be high. If in such failsafe system the number of monitors is numerous, the false dump probability has to be kept low to keep a high operation...

  8. A machine protection beam position monitor system

    International Nuclear Information System (INIS)

    Medvedko, E.; Smith, S.; Fisher, A.

    1998-01-01

    Loss of the stored beam in an uncontrolled manner can cause damage to the PEP-II B Factory. We describe here a device which detects large beam position excursions or unexpected beam loss and triggers the beam abort system to extract the stored beam safely. The bad-orbit abort trigger beam position monitor (BOAT BPM) generates a trigger when the beam orbit is far off the center (>20 mm), or rapid beam current loss (dI/dT) is detected. The BOAT BPM averages the input signal over one turn (136 kHz). AM demodulation is used to convert input signals at 476 MHz to baseband voltages. The detected signal goes to a filter section for suppression of the revolution frequency, then on to amplifiers, dividers, and comparators for position and current measurements and triggering. The derived current signal goes to a special filter, designed to perform dI/dT monitoring at fast, medium, and slow current loss rates. The BOAT BPM prototype test results confirm the design concepts. copyright 1998 American Institute of Physics

  9. Detecting System of Nested Hardware Virtual Machine Monitor

    Directory of Open Access Journals (Sweden)

    Artem Vladimirovich Iuzbashev

    2015-03-01

    Full Text Available Method of nested hardware virtual machine monitor detection was proposed in this work. The method is based on HVM timing attack. In case of HVM presence in system, the number of different instruction sequences execution time values will increase. We used this property as indicator in our detection.

  10. ATLAS diamond Beam Condition Monitor

    Energy Technology Data Exchange (ETDEWEB)

    Gorisek, A. [CERN (Switzerland)]. E-mail: andrej.gorisek@cern.ch; Cindro, V. [J. Stefan Institute (Slovenia); Dolenc, I. [J. Stefan Institute (Slovenia); Frais-Koelbl, H. [Fotec (Austria); Griesmayer, E. [Fotec (Austria); Kagan, H. [Ohio State University, OH (United States); Korpar, S. [J. Stefan Institute (Slovenia); Kramberger, G. [J. Stefan Institute (Slovenia); Mandic, I. [J. Stefan Institute (Slovenia); Meyer, M. [CERN (Switzerland); Mikuz, M. [J. Stefan Institute (Slovenia); Pernegger, H. [CERN (Switzerland); Smith, S. [Ohio State University, OH (United States); Trischuk, W. [University of Toronto (Canada); Weilhammer, P. [CERN (Switzerland); Zavrtanik, M. [J. Stefan Institute (Slovenia)

    2007-03-01

    The ATLAS experiment has chosen to use diamond for its Beam Condition Monitor (BCM) given its radiation hardness, low capacitance and short charge collection time. In addition, due to low leakage current diamonds do not require cooling. The ATLAS Beam Condition Monitoring system is based on single beam bunch crossing measurements rather than integrating the accumulated particle flux. Its fast electronics will allow separation of LHC collisions from background events such as beam gas interactions or beam accidents. There will be two stations placed symmetrically about the interaction point along the beam axis at z=+/-183.8cm. Timing of signals from the two stations will provide almost ideal separation of beam-beam interactions and background events. The ATLAS BCM module consists of diamond pad detectors of 1cm{sup 2} area and 500{mu}m thickness coupled to a two-stage RF current amplifier. The production of the final detector modules is almost done. A S/N ratio of 10:1 has been achieved with minimum ionizing particles (MIPs) in the test beam setup at KEK. Results from the test beams and bench measurements are presented.

  11. ATLAS diamond Beam Condition Monitor

    International Nuclear Information System (INIS)

    Gorisek, A.; Cindro, V.; Dolenc, I.; Frais-Koelbl, H.; Griesmayer, E.; Kagan, H.; Korpar, S.; Kramberger, G.; Mandic, I.; Meyer, M.; Mikuz, M.; Pernegger, H.; Smith, S.; Trischuk, W.; Weilhammer, P.; Zavrtanik, M.

    2007-01-01

    The ATLAS experiment has chosen to use diamond for its Beam Condition Monitor (BCM) given its radiation hardness, low capacitance and short charge collection time. In addition, due to low leakage current diamonds do not require cooling. The ATLAS Beam Condition Monitoring system is based on single beam bunch crossing measurements rather than integrating the accumulated particle flux. Its fast electronics will allow separation of LHC collisions from background events such as beam gas interactions or beam accidents. There will be two stations placed symmetrically about the interaction point along the beam axis at z=+/-183.8cm. Timing of signals from the two stations will provide almost ideal separation of beam-beam interactions and background events. The ATLAS BCM module consists of diamond pad detectors of 1cm 2 area and 500μm thickness coupled to a two-stage RF current amplifier. The production of the final detector modules is almost done. A S/N ratio of 10:1 has been achieved with minimum ionizing particles (MIPs) in the test beam setup at KEK. Results from the test beams and bench measurements are presented

  12. ATLAS diamond Beam Condition Monitor

    CERN Document Server

    Gorišek, A; Dolenc, I; Frais-Kölbl, H; Griesmayer, E; Kagan, H; Korpar, S; Kramberger, G; Mandic, I; Meyer, M; Mikuz, M; Pernegger, H; Smith, S; Trischuk, W; Weilhammer, P; Zavrtanik, M

    2007-01-01

    The ATLAS experiment has chosen to use diamond for its Beam Condition Monitor (BCM) given its radiation hardness, low capacitance and short charge collection time. In addition, due to low leakage current diamonds do not require cooling. The ATLAS Beam Condition Monitoring system is based on single beam bunch crossing measurements rather than integrating the accumulated particle flux. Its fast electronics will allow separation of LHC collisions from background events such as beam gas interactions or beam accidents. There will be two stations placed symmetrically about the interaction point along the beam axis at . Timing of signals from the two stations will provide almost ideal separation of beam–beam interactions and background events. The ATLAS BCM module consists of diamond pad detectors of area and thickness coupled to a two-stage RF current amplifier. The production of the final detector modules is almost done. A S/N ratio of 10:1 has been achieved with minimum ionizing particles (MIPs) in the test bea...

  13. A Sensor-less Method for Online Thermal Monitoring of Switched Reluctance Machine

    DEFF Research Database (Denmark)

    Wang, Chao; Liu, Hui; Liu, Xiao

    2015-01-01

    Stator winding is one of the most vulnerable parts in Switched Reluctance Machine (SRM), especially under thermal stresses during frequently changing operation circumstances and susceptible heat dissipation conditions. Thus real-time online thermal monitoring of the stator winding is of great sig...

  14. Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation

    Directory of Open Access Journals (Sweden)

    Tiziana Segreto

    2017-12-01

    Full Text Available Nickel-Titanium (Ni-Ti alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT. The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.

  15. Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation.

    Science.gov (United States)

    Segreto, Tiziana; Caggiano, Alessandra; Karam, Sara; Teti, Roberto

    2017-12-12

    Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.

  16. On the Conditioning of Machine-Learning-Assisted Turbulence Modeling

    Science.gov (United States)

    Wu, Jinlong; Sun, Rui; Wang, Qiqi; Xiao, Heng

    2017-11-01

    Recently, several researchers have demonstrated that machine learning techniques can be used to improve the RANS modeled Reynolds stress by training on available database of high fidelity simulations. However, obtaining improved mean velocity field remains an unsolved challenge, restricting the predictive capability of current machine-learning-assisted turbulence modeling approaches. In this work we define a condition number to evaluate the model conditioning of data-driven turbulence modeling approaches, and propose a stability-oriented machine learning framework to model Reynolds stress. Two canonical flows, the flow in a square duct and the flow over periodic hills, are investigated to demonstrate the predictive capability of the proposed framework. The satisfactory prediction performance of mean velocity field for both flows demonstrates the predictive capability of the proposed framework for machine-learning-assisted turbulence modeling. With showing the capability of improving the prediction of mean flow field, the proposed stability-oriented machine learning framework bridges the gap between the existing machine-learning-assisted turbulence modeling approaches and the demand of predictive capability of turbulence models in real applications.

  17. The ATLAS Beam Conditions Monitor

    International Nuclear Information System (INIS)

    Cindro, V; Dolenc, I; Kramberger, G; Macek, B; Mandic, I; Mikuz', M; Zavrtanik, M; Dobos, D; Gorisek, A; Pernegger, H; Weilhammer, P; Frais-Koelbl, H; Griesmayer, E; Niegl, M; Kagan, H; Tardif, D; Trischuk, W

    2008-01-01

    Beam conditions and the potential detector damage resulting from their anomalies have pushed the LHC experiments to build their own beam monitoring devices. The ATLAS Beam Conditions Monitor (BCM) consists of two stations (forward and backward) of detectors each with four modules. The sensors are required to tolerate doses up to 500 kGy and in excess of 10 15 charged particles per cm 2 over the lifetime of the experiment. Each module includes two diamond sensors read out in parallel. The stations are located symmetrically around the interaction point, positioning the diamond sensors at z = ±184 cm and r = 55 mm (a pseudo- rapidity of about 4.2). Equipped with fast electronics (2 ns rise time) these stations measure time-of-flight and pulse height to distinguish events resulting from lost beam particles from those normally occurring in proton-proton interactions. The BCM also provides a measurement of bunch-by-bunch luminosities in ATLAS by counting in-time and out-of-time collisions. Eleven detector modules have been fully assembled and tested. Tests performed range from characterisation of diamond sensors to full module tests with electron sources and in proton testbeams. Testbeam results from the CERN SPS show a module median-signal to noise of 11:1 for minimum ionising particles incident at a 45-degree angle. The best eight modules were installed on the ATLAS pixel support frame that was inserted into ATLAS in the summer of 2007. This paper describes the full BCM detector system along with simulation studies being used to develop the logic in the back-end FPGA coincidence hardware

  18. The ATLAS Beam Conditions Monitor

    Energy Technology Data Exchange (ETDEWEB)

    Cindro, V; Dolenc, I; Kramberger, G; Macek, B; Mandic, I; Mikuz' , M; Zavrtanik, M [Jozef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana (Slovenia); Dobos, D; Gorisek, A; Pernegger, H; Weilhammer, P [CERN, Geneva (Switzerland); Frais-Koelbl, H; Griesmayer, E; Niegl, M [University of Applied Sciences Wiener Neustadt and Fotec, Wiener Neustadt (Austria); Kagan, H [Ohio State University, Columbus (United States); Tardif, D; Trischuk, W [University of Toronto, Toronto (Canada)], E-mail: william@physics.utoronto.ca

    2008-02-15

    Beam conditions and the potential detector damage resulting from their anomalies have pushed the LHC experiments to build their own beam monitoring devices. The ATLAS Beam Conditions Monitor (BCM) consists of two stations (forward and backward) of detectors each with four modules. The sensors are required to tolerate doses up to 500 kGy and in excess of 10{sup 15} charged particles per cm{sup 2} over the lifetime of the experiment. Each module includes two diamond sensors read out in parallel. The stations are located symmetrically around the interaction point, positioning the diamond sensors at z = {+-}184 cm and r = 55 mm (a pseudo- rapidity of about 4.2). Equipped with fast electronics (2 ns rise time) these stations measure time-of-flight and pulse height to distinguish events resulting from lost beam particles from those normally occurring in proton-proton interactions. The BCM also provides a measurement of bunch-by-bunch luminosities in ATLAS by counting in-time and out-of-time collisions. Eleven detector modules have been fully assembled and tested. Tests performed range from characterisation of diamond sensors to full module tests with electron sources and in proton testbeams. Testbeam results from the CERN SPS show a module median-signal to noise of 11:1 for minimum ionising particles incident at a 45-degree angle. The best eight modules were installed on the ATLAS pixel support frame that was inserted into ATLAS in the summer of 2007. This paper describes the full BCM detector system along with simulation studies being used to develop the logic in the back-end FPGA coincidence hardware.

  19. Maintenance cost avoidance through comprehensive condition monitoring

    International Nuclear Information System (INIS)

    Miller, G.P.; McClymonds, S.L.

    1990-01-01

    Condition monitoring, the measurement and trending of a critical parameter for predictive maintenance, has reached new levels of acceptance and application within the utility and manufacturing industry. Commercially available systems extend well beyond traditional vibration-monitoring systems to include such areas as online wear, crack and leak detection, and stress monitoring. The challenge facing industry is to integrate the information generated from condition monitoring. Current studies indicate that the effectiveness of predictive maintenance depends much more on the program that is established to apply the monitoring techniques than on the monitoring equipment itself. This paper presents a five-phase approach to developing a condition monitoring program

  20. Condition monitoring a key component in the preventive maintenance

    International Nuclear Information System (INIS)

    Isar, C.

    2006-01-01

    The preventive maintenance programs are necessary to ensure that nuclear safety significant equipment will function when it is supposed to. Diesel generator, pumps, motor operated valves and air operated control valves are typically operated every three months. When you drive a car, you depend on lot of sounds, the feel of the steering wheel and gauges to determine if the car is running correctly. Similarly with operating equipment for a power plant - sounds or vibration of the equipment or the gauges and test equipment indicate a problem or degradation, actions are taken to correct the deficiency. Due to safety and economical reason diagnostic and monitoring systems are of growing interest in all complex industrial production. Diagnostic systems are requested to detect, diagnose and localize faulty operating conditions at an early stage in order to prevent severe failures and to enable predictive and condition oriented maintenance. In this context it is a need for using various on-line and off-line condition monitoring and diagnostics, non-destructive inspection techniques and surveillance. The condition monitoring technique used in nuclear power plant Cernavoda are presented in this paper. The selection of components and parameters to be monitored, monitoring and diagnostics techniques used are incorporated into a preventive maintenance program. Modern measurement technique in combination with advanced computerized data processing and acquisition show new ways in the field of machine surveillance. The diagnostic capabilities of predictive maintenance technologies have increased recently year with advances made in sensor technologies. The paper will focus on the following condition monitoring technique: - oil analysis - acoustic leakage monitoring - thermography - valve diagnostics: motor operated valve, air operated valve and check valve - motor current signature - vibration monitoring and rotating machine monitoring and diagnostics For each condition monitoring

  1. The ATLAS beam conditions monitor

    CERN Document Server

    Mikuz, M; Dolenc, I; Kagan, H; Kramberger, G; Frais-Kölbl, H; Gorisek, A; Griesmayer, E; Mandic, I; Pernegger, H; Trischuk, W; Weilhammer, P; Zavrtanik, M

    2006-01-01

    The ATLAS beam conditions monitor is being developed as a stand-alone device allowing to separate LHC collisions from background events induced either on beam gas or by beam accidents, for example scraping at the collimators upstream the spectrometer. This separation can be achieved by timing coincidences between two stations placed symmetric around the interaction point. The 25 ns repetition of collisions poses very stringent requirements on the timing resolution. The optimum separation between collision and background events is just 12.5 ns implying a distance of 3.8 m between the two stations. 3 ns wide pulses are required with 1 ns rise time and baseline restoration in 10 ns. Combined with the radiation field of 10/sup 15/ cm/sup -2/ in 10 years of LHC operation only diamond detectors are considered suitable for this task. pCVD diamond pad detectors of 1 cm/sup 2/ and around 500 mum thickness were assembled with a two-stage RF current amplifier and tested in proton beam at MGH, Boston and SPS pion beam at...

  2. A simple condition monitoring model for a direct monitoring process

    NARCIS (Netherlands)

    Christer, A.H.; Wang, Wenbin

    1995-01-01

    This paper addresses the problem of condition monitoring of a component which has available a measure of condition called wear. Wear accumulates over time and monitoring inspections are performed at chosen times to monitor and measure the cumulative wear. If past measurements of wear are available

  3. The machines maintenance conditions assessment in the plastic industry

    Directory of Open Access Journals (Sweden)

    Stanisław Borkowski

    2015-12-01

    Full Text Available The purpose, methodology, main findings, the originality of the subject area (research, way of using. The main research analysis purpose is a presentation, an assessment and an interpretation of the research findings on the machines maintenance conditions in the plastic industry. The research analysis was carried out with applying Technology ABC method and TPM coefficients calculations connected with Techno pak machine components maintenance. The research was carried out in the chosen manufacturing enterprise of the plastic industry. Research findings interpretation results have been introduced in the analyzed enterprise in the form of the manufacturing processes improvement.

  4. A Machine LearningFramework to Forecast Wave Conditions

    Science.gov (United States)

    Zhang, Y.; James, S. C.; O'Donncha, F.

    2017-12-01

    Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in

  5. A machine learning approach to the accurate prediction of monitor units for a compact proton machine.

    Science.gov (United States)

    Sun, Baozhou; Lam, Dao; Yang, Deshan; Grantham, Kevin; Zhang, Tiezhi; Mutic, Sasa; Zhao, Tianyu

    2018-05-01

    Clinical treatment planning systems for proton therapy currently do not calculate monitor units (MUs) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed to determine the field-specific output factors (OFs) but are often subject to limited machine time, measurement uncertainties and intensive labor. In this study, a machine learning-based approach was developed to predict output (cGy/MU) and derive MUs, incorporating the dependencies on gantry angle and field size for a single-room proton therapy system. The goal of this study was to develop a secondary check tool for OF measurements and eventually eliminate patient-specific OF measurements. The OFs of 1754 fields previously measured in a water phantom with calibrated ionization chambers and electrometers for patient-specific fields with various range and modulation width combinations for 23 options were included in this study. The training data sets for machine learning models in three different methods (Random Forest, XGBoost and Cubist) included 1431 (~81%) OFs. Ten-fold cross-validation was used to prevent "overfitting" and to validate each model. The remaining 323 (~19%) OFs were used to test the trained models. The difference between the measured and predicted values from machine learning models was analyzed. Model prediction accuracy was also compared with that of the semi-empirical model developed by Kooy (Phys. Med. Biol. 50, 2005). Additionally, gantry angle dependence of OFs was measured for three groups of options categorized on the selection of the second scatters. Field size dependence of OFs was investigated for the measurements with and without patient-specific apertures. All three machine learning methods showed higher accuracy than the semi-empirical model which shows considerably large discrepancy of up to 7.7% for the treatment fields with full range and full modulation width. The Cubist-based solution

  6. Application of Machine Learning to Rotorcraft Health Monitoring

    Science.gov (United States)

    Cody, Tyler; Dempsey, Paula J.

    2017-01-01

    Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

  7. Condition Assessment and End-of-Life Prediction System for Electric Machines and Their Loads

    Science.gov (United States)

    Parlos, Alexander G.; Toliyat, Hamid A.

    2005-01-01

    An end-of-life prediction system developed for electric machines and their loads could be used in integrated vehicle health monitoring at NASA and in other government agencies. This system will provide on-line, real-time condition assessment and end-of-life prediction of electric machines (e.g., motors, generators) and/or their loads of mechanically coupled machinery (e.g., pumps, fans, compressors, turbines, conveyor belts, magnetic levitation trains, and others). In long-duration space flight, the ability to predict the lifetime of machinery could spell the difference between mission success or failure. Therefore, the system described here may be of inestimable value to the U.S. space program. The system will provide continuous monitoring for on-line condition assessment and end-of-life prediction as opposed to the current off-line diagnoses.

  8. Unsupervised process monitoring and fault diagnosis with machine learning methods

    CERN Document Server

    Aldrich, Chris

    2013-01-01

    This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data

  9. On-line condition monitoring of nuclear systems via symbolic time series analysis

    International Nuclear Information System (INIS)

    Rajagopalan, V.; Ray, A.; Garcia, H. E.

    2006-01-01

    This paper provides a symbolic time series analysis approach to fault diagnostics and condition monitoring. The proposed technique is built upon concepts from wavelet theory, symbolic dynamics and pattern recognition. Various aspects of the methodology such as wavelet selection, choice of alphabet and determination of depth of D-Markov Machine are explained in the paper. The technique is validated with experiments performed in a Machine Condition Monitoring (MCM) test bed at the Idaho National Laboratory. (authors)

  10. MONITORING DIAGNOSTIC INDICATORS DURING OPERATION OF A PRINT MACHIN

    Directory of Open Access Journals (Sweden)

    Jozef Dobránsky

    2015-11-01

    Full Text Available This article deals with monitoring diagnostic indicators during the operation of a machine used for production of packing materials with a print. It analyses low-frequency vibrations measured in individual spherical roller bearings in eight print positions. The rollers in these positions have a different pressure based on positioning these rollers in relation to the central roller. As a result, the article also deals with a correlation of pressure and level of measured low-frequency vibrations. The speed of the print machine (the speed of a line in meters per minute is a very important variable during its operation, this is why it is important to verify the values of vibrations in various speeds of the line, what can lead to revelation of one or more resonance areas. Moreover, it examines vibrations of the central roller drive and measurement of backlash of transmission cogs of this drive. Based on performed analyses recommendations for an operator of the machine have been conceived.

  11. Machine Induced Experimental Background Conditions in the LHC

    CERN Document Server

    Levinsen, Yngve Inntjore; Stapnes, Steinar

    2012-09-19

    The Large Hadron Collider set a new energy record for particle accelerators in late 2009, breaking the previous record held by Tevatron of 2 TeV collision energy. The LHC today operates at a collision energy of 7 TeV. With higher beam energy and intensity, measures have to be taken to ensure optimal experimental conditions and safety of the machine and detectors. Machine induced experimental background can severely reduce the quality of experimental triggers and track reconstruction. In a worst case, the radiation levels can be damaging for some of the subdetectors. The LHC is a particular challenge in this regard due to the vastly different operating conditions of the different experiments. The nominal luminosity varies by four orders of magnitude. The unprecedented stored beam energy and the amount of superconducting elements can make it challenging to protect the accelerator itself as well. In this work we have simulated and measured the machine induced background originating from various sources: the beam...

  12. Condition monitoring of rotormachinery in nuclear power plants

    International Nuclear Information System (INIS)

    Suedmersen, U.; Runkel, J.; Vortriede, A.; Reimche, W.; Stegemann, D.

    1996-01-01

    Due to safety and economical reasons diagnostic and monitoring systems are of growing interest in nuclear power plants and other complex industrial productions. Key components of NPP's are rotating machineries of the primary and secondary loops like PWR main coolant pumps, BWR recirculation pumps, turbines, fresh water pumps and feed water pumps. Diagnostic systems are requested which detect, diagnose and localize faulty operation conditions at an early stage in order to prevent severe failures and to enable predictive and condition oriented maintenance. The knowledge of characteristical machine signatures and their time dependent behaviour are the basis of efficient condition monitoring of rotating machines. The performance of reference measurements are of importance for fault detection during operation by trend settings. The comparison with thresholds given by norms and standards is only a small section of available possibilities. Therefore, for each machinery own thresholds should be determined using statistical time values, spectra comparison, cepstrum analysis and correlation analysis for source localization corresponding to certain machine operation conditions. (author). 14 refs, 15 figs

  13. Condition monitoring of rotormachinery in nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    Suedmersen, U; Runkel, J; Vortriede, A; Reimche, W; Stegemann, D [University of Hannover, Hannover (Germany). Inst. of Nuclear Engineering and Nondestructive Testing

    1997-12-31

    Due to safety and economical reasons diagnostic and monitoring systems are of growing interest in nuclear power plants and other complex industrial productions. Key components of NPP`s are rotating machineries of the primary and secondary loops like PWR main coolant pumps, BWR recirculation pumps, turbines, fresh water pumps and feed water pumps. Diagnostic systems are requested which detect, diagnose and localize faulty operation conditions at an early stage in order to prevent severe failures and to enable predictive and condition oriented maintenance. The knowledge of characteristical machine signatures and their time dependent behaviour are the basis of efficient condition monitoring of rotating machines. The performance of reference measurements are of importance for fault detection during operation by trend settings. The comparison with thresholds given by norms and standards is only a small section of available possibilities. Therefore, for each machinery own thresholds should be determined using statistical time values, spectra comparison, cepstrum analysis and correlation analysis for source localization corresponding to certain machine operation conditions. (author). 14 refs, 15 figs.

  14. An on-line monitoring system for a micro electrical discharge machining (micro-EDM) process

    International Nuclear Information System (INIS)

    Liao, Y S; Chang, T Y; Chuang, T J

    2008-01-01

    A pulse-type discriminating system to monitor the process of micro electrical discharge machining (micro-EDM) is developed and implemented. The specific features are extracted and the pulses from a RC-type power source are classified into normal, effective arc, transient short circuit and complex types. An approach to discriminate the pulse type according to three durations measured at three pre-determined voltage levels of a pulse is proposed. The developed system is verified by using simulated signals. Discrimination of the pulse trains in actual machining processes shows that the pulses are mainly the normal type for micro wire-EDM and micro-EDM milling. The pulse-type distribution varies during the micro-EDM drilling process. The percentage of complex-type pulse increases monotonically with the drilling depth. It starts to drop when the gap condition is seriously deteriorated. Accordingly, an on-line monitoring strategy for the micro-EDM drilling process is proposed

  15. Wireless Monitoring of Induction Machine Rotor Physical Variables

    Directory of Open Access Journals (Sweden)

    Jefferson Doolan Fernandes

    2017-11-01

    Full Text Available With the widespread use of electric machines, there is a growing need to extract information from the machines to improve their control systems and maintenance management. The present work shows the development of an embedded system to perform the monitoring of the rotor physical variables of a squirrel cage induction motor. The system is comprised of: a circuit to acquire desirable rotor variable(s and value(s that send it to the computer; a rectifier and power storage circuit that converts an alternating current in a continuous current but also stores energy for a certain amount of time to wait for the motor’s shutdown; and a magnetic generator that harvests energy from the rotating field to power the circuits mentioned above. The embedded system is set on the rotor of a 5 HP squirrel cage induction motor, making it difficult to power the system because it is rotating. This problem can be solved with the construction of a magnetic generator device to avoid the need of using batteries or collector rings and will send data to the computer using a wireless NRF24L01 module. For the proposed system, initial validation tests were made using a temperature sensor (DS18b20, as this variable is known as the most important when identifying the need for maintenance and control systems. Few tests have shown promising results that, with further improvements, can prove the feasibility of using sensors in the rotor.

  16. Wireless Monitoring of Induction Machine Rotor Physical Variables.

    Science.gov (United States)

    Doolan Fernandes, Jefferson; Carvalho Souza, Francisco Elvis; Cipriano Maniçoba, Glauco George; Salazar, Andrés Ortiz; de Paiva, José Alvaro

    2017-11-18

    With the widespread use of electric machines, there is a growing need to extract information from the machines to improve their control systems and maintenance management. The present work shows the development of an embedded system to perform the monitoring of the rotor physical variables of a squirrel cage induction motor. The system is comprised of: a circuit to acquire desirable rotor variable(s) and value(s) that send it to the computer; a rectifier and power storage circuit that converts an alternating current in a continuous current but also stores energy for a certain amount of time to wait for the motor's shutdown; and a magnetic generator that harvests energy from the rotating field to power the circuits mentioned above. The embedded system is set on the rotor of a 5 HP squirrel cage induction motor, making it difficult to power the system because it is rotating. This problem can be solved with the construction of a magnetic generator device to avoid the need of using batteries or collector rings and will send data to the computer using a wireless NRF24L01 module. For the proposed system, initial validation tests were made using a temperature sensor (DS18b20), as this variable is known as the most important when identifying the need for maintenance and control systems. Few tests have shown promising results that, with further improvements, can prove the feasibility of using sensors in the rotor.

  17. Development of Wireless Smart Sensor for Structure and Machine Monitoring

    Directory of Open Access Journals (Sweden)

    Ismoyo Haryanto

    2013-07-01

    Full Text Available Vibration based condition monitoring is a method used for determining the condition of a system. The condition of mechanical or a structural system can be determined from the vibration. The vibration that is produced by the system indicates the condition of a system and possibly used to calculate the lifetime of a system or even used to take early action before fatal failure occurred. This paper explains how the wireless smart sensor can be used to identify the health condition of a system by monitoring the vibration parameters. The wireless smart sensor would continously  senses the vibration parameters of the system in a real-time systems and then data will be transmitted wirelessly  to a base station which is a host PC used for digital signal processing, from there the vibration will be plotted as a graph which used to analyzed the condition of the system. Finally, several tested performed to the real system to verify the accuracy of a smart sensor and the method of condition based monitoring.

  18. Air quality monitoring using mobile microscopy and machine learning

    KAUST Repository

    Wu, Yi-Chen; Shiledar, Ashutosh; Li, Yi-Cheng; Wong, Jeffrey; Feng, Steve; Chen, Xuan; Chen, Christine; Jin, Kevin; Janamian, Saba; Yang, Zhe; Ballard, Zachary Scott; Gö rö cs, Zoltá n; Feizi, Alborz; Ozcan, Aydogan

    2017-01-01

    Rapid, accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. In fact, particles in air with a diameter of ≤2.5 μm have been classified as carcinogenic by the World Health Organization. Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-learning. This platform, termed c-Air, is also integrated with a smartphone application for device control and display of results. This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air. It provides statistics of the particle size and density distribution with a sizing accuracy of ~93%. We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times, and compared our results to those of an Environmental Protection Agency–approved device based on beta-attenuation monitoring, which showed strong correlation to c-Air measurements. Furthermore, we used c-Air to map the air quality around Los Angeles International Airport (LAX) over 24 h to confirm that the impact of LAX on increased PM concentration was present even at >7 km away from the airport, especially along the direction of landing flights. With its machine-learning-based computational microscopy interface, c-Air can be adaptively tailored to detect specific particles in air, for example, various types of pollen and mold and provide a cost-effective mobile solution for highly accurate and distributed sensing of air quality.

  19. Air quality monitoring using mobile microscopy and machine learning

    KAUST Repository

    Wu, Yi-Chen

    2017-09-08

    Rapid, accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. In fact, particles in air with a diameter of ≤2.5 μm have been classified as carcinogenic by the World Health Organization. Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-learning. This platform, termed c-Air, is also integrated with a smartphone application for device control and display of results. This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air. It provides statistics of the particle size and density distribution with a sizing accuracy of ~93%. We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times, and compared our results to those of an Environmental Protection Agency–approved device based on beta-attenuation monitoring, which showed strong correlation to c-Air measurements. Furthermore, we used c-Air to map the air quality around Los Angeles International Airport (LAX) over 24 h to confirm that the impact of LAX on increased PM concentration was present even at >7 km away from the airport, especially along the direction of landing flights. With its machine-learning-based computational microscopy interface, c-Air can be adaptively tailored to detect specific particles in air, for example, various types of pollen and mold and provide a cost-effective mobile solution for highly accurate and distributed sensing of air quality.

  20. Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM).

    Science.gov (United States)

    Nadiri, Ata Allah; Gharekhani, Maryam; Khatibi, Rahman; Sadeghfam, Sina; Moghaddam, Asghar Asghari

    2017-01-01

    This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. Decision Support System for Condition Monitoring Technologies

    NARCIS (Netherlands)

    Mouatamir, Abderrahim

    2018-01-01

    The technological feasibility of a condition-based maintenance (CBM) policy is intrinsically related to the suitable selection of condition monitoring (CM) technologies such as vibration- and oil analysis or other non-destructive testing (NDT) techniques such as radiographic- and magnetic particle

  2. Conditional High-Order Boltzmann Machines for Supervised Relation Learning.

    Science.gov (United States)

    Huang, Yan; Wang, Wei; Wang, Liang; Tan, Tieniu

    2017-09-01

    Relation learning is a fundamental problem in many vision tasks. Recently, high-order Boltzmann machine and its variants have shown their great potentials in learning various types of data relation in a range of tasks. But most of these models are learned in an unsupervised way, i.e., without using relation class labels, which are not very discriminative for some challenging tasks, e.g., face verification. In this paper, with the goal to perform supervised relation learning, we introduce relation class labels into conventional high-order multiplicative interactions with pairwise input samples, and propose a conditional high-order Boltzmann Machine (CHBM), which can learn to classify the data relation in a binary classification way. To be able to deal with more complex data relation, we develop two improved variants of CHBM: 1) latent CHBM, which jointly performs relation feature learning and classification, by using a set of latent variables to block the pathway from pairwise input samples to output relation labels and 2) gated CHBM, which untangles factors of variation in data relation, by exploiting a set of latent variables to multiplicatively gate the classification of CHBM. To reduce the large number of model parameters generated by the multiplicative interactions, we approximately factorize high-order parameter tensors into multiple matrices. Then, we develop efficient supervised learning algorithms, by first pretraining the models using joint likelihood to provide good parameter initialization, and then finetuning them using conditional likelihood to enhance the discriminant ability. We apply the proposed models to a series of tasks including invariant recognition, face verification, and action similarity labeling. Experimental results demonstrate that by exploiting supervised relation labels, our models can greatly improve the performance.

  3. Application for vibration monitoring of aspheric surface machining based on wireless sensor networks

    Science.gov (United States)

    Han, Chun Guang; Guo, Yin Biao; Jiang, Chen

    2010-05-01

    Any kinds of tiny vibration of machine tool parts will have a great influence on surface quality of the workpiece at ultra-precise machining process of aspheric surface. At present the major way for decreasing influence of vibration is machining compensation technology. Therefore it is important for machining compensation control to acquire and transmit these vibration signals effectively. This paper presents a vibration monitoring system of aspheric surface machining machine tool based on wireless sensor networks (WSN). Some key issues of wireless sensor networks for vibration monitoring system of aspheric surface machining are discussed. The reliability of data transmission, network communication protocol and synchronization mechanism of wireless sensor networks are studied for the vibration monitoring system. The proposed system achieves multi-sensors vibration monitoring involving the grinding wheel, the workpiece and the workbench spindle. The wireless transmission of vibration signals is achieved by the combination with vibration sensor nodes and wireless network. In this paper, these vibration sensor nodes are developed. An experimental platform is structured which employs wireless sensor networks to the vibration monitoring system in order to test acquisition and wireless transmission of vibration signal. The test results show that the proposed system can achieve vibration data transmission effectively and reliability and meet the monitoring requirements of aspheric surface machining machine tool.

  4. [Extension of cardiac monitoring function by used of ordinary ECG machine].

    Science.gov (United States)

    Chen, Zhencheng; Jiang, Yong; Ni, Lili; Wang, Hongyan

    2002-06-01

    This paper deals with a portable monitor system on liquid crystal display (LCD) based on this available ordinary ECG machine, which is low power and suitable for China's specific condition. Apart from developing the overall scheme of the system, this paper also has completed the design of the hardware and the software. The 80c196 single chip microcomputer is taken as the central microprocessor and real time electrocardiac single is data treated and analyzed in the system. With the performance of ordinary monitor, this machine also possesses the following functions: five types of arrhythmia analysis, alarm, freeze, and record of automatic pappering, convenient in carrying, with alternate-current (AC) or direct-current (DC) powered. The hardware circuit is simplified and the software structure is optimized in this paper. Multiple low power designs and LCD unit design are adopted and completed in it. Popular in usage, low in cost price, the portable monitor system will have a valuable influence on China's monitor system field.

  5. Using virtual machine monitors to overcome the challenges of monitoring and managing virtualized cloud infrastructures

    Science.gov (United States)

    Bamiah, Mervat Adib; Brohi, Sarfraz Nawaz; Chuprat, Suriayati

    2012-01-01

    Virtualization is one of the hottest research topics nowadays. Several academic researchers and developers from IT industry are designing approaches for solving security and manageability issues of Virtual Machines (VMs) residing on virtualized cloud infrastructures. Moving the application from a physical to a virtual platform increases the efficiency, flexibility and reduces management cost as well as effort. Cloud computing is adopting the paradigm of virtualization, using this technique, memory, CPU and computational power is provided to clients' VMs by utilizing the underlying physical hardware. Beside these advantages there are few challenges faced by adopting virtualization such as management of VMs and network traffic, unexpected additional cost and resource allocation. Virtual Machine Monitor (VMM) or hypervisor is the tool used by cloud providers to manage the VMs on cloud. There are several heterogeneous hypervisors provided by various vendors that include VMware, Hyper-V, Xen and Kernel Virtual Machine (KVM). Considering the challenge of VM management, this paper describes several techniques to monitor and manage virtualized cloud infrastructures.

  6. A hybrid prognostic model for multistep ahead prediction of machine condition

    Science.gov (United States)

    Roulias, D.; Loutas, T. H.; Kostopoulos, V.

    2012-05-01

    Prognostics are the future trend in condition based maintenance. In the current framework a data driven prognostic model is developed. The typical procedure of developing such a model comprises a) the selection of features which correlate well with the gradual degradation of the machine and b) the training of a mathematical tool. In this work the data are taken from a laboratory scale single stage gearbox under multi-sensor monitoring. Tests monitoring the condition of the gear pair from healthy state until total brake down following several days of continuous operation were conducted. After basic pre-processing of the derived data, an indicator that correlated well with the gearbox condition was obtained. Consecutively the time series is split in few distinguishable time regions via an intelligent data clustering scheme. Each operating region is modelled with a feed-forward artificial neural network (FFANN) scheme. The performance of the proposed model is tested by applying the system to predict the machine degradation level on unseen data. The results show the plausibility and effectiveness of the model in following the trend of the timeseries even in the case that a sudden change occurs. Moreover the model shows ability to generalise for application in similar mechanical assets.

  7. A design condition for incorporating human judgement into monitoring systems

    International Nuclear Information System (INIS)

    Tanaka, K.; Klir, G.J.

    1999-01-01

    In safety monitoring, there exists an uncertainty situation in which the sensor cannot detect whether or not the monitored object is in danger. For the uncertainty zone identified by a non-homogeneous safety monitoring system that utilizes two types of sensors with different thresholds, operators or experts are expected to judge whether the real state is safe or dangerous on the basis of additional information from a detailed inspection or other related sensors output. However, the activities for inspection performed by relevant humans may require additional cost and introduce inspection errors. The present article proposes two types of an automatic monitoring system not involving any human inspection or a human-machine (H-M) cooperative monitoring system with inspection. In order to compare the systems, an approach based on the Dempster-Shafer theory is proposed as uncertainty analysis by this theory (it is simpler than by the traditional Bayesian approach). By comparing their expected losses as a result of failed dangerous failures or failed safe failures as well as the inspection errors, the condition is determined under which H-M cooperative systems incorporating human judgements are more effective than automatic monitoring systems

  8. An integrated condition-monitoring method for a milling process using reduced decomposition features

    International Nuclear Information System (INIS)

    Liu, Jie; Wu, Bo; Hu, Youmin; Wang, Yan

    2017-01-01

    Complex and non-stationary cutting chatter affects productivity and quality in the milling process. Developing an effective condition-monitoring approach is critical to accurately identify cutting chatter. In this paper, an integrated condition-monitoring method is proposed, where reduced features are used to efficiently recognize and classify machine states in the milling process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition, and Shannon power spectral entropy is calculated to extract features from the decomposed signals. Principal component analysis is adopted to reduce feature size and computational cost. With the extracted feature information, the probabilistic neural network model is used to recognize and classify the machine states, including stable, transition, and chatter states. Experimental studies are conducted, and results show that the proposed method can effectively detect cutting chatter during different milling operation conditions. This monitoring method is also efficient enough to satisfy fast machine state recognition and classification. (paper)

  9. Operational performance of generator condition monitors

    International Nuclear Information System (INIS)

    Braun, J.M.; Brown, G.

    1990-01-01

    This paper reports on the generator condition monitor (GCM) developed in an attempt to detect overheating inside large turbine generators. As part of a broader study on rotating machinery diagnostics, generator condition monitors were evaluated under field conditions in a 550 MW turbogenerator. Small 100 W resistors coated with insulating paints and varnishes were mounted inside the generator to simulate insulation overheating. The GCM responded very rapidly to an overheating event, typically within two minutes, even for hot spots as small s 10 cm 2 . Similarly the aerosols produced on overheating were found extremely short lived, decaying within two to three minutes after overheating was discontinued. Use of heated ion chambers was found to desensitize the GCM regardless of the nature of the overheated insulation and in some cases would altogether prevent the GCM from reaching the 50% pre-set alarm level commonly used on GCMs

  10. Application of Electro Chemical Machining for materials used in extreme conditions

    Science.gov (United States)

    Pandilov, Z.

    2018-03-01

    Electro-Chemical Machining (ECM) is the generic term for a variety of electrochemical processes. ECM is used to machine work pieces from metal and metal alloys irrespective of their hardness, strength or thermal properties, through the anodic dissolution, in aerospace, automotive, construction, medical equipment, micro-systems and power supply industries. The Electro Chemical Machining is extremely suitable for machining of materials used in extreme conditions. General overview of the Electro-Chemical Machining and its application for different materials used in extreme conditions is presented.

  11. Integrating structural health and condition monitoring

    DEFF Research Database (Denmark)

    May, Allan; Thöns, Sebastian; McMillan, David

    2015-01-01

    window’ allowing for the possible detection of faults up to 6 months in advance. The SHM system model uses a reduction in the probability of failure factor to account for lower modelling uncertainties. A case study is produced that shows a reduction in operating costs and also a reduction in risk......There is a large financial incentive to minimise operations and maintenance (O&M) costs for offshore wind power by optimising the maintenance plan. The integration of condition monitoring (CM) and structural health monitoring (SHM) may help realise this. There is limited work on the integration...

  12. Electrical condition monitoring method for polymers

    Science.gov (United States)

    Watkins, Jr. Kenneth S.; Morris, Shelby J.; Masakowski, Daniel D.; Wong, Ching Ping; Luo, Shijian

    2010-02-16

    An electrical condition monitoring method utilizes measurement of electrical resistivity of a conductive composite degradation sensor to monitor environmentally induced degradation of a polymeric product such as insulated wire and cable. The degradation sensor comprises a polymeric matrix and conductive filler. The polymeric matrix may be a polymer used in the product, or it may be a polymer with degradation properties similar to that of a polymer used in the product. The method comprises a means for communicating the resistivity to a measuring instrument and a means to correlate resistivity of the degradation sensor with environmentally induced degradation of the product.

  13. Real Time Monitoring System of Pollution Waste on Musi River Using Support Vector Machine (SVM) Method

    Science.gov (United States)

    Fachrurrozi, Muhammad; Saparudin; Erwin

    2017-04-01

    Real-time Monitoring and early detection system which measures the quality standard of waste in Musi River, Palembang, Indonesia is a system for determining air and water pollution level. This system was designed in order to create an integrated monitoring system and provide real time information that can be read. It is designed to measure acidity and water turbidity polluted by industrial waste, as well as to show and provide conditional data integrated in one system. This system consists of inputting and processing the data, and giving output based on processed data. Turbidity, substances, and pH sensor is used as a detector that produce analog electrical direct current voltage (DC). Early detection system works by determining the value of the ammonia threshold, acidity, and turbidity level of water in Musi River. The results is then presented based on the level group pollution by the Support Vector Machine classification method.

  14. The heater system monitoring and control of the fuelling machines test rig fluid

    International Nuclear Information System (INIS)

    Iorga, C.; Iorga, H.

    2016-01-01

    The thermo-mechanical hot loop (HL) of the testing rig for the fuelling machines (F/Ms) represents a set of facilities and equipment that perform the pressure, temperature and flow thermo-hydraulic parameters similar to those from the fuel channel for CANDU 600 reactor types. The 2.1 MW electric heater (EH), part of the HL, working under the conditions of a pressure vessel (110 bars) and provides an average temperature of 300°C of the working agent. The monitoring equipment implemented aims to simultaneously control the temperature for each of the 12 modules that compose the EH, without influencing the work logic of the display/recording and protecting existing equipment. This paper presents the structure of the monitoring equipment and its performance obtained after performing the functional tests. (authors)

  15. Equipment monitoring and diagnosis of their mechanical condition

    International Nuclear Information System (INIS)

    Morel, J.; Monnier, B.

    1994-01-01

    The main objectives of reactor monitoring are reviewed and the three monitoring types are described: reception controls and alarms, periodical controls, and specific monitoring. The monitoring process is then presented: information acquisition, dysfunction detection and diagnosis, risk analysis and maintenance action determination. Diagnosis assistance is now automated with expert systems such as DIVA (shaft vibration diagnosis) and DIAPO (primary pump diagnosis). Several application examples at EDF are described: SEXTEN reactor containment tightness monitoring, large and small-size turbo-machine monitoring, reactor inner structure monitoring, loose part detection in the primary circuit. All these informations will be centralized in a general monitoring and diagnosis assistance station. 3 fig

  16. Advanced condition monitoring program for turbine system

    International Nuclear Information System (INIS)

    Ono, Shigetoshi

    2015-01-01

    It is important for utilities to achieve a stable operation in nuclear power plants. To achieve it, plant anomalies that affect a stable operation must be found out and eliminated. Therefore, the advanced condition monitoring program was developed. In this program, a sophisticated heat balance model based on the actual plant data is adopted to identify plant anomalies at an incipient stage and the symptoms of plant anomalies are found by heat balance changes from the model calculation. The model calculation results have shown precise prediction for actual plant parameters. Moreover, this program has the diagnostic engine that helps operators derive the cause of plant anomalies. By using this monitoring program, the component reliability in the turbine system can be periodically monitored and assessed, and as a result the stable operation of nuclear power plants can be achieved. (author)

  17. Analysis on machine tool systems using spindle vibration monitoring for automatic tool changer

    Directory of Open Access Journals (Sweden)

    Shang-Liang Chen

    2015-12-01

    Full Text Available Recently, the intelligent systems of technology have become one of the major items in the development of machine tools. One crucial technology is the machinery status monitoring function, which is required for abnormal warnings and the improvement of cutting efficiency. During processing, the mobility act of the spindle unit determines the most frequent and important part such as automatic tool changer. The vibration detection system includes the development of hardware and software, such as vibration meter, signal acquisition card, data processing platform, and machine control program. Meanwhile, based on the difference between the mechanical configuration and the desired characteristics, it is difficult for a vibration detection system to directly choose the commercially available kits. For this reason, it was also selected as an item for self-development research, along with the exploration of a significant parametric study that is sufficient to represent the machine characteristics and states. However, we also launched the development of functional parts of the system simultaneously. Finally, we entered the conditions and the parameters generated from both the states and the characteristics into the developed system to verify its feasibility.

  18. Condition Monitoring of the SSE Generation Fleet

    Science.gov (United States)

    Twiddle, J.; Muthuraman, S.; Connolly, N.

    2012-05-01

    SSE (previously known as Scottish and Southern Energy) operates a diverse portfolio of generation plant, including coal, gas and renewable plant with a total generation capacity of 11,375MW (Sept 2011). In recent years a group of specialists dedicated to providing condition monitoring services has been established at the Equipment Performance Centre (EPC) based at Knottingley, West Yorkshire. We aim to illustrate the role of the EPC and the methods used for monitoring the generation fleet with the objective of maintaining asset integrity, reducing risk of plant failure and unplanned outages and describe the challenges which have been overcome in establishing the EPC. This paper describes methods including vibration and process data analysis, model-based techniques and on-site testing used for monitoring of generation plant, including gas turbines, steam turbines, generators and steam raising plant. These condition monitoring processes utilise available data, adding value to the business, by bringing services in-house and capturing knowledge of plant operation for the benefit of the whole fleet.

  19. Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

    OpenAIRE

    Rui Zhao; Ruqiang Yan; Jinjiang Wang; Kezhi Mao

    2017-01-01

    In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression mode...

  20. Condition Monitoring Using Computational Intelligence Methods Applications in Mechanical and Electrical Systems

    CERN Document Server

    Marwala, Tshilidzi

    2012-01-01

    Condition monitoring uses the observed operating characteristics of a machine or structure to diagnose trends in the signal being monitored and to predict the need for maintenance before a breakdown occurs. This reduces the risk, inherent in a fixed maintenance schedule, of performing maintenance needlessly early or of having a machine fail before maintenance is due either of which can be expensive with the latter also posing a risk of serious accident especially in systems like aeroengines in which a catastrophic failure would put lives at risk. The technique also measures responses from the whole of the system under observation so it can detect the effects of faults which might be hidden deep within a system, hidden from traditional methods of inspection. Condition Monitoring Using Computational Intelligence Methods promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, m...

  1. 3rd International Conference on Condition Monitoring of Machinery in Non-Stationary Operations

    CERN Document Server

    Rubini, Riccardo; D'Elia, Gianluca; Cocconcelli, Marco; Chaari, Fakher; Zimroz, Radoslaw; Bartelmus, Walter; Haddar, Mohamed

    2014-01-01

    This book presents the processings of the third edition of the Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO13) which was held in Ferrara, Italy. This yearly event merges an international community of researchers who met – in 2011 in Wroclaw (Poland) and in 2012 in Hammamet (Tunisia) – to discuss issues of diagnostics of rotating machines operating in complex motion and/or load conditions. The growing interest of the industrial world on the topics covered by the CMMNO13 involves the fields of packaging, automotive, agricultural, mining, processing and wind machines in addition to that of the systems for data acquisition.The participation of speakers and visitors from industry makes the event an opportunity for immediate assessment of the potential applications of advanced methodologies for the signal analysis. Signals acquired from machines often contain contributions from several different components as well as noise. Therefore, the major challenge of condition monitoring is to po...

  2. On the Impossibility of Detecting Virtual Machine Monitors

    Science.gov (United States)

    Gueron, Shay; Seifert, Jean-Pierre

    Virtualization based upon Virtual Machines is a central building block of Trusted Computing, and it is believed to offer isolation and confinement of privileged instructions among other security benefits. However, it is not necessarily bullet-proof — some recent publications have shown that Virtual Machine technology could potentially allow the installation of undetectable malware root kits. As a result, it was suggested that such virtualization attacks could be mitigated by checking if a threatened system runs in a virtualized or in a native environment. This naturally raises the following problem: Can a program determine whether it is running in a virtualized environment, or in a native machine environment? We prove here that, under a classical VM model, this problem is not decidable. Further, although our result seems to be quite theoretic, we also show that it has practical implications on related virtualization problems.

  3. Condition Monitoring and Management from Acoustic Emissions

    DEFF Research Database (Denmark)

    Pontoppidan, Niels Henrik Bohl

    2005-01-01

    In the following, I will use technical terms without explanation as it gives the freedom to describe the project in a shorter form for those who already know. The thesis is about condition monitoring of large diesel engines from acoustic emission signals. The experiments have been focused...... is the analysis of the angular position changes of the engine related events such as fuel injection and valve openings, caused by operational load changes. With inspiration from speech recognition and voice effects the angular timing changes have been inverted with the event alignment framework. With the event...

  4. Condition Monitoring of Cables Task 3 Report: Condition Monitoring Techniques for Electric Cables

    Energy Technology Data Exchange (ETDEWEB)

    Villaran, M.; Lofaro, R.; na

    2009-11-30

    For more than 20 years the NRC has sponsored research studying electric cable aging degradation, condition monitoring, and environmental qualification testing practices for electric cables used in nuclear power plants. This report summarizes several of the most effective and commonly used condition monitoring techniques available to detect damage and measure the extent of degradation in electric cable insulation. The technical basis for each technique is summarized, along with its application, trendability of test data, ease of performing the technique, advantages and limitations, and the usefulness of the test results to characterize and assess the condition of electric cables.

  5. System for monitoring microclimate conditions in greenhouse

    Directory of Open Access Journals (Sweden)

    Marković Dušan B.

    2014-01-01

    Full Text Available Monitoring microclimate parameters in different kind of environments has significant contribution to many areas of human activity and production processes. One of them is vegetable production in greenhouses where measurement of its microclimate parameters may influence the decision on taking appropriate action and protect crops. It is also important to preserve optimal condition in greenhouses to facilitate the process of transpiration, plant mineral nutrition and prevent of a variety physiological damage caused by a deficit of some specific nutrients. Systems for monitoring have wide application in the last years thanks to development of modern computer technology. In this paper model of the monitoring system based on smart transducer concept was introduced. Within the system components are based on MSP430 ultra low power micro controllers. They are using wireless communication to exchange data within the system that was structured according to smart transducer concept. User applications from the network could access to system interface using HTTP protocol where web server could be running on the computer or it could be an embedded web server running on micro controller based device.

  6. Effect of operational conditions of electroerosion machining on the surface microgeometry parameters of steels and alloys

    International Nuclear Information System (INIS)

    Foteev, N.K.

    1976-01-01

    Studies the influence of pulse duration and a series of operating conditions of a ShGI-40-440 spark-machining generator on changes in the basic surface microgeometry characteristics of components of stainless steel 1Kh18N10T, steel St 45 and hard alloy T14K8. The microgeometry characteristics of spark-machined surfaces differ significantly from the corresponding characteristics of surfaces machined by cutting and vibro-rolling

  7. An integrated system for pipeline condition monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Strong, Andrew P.; Lees, Gareth; Hartog, Arthur; Twohig, Richard; Kader, Kamal; Hilton, Graeme; Mullens, Stephen; Khlybov, Artem [Schlumberger, Southampton (United Kingdom); Sanderson, Norman [BP Exploration, Sunbury (United Kingdom)

    2009-07-01

    In this paper we present the unique and innovative 'Integriti' pipeline and flow line integrity monitoring system developed by Schlumberger in collaboration with BP. The system uses optical fiber distributed sensors to provide simultaneous distributed measurements of temperature, strain and vibration for the detection, monitoring, and location of events including: Third Party Interference (TPI), including multiple simultaneous disturbances; geo-hazards and landslides; gas and oil leaks; permafrost protection. The Integriti technology also provides a unique means for tracking the progress of cleaning and instrumented pigs using existing optical telecom and data communications cables buried close to pipelines. The Integriti solution provides a unique and proactive approach to pipeline integrity management. It performs analysis of a combination of measurands to provide the pipeline operator with an event recognition and location capability, in effect providing a hazard warning system, and offering the operator the potential to take early action to prevent loss. Through the use of remote, optically powered amplification, an unprecedented detection range of 100 km is possible without the need for any electronics and therefore remote power in the field. A system can thus monitor 200 km of pipeline when configured to monitor 100 km upstream and downstream from a single location. As well as detecting conditions and events leading to leaks, this fully integrated system provides a means of detecting and locating small leaks in gas pipelines below the threshold of present online leak detection systems based on monitoring flow parameters. Other significant benefits include: potential reductions in construction costs; enhancement of the operator's existing integrity management program; potential reductions in surveillance costs and HSE risks. In addition to onshore pipeline systems this combination of functionality and range is available for practicable

  8. Condition monitoring of main coolant pumps, Dhruva

    International Nuclear Information System (INIS)

    Prasad, V.; Satheesh, C.; Acharya, V.N.; Tikku, A.C.; Mishra, S.K.

    2002-01-01

    Full text: Dhruva is a 100 MW research reactor with natural uranium fuel, heavy water as moderator and primary coolant. Three Centrifugal pumps circulate the primary coolant across the core and the heat exchangers. Each pump is coupled to a flywheel (FW) assembly in order to meet operational safety requirements. All the 3 main coolant pump (MCP) sets are required to operate during operation of the reactor. The pump-sets are in operation since the year 1984 and have logged more than 1,00,000 hrs. Frequent breakdowns of its FW bearings were experienced during initial years of operation. Condition monitoring of these pumps, largely on vibration based parameters, was initiated on regular basis. Break-downs of main coolant pumps reduced considerably due to the fair accurate predictions of incipient break-downs and timely maintenance efforts. An effort is made in this paper to share the experience

  9. The Effect of Machining Conditions on the Forces in the Process of Roller Brush Machining

    Directory of Open Access Journals (Sweden)

    Jakub Matuszak

    2017-12-01

    Full Text Available Because of its advantages, brushing processing has many uses. The main ones include the removal of corrosion products, surface cleaning, deburring and shaping the properties of the surface layer. The intensity of these processes depends on the degree of impact of brush fibres on the work surface. In the case of tools, in which the resilient fibres are the working elements, forces in the brushing process, apart from the machining parameters, depend on the characteristics and overall dimensions of individual fibres. The paper presents the results of studies of the influence of technological parameters and type of fibres on the radial force in the brushing process.

  10. Monitoring wear and corrosion in industrial machines and systems: A radiation tool

    International Nuclear Information System (INIS)

    Konstantinov, I.O.; Zatolokin, B.V.

    1994-01-01

    Industrial equipment and machines, transport systems, nuclear and conventional power plants, pipelines, and other materials is substantially influenced by degradation processes such as wear and corrosion. For safety and economic reasons, appropriately monitoring the damage could prevent dangerous accidents. When the surfaces of machine parts under investigation are not easy to reach or are concealed by overlying structures, nuclear methods have become powerful tools for examination. They include X-ray radiography, neutron radiography, and a technique known as thin layer activation (TLA)

  11. RAW MILK IN AUTOMATIC SALE MACHINES: MONITORING PLAN IN PIEDEMONT REGION

    Directory of Open Access Journals (Sweden)

    S. Gallina

    2010-06-01

    Full Text Available Raw milk at vending machine is surging in popularity amongst consumers of Northern Italy; indeed in Piedmont Region there are more than 100 vending machines. In June 2008 Piedmont Region set out a specific monitoring plan to check the milk quality. From June to December 2008, 113 raw milk samples were collected at vending machines. Samples were analysed for Listeria monocytogenes, Salmonella spp., coagulase positive staphylococci, Staphylococcus aureus and Campylobacter. Moreover, 100 samples were analysed for the quantification of aflatoxin M1. 26 samples have been resulted Not Conform for the hygienic criteria and 1 exceeded the aflatoxin M1 limit.

  12. A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring

    Directory of Open Access Journals (Sweden)

    Yong Li

    2014-01-01

    Full Text Available The translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM has been considered as one of the advanced maintenance schemes to achieve effective, reliable and cost-effective operation of machine systems, however, current vibration-based maintenance schemes cannot be employed directly in the translational axis system, due to its complex structure and the inefficiency of commonly used condition monitoring features. In this paper, a wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. Firstly, the quadratic nonlinearity of the servomotor torque signature is discussed, and then, a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection. On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis. The properties of the proposed quadratic nonlinearity feature are investigated by simulations. Subsequently, this feature is applied to the real-world servomotor torque data collected from the X-axis on a high precision vertical machining centre. All the results show that the performance of the proposed feature is much better than that of original condition monitoring features.

  13. Remote condition-based monitoring of turbines

    International Nuclear Information System (INIS)

    2005-01-01

    specific point in time; Timewave: Amount of motion and symmetry of wave shape (i.e., a truncated wave can be an indication of a rub); Orbits: A cross-sectional view of shaft movement; DC Gap Voltage: A measurement of the distance (e.g., gap) between the shaft and the proximity probes. This value is useful in determining bearing wear of shaft centerline location. Daily monitoring of these metrics will not only warn of impending failure, but provide valuable information regarding the possible cause of the impending failure and an approximate indication of time to failure. In the event of a sudden problem and subsequent trip of a turbine, this data helps determine the root cause of the failure. This results in faster problem resolution and a quicker restart of the turbine. Additionally, daily monitoring of these metrics allows companies to watch a problematic turbine's health until the next scheduled outage. Azima's remote, condition-based monitoring system and diagnostics service is an effective way to collect and trend these metrics on a daily basis, as well as supply expert advice in the event of any anomalies. The Azima system aggregates the analog data from the proximity probes mounted on the turbine at a sensor hub. The sensor hub digitizes the analog data and then, either wirelessly or through a wired Ethernet connection, sends the data via the Internet to a hosted server. The hosted server maintains the software that trends the data (e.g., vibration, spectrum, timewave, DC gap voltage, and orbits) and provides automatic alerting. The data can be accessed anywhere, anytime through a standard Web browser. The advantages of daily, remote, condition-based monitoring include: Daily monitoring of vital turbine health metrics to detect impending problems before they become critical; Automatic alerting when a change in condition is detected; Anywhere, anytime access via a standard Web browser. This allows multiple groups at different locations to simultaneously review and

  14. The effect of cutting conditions on power inputs when machining

    Science.gov (United States)

    Petrushin, S. I.; Gruby, S. V.; Nosirsoda, Sh C.

    2016-08-01

    Any technological process involving modification of material properties or product form necessitates consumption of a certain power amount. When developing new technologies one should take into account the benefits of their implementation vs. arising power inputs. It is revealed that procedures of edge cutting machining are the most energy-efficient amongst the present day forming procedures such as physical and technical methods including electrochemical, electroerosion, ultrasound, and laser processing, rapid prototyping technologies etc, such as physical and technical methods including electrochemical, electroerosion, ultrasound, and laser processing, rapid prototyping technologies etc. An expanded formula for calculation of power inputs is deduced, which takes into consideration the mode of cutting together with the tip radius, the form of the replaceable multifaceted insert and its wear. Having taken as an example cutting of graphite iron by the assembled cutting tools with replaceable multifaceted inserts the authors point at better power efficiency of high feeding cutting in comparison with high-speed cutting.

  15. Application of Support Vector Machine to Forex Monitoring

    Science.gov (United States)

    Kamruzzaman, Joarder; Sarker, Ruhul A.

    Previous studies have demonstrated superior performance of artificial neural network (ANN) based forex forecasting models over traditional regression models. This paper applies support vector machines to build a forecasting model from the historical data using six simple technical indicators and presents a comparison with an ANN based model trained by scaled conjugate gradient (SCG) learning algorithm. The models are evaluated and compared on the basis of five commonly used performance metrics that measure closeness of prediction as well as correctness in directional change. Forecasting results of six different currencies against Australian dollar reveal superior performance of SVM model using simple linear kernel over ANN-SCG model in terms of all the evaluation metrics. The effect of SVM parameter selection on prediction performance is also investigated and analyzed.

  16. Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy

    Science.gov (United States)

    Gueth, P.; Dauvergne, D.; Freud, N.; Létang, J. M.; Ray, C.; Testa, E.; Sarrut, D.

    2013-07-01

    Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations.

  17. Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy

    International Nuclear Information System (INIS)

    Gueth, P; Freud, N; Létang, J M; Sarrut, D; Dauvergne, D; Ray, C; Testa, E

    2013-01-01

    Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations. (paper)

  18. Proportional monitoring of the acoustic emission in crypto-conditions

    Directory of Open Access Journals (Sweden)

    Petr Dostál

    2011-01-01

    Full Text Available The work is aimed at studying corrosion and fatigue properties of aluminum alloys by means of acoustic emission (AE. During material degradation are acoustic events scanned and evaluated. The main objective of the article is a description of behavior of aluminum alloys degraded in specific conditions and critical degradation stages determination. The first part of the article describes controlled degradation of the material in the crypto–conditions. The acoustic emission method is used for process analyzing. This part contains the AE signals assessment and comparing aluminium alloy to steel. Then the specimens are loaded on high-cyclic loading apparatus for fatigue life monitoring. Also, the synergy of fatigue and corrosion processes is taken into account.The aim is the description of fatigue properties for aluminum alloys that have already been corrosion-degraded. Attention is also focused on the structure of fatigue cracks. The main part of the article is aimed at corrosion degradation of aluminium alloys researched in real time by means of AE. The most important benefit of AE detection/recording is that it provides information about the process in real time. Using this measurement system is possible to observe the current status of the machines/devices and to prevent serious accidents.

  19. A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings.

    Science.gov (United States)

    Liu, Jie; Hu, Youmin; Wu, Bo; Wang, Yan; Xie, Fengyun

    2017-05-18

    The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD). Parameters of the VMD, in the form of generalized intervals, provide a concise representation for aleatory and epistemic uncertainty and improve the robustness of identification. The multi-scale permutation entropy method is applied to extract state features from the decomposed signals in different operating conditions. Traditional principal component analysis is adopted to reduce feature size and computational cost. With the extracted features' information, the generalized hidden Markov model, based on generalized interval probability, is used to recognize and classify the fault types and fault severity levels. Finally, the experiment results show that the proposed method is effective at recognizing and classifying the fault types and fault severity levels of rolling bearings. This monitoring method is also efficient enough to quantify the two uncertainty components.

  20. A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings

    Directory of Open Access Journals (Sweden)

    Jie Liu

    2017-05-01

    Full Text Available The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD. Parameters of the VMD, in the form of generalized intervals, provide a concise representation for aleatory and epistemic uncertainty and improve the robustness of identification. The multi-scale permutation entropy method is applied to extract state features from the decomposed signals in different operating conditions. Traditional principal component analysis is adopted to reduce feature size and computational cost. With the extracted features’ information, the generalized hidden Markov model, based on generalized interval probability, is used to recognize and classify the fault types and fault severity levels. Finally, the experiment results show that the proposed method is effective at recognizing and classifying the fault types and fault severity levels of rolling bearings. This monitoring method is also efficient enough to quantify the two uncertainty components.

  1. Conditions for Model Matching of Switched Asynchronous Sequential Machines with Output Feedback

    OpenAIRE

    Jung–Min Yang

    2016-01-01

    Solvability of the model matching problem for input/output switched asynchronous sequential machines is discussed in this paper. The control objective is to determine the existence condition and design algorithm for a corrective controller that can match the stable-state behavior of the closed-loop system to that of a reference model. Switching operations and correction procedures are incorporated using output feedback so that the controlled switched machine can show the ...

  2. Determining the Efficiency of Adaptation of Foreign Economic Activity of Machine-Building Enterprises in Conditions of Deepening the European Integration Process of Ukraine

    Directory of Open Access Journals (Sweden)

    Semeniuk Iryna Yu.

    2018-02-01

    Full Text Available The article determines that introduction and implementation of the mechanism for foreign economic adaptation of machine-building enterprises to the conditions of the European integration processes requires constant monitoring of the processes of export-import operations and the adaptation activities to identify current problems and avoid risks. It has been found that one of the monitoring instruments is the system of indicators, which provides to evaluate the efficiency of use of the mechanism for foreign economic adaptation of a machine-building enterprise by comparing the values of the obtained indicators after accomplishing adaptation changes with the values of the indicators of previous periods. It is suggested to determine efficiency of adaptation of foreign economic activity of machine-building enterprises to conditions of deepening of the European integration process of Ukraine by means of: index of change of volume of exported production of a machine-building enterprise to the EU countries; weighted average of the change in the share of the European market, which is covered by the enterprise’s products; indicator of efficiency of exports of production of a machine-building enterprise to the European Union countries; indicator of the index of changes in the volume of permanent orders from European partners; integral indicator of efficiency of use of adaptive potential of a machine-building enterprise in conditions of integration processes.

  3. Condition Monitoring of Machinery in Non-Stationary Operations : Proceedings of the Second International Conference "Condition Monitoring of Machinery in Non-Stationnary Operations"

    CERN Document Server

    Bartelmus, Walter; Chaari, Fakher; Zimroz, Radoslaw; Haddar, Mohamed

    2012-01-01

    Condition monitoring of machines in non-stationary operations (CMMNO) can be seen as the major challenge for research in the field of machinery diagnostics. Condition monitoring of machines in non-stationary operations is the title of the presented book and the title of the Conference held in Hammamet - Tunisia March 26 – 28, 2012. It is the second conference under this title, first took place in Wroclaw - Poland , March 2011. The subject CMMNO comes directly from industry needs and observation of real objects. Most monitored and diagnosed objects used in industry works in non-stationary operations condition. The non-stationary operations come from fulfillment of machinery tasks, for which they are designed for. All machinery used in different kind of mines, transport systems, vehicles like: cars, buses etc, helicopters, ships and battleships and so on work in non-stationary operations. The papers included in the book are shaped by the organizing board of the conference and authors of the papers. The papers...

  4. Some problems of control of dynamical conditions of technological vibrating machines

    Science.gov (United States)

    Kuznetsov, N. K.; Lapshin, V. L.; Eliseev, A. V.

    2017-10-01

    The possibility of control of dynamical condition of the shakers that are designed for vibration treatment of parts interacting with granular media is discussed. The aim of this article is to develop the methodological basis of technology of creation of mathematical models of shake tables and the development of principles of formation of vibrational fields, estimation of their parameters and control of the structure vibration fields. Approaches to build mathematical models that take into account unilateral constraints, the relationships between elements, with the vibrating surface are developed. Methods intended to construct mathematical model of linear mechanical oscillation systems are used. Small oscillations about the position of static equilibrium are performed. The original method of correction of vibration fields by introduction of the oscillating system additional ties to the structure are proposed. Additional ties are implemented in the form of a mass-inertial device for changing the inertial parameters of the working body of the vibration table by moving the mass-inertial elements. The concept of monitoring the dynamic state of the vibration table based on the original measuring devices is proposed. Estimation for possible changes in dynamic properties is produced. The article is of interest for specialists in the field of creation of vibration technology machines and equipment.

  5. Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model

    Directory of Open Access Journals (Sweden)

    Guofeng Wang

    2014-11-01

    Full Text Available Tool condition monitoring (TCM plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM, hidden Markov model (HMM and radius basis function (RBF are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.

  6. Tiger: knowledge based gas turbine condition monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Trave-Massuyes, L. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Quevedo, J. [University of Catalonia, (Spain); Milne, R.; Nicol, Ch.

    1995-12-31

    Exxon petrochemical plant in Scotland requires continuous ethylene supply from offshore site in North Sea. The supply is achieved thanks to compressors driven by a 28 MW gas turbine, whose monitoring is of major importance. The TIGER fault diagnostic system is a knowledge base system containing a prediction model. (D.L.) 11 refs.

  7. Tiger: knowledge based gas turbine condition monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Trave-Massuyes, L [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Quevedo, J [University of Catalonia, (Spain); Milne, R; Nicol, Ch

    1996-12-31

    Exxon petrochemical plant in Scotland requires continuous ethylene supply from offshore site in North Sea. The supply is achieved thanks to compressors driven by a 28 MW gas turbine, whose monitoring is of major importance. The TIGER fault diagnostic system is a knowledge base system containing a prediction model. (D.L.) 11 refs.

  8. Optimizing cutting conditions on sustainable machining of aluminum alloy to minimize power consumption

    Science.gov (United States)

    Nur, Rusdi; Suyuti, Muhammad Arsyad; Susanto, Tri Agus

    2017-06-01

    Aluminum is widely utilized in the industrial sector. There are several advantages of aluminum, i.e. good flexibility and formability, high corrosion resistance and electrical conductivity, and high heat. Despite of these characteristics, however, pure aluminum is rarely used because of its lacks of strength. Thus, most of the aluminum used in the industrial sectors was in the form of alloy form. Sustainable machining can be considered to link with the transformation of input materials and energy/power demand into finished goods. Machining processes are responsible for environmental effects accepting to their power consumption. The cutting conditions have been optimized to minimize the cutting power, which is the power consumed for cutting. This paper presents an experimental study of sustainable machining of Al-11%Si base alloy that was operated without any cooling system to assess the capacity in reducing power consumption. The cutting force was measured and the cutting power was calculated. Both of cutting force and cutting power were analyzed and modeled by using the central composite design (CCD). The result of this study indicated that the cutting speed has an effect on machining performance and that optimum cutting conditions have to be determined, while sustainable machining can be followed in terms of minimizing power consumption and cutting force. The model developed from this study can be used for evaluation process and optimization to determine optimal cutting conditions for the performance of the whole process.

  9. Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges

    Directory of Open Access Journals (Sweden)

    Ioannis Matthaiou

    2017-09-01

    Full Text Available In this study, condition monitoring strategies are examined for gas turbine engines using vibration data. The focus is on data-driven approaches, for this reason a novelty detection framework is considered for the development of reliable data-driven models that can describe the underlying relationships of the processes taking place during an engine’s operation. From a data analysis perspective, the high dimensionality of features extracted and the data complexity are two problems that need to be dealt with throughout analyses of this type. The latter refers to the fact that the healthy engine state data can be non-stationary. To address this, the implementation of the wavelet transform is examined to get a set of features from vibration signals that describe the non-stationary parts. The problem of high dimensionality of the features is addressed by “compressing” them using the kernel principal component analysis so that more meaningful, lower-dimensional features can be used to train the pattern recognition algorithms. For feature discrimination, a novelty detection scheme that is based on the one-class support vector machine (OCSVM algorithm is chosen for investigation. The main advantage, when compared to other pattern recognition algorithms, is that the learning problem is being cast as a quadratic program. The developed condition monitoring strategy can be applied for detecting excessive vibration levels that can lead to engine component failure. Here, we demonstrate its performance on vibration data from an experimental gas turbine engine operating on different conditions. Engine vibration data that are designated as belonging to the engine’s “normal” condition correspond to fuels and air-to-fuel ratio combinations, in which the engine experienced low levels of vibration. Results demonstrate that such novelty detection schemes can achieve a satisfactory validation accuracy through appropriate selection of two parameters of the

  10. Surface Characteristics of Machined NiTi Shape Memory Alloy: The Effects of Cryogenic Cooling and Preheating Conditions

    Science.gov (United States)

    Kaynak, Y.; Huang, B.; Karaca, H. E.; Jawahir, I. S.

    2017-07-01

    This experimental study focuses on the phase state and phase transformation response of the surface and subsurface of machined NiTi alloys. X-ray diffraction (XRD) analysis and differential scanning calorimeter techniques were utilized to measure the phase state and the transformation response of machined specimens, respectively. Specimens were machined under dry machining at ambient temperature, preheated conditions, and cryogenic cooling conditions at various cutting speeds. The findings from this research demonstrate that cryogenic machining substantially alters austenite finish temperature of martensitic NiTi alloy. Austenite finish ( A f) temperature shows more than 25 percent increase resulting from cryogenic machining compared with austenite finish temperature of as-received NiTi. Dry and preheated conditions do not substantially alter austenite finish temperature. XRD analysis shows that distinctive transformation from martensite to austenite occurs during machining process in all three conditions. Complete transformation from martensite to austenite is observed in dry cutting at all selected cutting speeds.

  11. Artificial intelligence tools decision support systems in condition monitoring and diagnosis

    CERN Document Server

    Galar Pascual, Diego

    2015-01-01

    Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource: Addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniques Considers the merits of each technique as well as the issues associated with real-life application Covers classification methods, from neural networks to Bayesian and support vector machines Proposes fuzzy logic to explain the uncertainties associated with diagnostic processes Provides data sets, sample signals, and MATLAB® code for algorithm testing Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis delivers a thorough evaluation of the latest AI tools for CM, describing the most common fault diagnosis techniques used and the data acquired when these techniques are applied.

  12. Technology review: prototyping platforms for monitoring ambient conditions.

    Science.gov (United States)

    Afolaranmi, Samuel Olaiya; Ramis Ferrer, Borja; Martinez Lastra, Jose Luis

    2018-05-08

    The monitoring of ambient conditions in indoor spaces is very essential owing to the amount of time spent indoors. Specifically, the monitoring of air quality is significant because contaminated air affects the health, comfort and productivity of occupants. This research work presents a technology review of prototyping platforms for monitoring ambient conditions in indoor spaces. It involves the research on sensors (for CO 2 , air quality and ambient conditions), IoT platforms, and novel and commercial prototyping platforms. The ultimate objective of this review is to enable the easy identification, selection and utilisation of the technologies best suited for monitoring ambient conditions in indoor spaces. Following the review, it is recommended to use metal oxide sensors, optical sensors and electrochemical sensors for IAQ monitoring (including NDIR sensors for CO 2 monitoring), Raspberry Pi for data processing, ZigBee and Wi-Fi for data communication, and ThingSpeak IoT platform for data storage, analysis and visualisation.

  13. Gearbox Condition Monitoring Using Advanced Classifiers

    Directory of Open Access Journals (Sweden)

    P. Večeř

    2010-01-01

    Full Text Available New efficient and reliable methods for gearbox diagnostics are needed in automotive industry because of growing demand for production quality. This paper presents the application of two different classifiers for gearbox diagnostics – Kohonen Neural Networks and the Adaptive-Network-based Fuzzy Interface System (ANFIS. Two different practical applications are presented. In the first application, the tested gearboxes are separated into two classes according to their condition indicators. In the second example, ANFIS is applied to label the tested gearboxes with a Quality Index according to the condition indicators. In both applications, the condition indicators were computed from the vibration of the gearbox housing. 

  14. Analysis on machine tool systems using spindle vibration monitoring for automatic tool changer

    OpenAIRE

    Shang-Liang Chen; Yin-Ting Cheng; Chin-Fa Su

    2015-01-01

    Recently, the intelligent systems of technology have become one of the major items in the development of machine tools. One crucial technology is the machinery status monitoring function, which is required for abnormal warnings and the improvement of cutting efficiency. During processing, the mobility act of the spindle unit determines the most frequent and important part such as automatic tool changer. The vibration detection system includes the development of hardware and software, such as ...

  15. Online Vibration Monitoring of a Water Pump Machine to Detect Its Malfunction Components Based on Artificial Neural Network

    Science.gov (United States)

    Rahmawati, P.; Prajitno, P.

    2018-04-01

    Vibration monitoring is a measurement instrument used to identify, predict, and prevent failures in machine instruments[6]. This is very needed in the industrial applications, cause any problem with the equipment or plant translates into economical loss and they are mostly monitored component off-line[2]. In this research, a system has been developed to detect the malfunction of the components of Shimizu PS-128BT water pump machine, such as capacitor, bearing and impeller by online measurements. The malfunction components are detected by taking vibration data using a Micro-Electro-Mechanical System(MEMS)-based accelerometer that are acquired by using Raspberry Pi microcomputer and then the data are converted into the form of Relative Power Ratio(RPR). In this form the signal acquired from different components conditions have different patterns. The collected RPR used as the base of classification process for recognizing the damage components of the water pump that are conducted by Artificial Neural Network(ANN). Finally, the damage test result will be sent via text message using GSM module that are connected to Raspberry Pi microcomputer. The results, with several measurement readings, with each reading in 10 minutes duration for each different component conditions, all cases yield 100% of accuracies while in the case of defective capacitor yields 90% of accuracy.

  16. A machine-hearing system exploiting head movements for binaural sound localisation in reverberant conditions

    DEFF Research Database (Denmark)

    May, Tobias; Ma, Ning; Wierstorf, Hagen

    2015-01-01

    This paper is concerned with machine localisation of multiple active speech sources in reverberant environments using two (binaural) microphones. Such conditions typically present a problem for ‘classical’ binaural models. Inspired by the human ability to utilise head movements, the current study...

  17. New oil condition monitoring system, Wearsens® enables continuous, online detection of critical operating conditions and wear damage

    Directory of Open Access Journals (Sweden)

    Manfred Mauntz

    2015-12-01

    Full Text Available A new oil sensor system is presented for the continuous, online measurement of the wear in turbines, industrial gears, generators, hydraulic systems and transformers. Detection of change is much earlier than existing technologies such as particle counting, vibration measurement or recording temperature. Thus targeted, corrective procedures and/or maintenance can be carried out before actual damage occurs. Efficient machine utilization, accurately timed preventive maintenance, increased service life and a reduction of downtime can all be achieved. The presented sensor system effectively controls the proper operation conditions of bearings and cogwheels in gears. The online diagnostics system measures components of the specific complex impedance of oils. For instance, metal abrasion due to wear debris, broken oil molecules, forming acids or oil soaps, result in an increase of the electrical conductivity, which directly correlates with the degree of contamination of the oil. For additivated lubricants, the stage of degradation of the additives can also be derived from changes in the dielectric constant. The determination of impurities or reduction in the quality of the oil and the quasi continuous evaluation of wear and chemical aging follow the holistic approach of a real-time monitoring of an alteration in the condition of the oil-machine system. Once the oil condition monitoring sensors are installed on the wind turbine, industrial gearbox and test stands, the measuring data can be displayed and evaluated elsewhere. The signals are transmitted to a web-based condition monitoring system via LAN, WLAN or serial interfaces of the sensor unit. Monitoring of the damage mechanisms during proper operation below the tolerance limits of the components enables specific preventive maintenance independent of rigid inspection intervals.

  18. Comparative analysis of the machine repair Problem with imperfect coverage and service pressure condition

    Science.gov (United States)

    Wang, Kuo-Hsiung; Liou, Cheng-Dar; Lin, Yu-Hsueh

    2013-02-01

    We analyze the warm-standby M/M/R machine repair problem with multiple imperfect coverage which involving the service pressure condition. When an operating machine (or warm standby) fails, it may be immediately detected, located, and replaced with a coverage probability c by a standby if one is available. A recursive method is used to develop the steady-state analytic solutions. The total expected profit function per unit time is derived to determine the joint optimal values at the maximum profit. We utilize the direct search method to measure the various characteristics of the profit function followed by Quasi-Newton method to search the optimal solutions.

  19. Modern techniques for condition monitoring of railway vehicle dynamics

    International Nuclear Information System (INIS)

    Ngigi, R W; Pislaru, C; Ball, A; Gu, F

    2012-01-01

    A modern railway system relies on sophisticated monitoring systems for maintenance and renewal activities. Some of the existing conditions monitoring techniques perform fault detection using advanced filtering, system identification and signal analysis methods. These theoretical approaches do not require complex mathematical models of the system and can overcome potential difficulties associated with nonlinearities and parameter variations in the system. Practical applications of condition monitoring tools use sensors which are mounted either on the track or rolling stock. For instance, monitoring wheelset dynamics could be done through the use of track-mounted sensors, while vehicle-based sensors are preferred for monitoring the train infrastructure. This paper attempts to collate and critically appraise the modern techniques used for condition monitoring of railway vehicle dynamics by analysing the advantages and shortcomings of these methods.

  20. Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning.

    Science.gov (United States)

    Boissoneault, Jeff; Sevel, Landrew; Letzen, Janelle; Robinson, Michael; Staud, Roland

    2017-01-01

    Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

  1. Recovery process of wall condition in KSTAR vacuum vessel after temporal machine-vent for repair

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Kwang Pyo, E-mail: kpkim@nfri.er.ke; Hong, Suk-Ho; Lee, Hyunmyung; Song, Jae-in; Jung, Nam-Yong; Lee, Kunsu; Chu, Yong; Kim, Hakkun; Park, Kaprai; Oh, Yeong-Kook

    2015-10-15

    Highlights: • Efforts have been made to obtain vacuum condition that is essential for the plasma experiments. • For example, the vacuum vessel should be vented to repair in-vessel components such as diagnostic shutter, and PFC damaged by high energy plasma. • Here, we present the recovery process of wall condition in KSTAR after temporal machine-vent for repair. • It is found that an acceptable vacuum condition has been achieved only by plasma based wall conditioning techniques such as baking, GDC, and boronization. • This study was that the proper recovering method of the vacuum condition should be developed according to the severity of the accident. - Abstract: Efforts have been made to obtain vacuum condition that is essential for the plasma experiments. Under certain situations, for example, the vacuum vessel should be vented to repair in-vessel components such as diagnostic shutter, exchange of window for diagnostic equipment, and PFC damaged by high energy plasma. For the quick restart of the campaign, a recovery process was established to make the vacuum condition acceptable for the plasma experiment. In this paper, we present the recovery process of wall condition in KSTAR after temporal machine-vent for repair. It is found that an acceptable vacuum condition has been achieved only by plasma based wall conditioning techniques such as baking, GDC, and boronization. This study was that the proper recovering method of the vacuum condition should be developed according to the severity of the accident.

  2. Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Alessandra Caggiano

    2018-03-01

    Full Text Available Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA is proposed. PCA allowed to identify a smaller number of features (k = 2 features, the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax was achieved, with predicted values very close to the measured tool wear values.

  3. Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition.

    Science.gov (United States)

    Caggiano, Alessandra

    2018-03-09

    Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features ( k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear ( VB max ) was achieved, with predicted values very close to the measured tool wear values.

  4. Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

    Science.gov (United States)

    2018-01-01

    Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features (k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax) was achieved, with predicted values very close to the measured tool wear values. PMID:29522443

  5. Radiation Monitoring System in Advanced Spent Fuel Conditioning Process Facility

    Energy Technology Data Exchange (ETDEWEB)

    You, Gil Sung; Kook, D. H.; Choung, W. M.; Ku, J. H.; Cho, I. J.; You, G. S.; Kwon, K. C.; Lee, W. K.; Lee, E. P

    2006-09-15

    The Advanced spent fuel Conditioning Process is under development for effective management of spent fuel by converting UO{sub 2} into U-metal. For demonstration of this process, {alpha}-{gamma} type new hot cell was built in the IMEF basement . To secure against radiation hazard, this facility needs radiation monitoring system which will observe the entire operating area before the hot cell and service area at back of it. This system consists of 7 parts; Area Monitor for {gamma}-ray, Room Air Monitor for particulate and iodine in both area, Hot cell Monitor for hot cell inside high radiation and rear door interlock, Duct Monitor for particulate of outlet ventilation, Iodine Monitor for iodine of outlet duct, CCTV for watching workers and material movement, Server for management of whole monitoring system. After installation and test of this, radiation monitoring system will be expected to assist the successful ACP demonstration.

  6. Radiation Monitoring System in Advanced Spent Fuel Conditioning Process Facility

    International Nuclear Information System (INIS)

    You, Gil Sung; Kook, D. H.; Choung, W. M.; Ku, J. H.; Cho, I. J.; You, G. S.; Kwon, K. C.; Lee, W. K.; Lee, E. P.

    2006-09-01

    The Advanced spent fuel Conditioning Process is under development for effective management of spent fuel by converting UO 2 into U-metal. For demonstration of this process, α-γ type new hot cell was built in the IMEF basement . To secure against radiation hazard, this facility needs radiation monitoring system which will observe the entire operating area before the hot cell and service area at back of it. This system consists of 7 parts; Area Monitor for γ-ray, Room Air Monitor for particulate and iodine in both area, Hot cell Monitor for hot cell inside high radiation and rear door interlock, Duct Monitor for particulate of outlet ventilation, Iodine Monitor for iodine of outlet duct, CCTV for watching workers and material movement, Server for management of whole monitoring system. After installation and test of this, radiation monitoring system will be expected to assist the successful ACP demonstration

  7. Using the motor to monitor pump conditions

    Energy Technology Data Exchange (ETDEWEB)

    Casada, D. [Oak Ridge National Lab., TN (United States)

    1996-12-01

    When the load of a mechanical device being driven by a motor changes, whether in response to changes in the overall process or changes in the performance of the driven device, the motor inherently responds. For induction motors, the current amplitude and phase angle change as the shaft load changes. By examining the details of these changes in amplitude and phase, load fluctuations of the driven device can be observed. The usefulness of the motor as a transducer to improve the understanding of devices with high torque fluctuations, such as positive displacement compressors and motor-operated valves, has been recognized and demonstrated for a number of years. On such devices as these, the spectrum of the motor current amplitude, phase, or power normally has certain characteristic peaks associated with various load components, such as the piston stroke or gear tooth meshing frequencies. Comparison and trending of the amplitudes of these peaks has been shown to provide some indication of their mechanical condition. For most centrifugal pumps, the load fluctuations are normally low in torque amplitude, and as a result, the motor experiences a correspondingly lower level of load fluctuation. However, both laboratory and field test data have demonstrated that the motor does provide insight into some important pump performance conditions, such as hydraulic stability and pump-to-motor alignment. Comparisons of other dynamic signals, such as vibration and pressure pulsation, to motor data for centrifugal pumps are provided. The effects of inadequate suction head, misalignment, mechanical and hydraulic unbalance on these signals are presented.

  8. Using the motor to monitor pump conditions

    International Nuclear Information System (INIS)

    Casada, D.

    1996-01-01

    When the load of a mechanical device being driven by a motor changes, whether in response to changes in the overall process or changes in the performance of the driven device, the motor inherently responds. For induction motors, the current amplitude and phase angle change as the shaft load changes. By examining the details of these changes in amplitude and phase, load fluctuations of the driven device can be observed. The usefulness of the motor as a transducer to improve the understanding of devices with high torque fluctuations, such as positive displacement compressors and motor-operated valves, has been recognized and demonstrated for a number of years. On such devices as these, the spectrum of the motor current amplitude, phase, or power normally has certain characteristic peaks associated with various load components, such as the piston stroke or gear tooth meshing frequencies. Comparison and trending of the amplitudes of these peaks has been shown to provide some indication of their mechanical condition. For most centrifugal pumps, the load fluctuations are normally low in torque amplitude, and as a result, the motor experiences a correspondingly lower level of load fluctuation. However, both laboratory and field test data have demonstrated that the motor does provide insight into some important pump performance conditions, such as hydraulic stability and pump-to-motor alignment. Comparisons of other dynamic signals, such as vibration and pressure pulsation, to motor data for centrifugal pumps are provided. The effects of inadequate suction head, misalignment, mechanical and hydraulic unbalance on these signals are presented

  9. Replacement Condition Detection of Railway Point Machines Using an Electric Current Sensor.

    Science.gov (United States)

    Sa, Jaewon; Choi, Younchang; Chung, Yongwha; Kim, Hee-Young; Park, Daihee; Yoon, Sukhan

    2017-01-29

    Detecting replacement conditions of railway point machines is important to simultaneously satisfy the budget-limit and train-safety requirements. In this study, we consider classification of the subtle differences in the aging effect-using electric current shape analysis-for the purpose of replacement condition detection of railway point machines. After analyzing the shapes of after-replacement data and then labeling the shapes of each before-replacement data, we can derive the criteria that can handle the subtle differences between "does-not-need-to-be-replaced" and "needs-to-be-replaced" shapes. On the basis of the experimental results with in-field replacement data, we confirmed that the proposed method could detect the replacement conditions with acceptable accuracy, as well as provide visual interpretability of the criteria used for the time-series classification.

  10. Replacement Condition Detection of Railway Point Machines Using an Electric Current Sensor

    Science.gov (United States)

    Sa, Jaewon; Choi, Younchang; Chung, Yongwha; Kim, Hee-Young; Park, Daihee; Yoon, Sukhan

    2017-01-01

    Detecting replacement conditions of railway point machines is important to simultaneously satisfy the budget-limit and train-safety requirements. In this study, we consider classification of the subtle differences in the aging effect—using electric current shape analysis—for the purpose of replacement condition detection of railway point machines. After analyzing the shapes of after-replacement data and then labeling the shapes of each before-replacement data, we can derive the criteria that can handle the subtle differences between “does-not-need-to-be-replaced” and “needs-to-be-replaced” shapes. On the basis of the experimental results with in-field replacement data, we confirmed that the proposed method could detect the replacement conditions with acceptable accuracy, as well as provide visual interpretability of the criteria used for the time-series classification. PMID:28146057

  11. A Comparative Experimental Study on the Use of Machine Learning Approaches for Automated Valve Monitoring Based on Acoustic Emission Parameters

    Science.gov (United States)

    Ali, Salah M.; Hui, K. H.; Hee, L. M.; Salman Leong, M.; Al-Obaidi, M. A.; Ali, Y. H.; Abdelrhman, Ahmed M.

    2018-03-01

    Acoustic emission (AE) analysis has become a vital tool for initiating the maintenance tasks in many industries. However, the analysis process and interpretation has been found to be highly dependent on the experts. Therefore, an automated monitoring method would be required to reduce the cost and time consumed in the interpretation of AE signal. This paper investigates the application of two of the most common machine learning approaches namely artificial neural network (ANN) and support vector machine (SVM) to automate the diagnosis of valve faults in reciprocating compressor based on AE signal parameters. Since the accuracy is an essential factor in any automated diagnostic system, this paper also provides a comparative study based on predictive performance of ANN and SVM. AE parameters data was acquired from single stage reciprocating air compressor with different operational and valve conditions. ANN and SVM diagnosis models were subsequently devised by combining AE parameters of different conditions. Results demonstrate that ANN and SVM models have the same results in term of prediction accuracy. However, SVM model is recommended to automate diagnose the valve condition in due to the ability of handling a high number of input features with low sampling data sets.

  12. Development of Dual-Axis MEMS Accelerometers for Machine Tools Vibration Monitoring

    Directory of Open Access Journals (Sweden)

    Chih-Yung Huang

    2016-07-01

    Full Text Available With the development of intelligent machine tools, monitoring the vibration by the accelerometer is an important issue. Accelerometers used for measuring vibration signals during milling processes require the characteristics of high sensitivity, high resolution, and high bandwidth. A commonly used accelerometer is the lead zirconate titanate (PZT type; however, integrating it into intelligent modules is excessively expensive and difficult. Therefore, the micro electro mechanical systems (MEMS accelerometer is an alternative with the advantages of lower price and superior integration. In the present study, we integrated two MEMS accelerometer chips into a low-pass filter and housing to develop a low-cost dual-axis accelerometer with a bandwidth of 5 kHz and a full scale range of ±50 g for measuring machine tool vibration. In addition, a platform for measuring the linearity, cross-axis sensitivity and frequency response of the MEMS accelerometer by using the back-to-back calibration method was also developed. Finally, cutting experiments with steady and chatter cutting were performed to verify the results of comparing the MEMS accelerometer with the PZT accelerometer in the time and frequency domains. The results demonstrated that the dual-axis MEMS accelerometer is suitable for monitoring the vibration of machine tools at low cost.

  13. A Machine-Learning and Filtering Based Data Assimilation Framework for Geologic Carbon Sequestration Monitoring Optimization

    Science.gov (United States)

    Chen, B.; Harp, D. R.; Lin, Y.; Keating, E. H.; Pawar, R.

    2017-12-01

    Monitoring is a crucial aspect of geologic carbon sequestration (GCS) risk management. It has gained importance as a means to ensure CO2 is safely and permanently stored underground throughout the lifecycle of a GCS project. Three issues are often involved in a monitoring project: (i) where is the optimal location to place the monitoring well(s), (ii) what type of data (pressure, rate and/or CO2 concentration) should be measured, and (iii) What is the optimal frequency to collect the data. In order to address these important issues, a filtering-based data assimilation procedure is developed to perform the monitoring optimization. The optimal monitoring strategy is selected based on the uncertainty reduction of the objective of interest (e.g., cumulative CO2 leak) for all potential monitoring strategies. To reduce the computational cost of the filtering-based data assimilation process, two machine-learning algorithms: Support Vector Regression (SVR) and Multivariate Adaptive Regression Splines (MARS) are used to develop the computationally efficient reduced-order-models (ROMs) from full numerical simulations of CO2 and brine flow. The proposed framework for GCS monitoring optimization is demonstrated with two examples: a simple 3D synthetic case and a real field case named Rock Spring Uplift carbon storage site in Southwestern Wyoming.

  14. Development of hardware system using temperature and vibration maintenance models integration concepts for conventional machines monitoring: A case study

    OpenAIRE

    Adeyeri, Michael Kanisuru; Mpofu, Khumbulani; Kareem, Buliaminu

    2016-01-01

    This article describes the integration of temperature and vibration models for maintenance monitoring of conventional machinery parts in which their optimal andbest functionalities are affected by abnormal changes in temperature and vibration values thereby resulting in machine failures, machines breakdown, poor quality of products, inability to meeting customers' demand, poor inventory control and just to mention a few. The work entails the use of temperature and vibration sensors as monitor...

  15. Methodology for testing a system for remote monitoring and control on auxiliary machines in electric vehicles

    Directory of Open Access Journals (Sweden)

    Dimitrov Vasil

    2017-01-01

    Full Text Available A laboratory system for remote monitoring and control of an asynchronous motor controlled by a soft starter and contemporary measuring and control devices has been developed and built. This laboratory system is used for research and in teaching. A study of the principles of operation, setting up and examination of intelligent energy meters, soft starters and PLC has been made as knowledge of the relevant software products is necessary. This is of great importance because systems for remote monitoring and control of energy consumption, efficiency and proper operation of the controlled objects are very often used in different spheres of industry, in building automation, transport, electricity distribution network, etc. Their implementation in electric vehicles for remote monitoring and control on auxiliary machines is also possible and very useful. In this paper, a methodology of tests is developed and some experiments are presented. Thus, an experimental verification of the developed methodology is made.

  16. Technical guide for monitoring selected conditions related to wilderness character

    Science.gov (United States)

    Peter Landres; Steve Boutcher; Liese Dean; Troy Hall; Tamara Blett; Terry Carlson; Ann Mebane; Carol Hardy; Susan Rinehart; Linda Merigliano; David N. Cole; Andy Leach; Pam Wright; Deb Bumpus

    2009-01-01

    The purpose of monitoring wilderness character is to improve wilderness stewardship by providing managers a tool to assess how selected actions and conditions related to wilderness character are changing over time. Wilderness character monitoring provides information to help answer two key questions about wilderness character and wilderness stewardship: 1. How is...

  17. Non-stationary condition monitoring through event alignment

    DEFF Research Database (Denmark)

    Pontoppidan, Niels Henrik; Larsen, Jan

    2004-01-01

    We present an event alignment framework which enables change detection in non-stationary signals. change detection. Classical condition monitoring frameworks have been restrained to laboratory settings with stationary operating conditions, which are not resembling real world operation....... In this paper we apply the technique for non-stationary condition monitoring of large diesel engines based on acoustical emission sensor signals. The performance of the event alignment is analyzed in an unsupervised probabilistic detection framework based on outlier detection with either Principal Component...... Analysis or Gaussian Processes modeling. We are especially interested in the true performance of the condition monitoring performance with mixed aligned and unaligned data, e.g. detection of fault condition of unaligned examples versus false alarms of aligned normal condition data. Further, we expect...

  18. Semi-supervised vibration-based classification and condition monitoring of compressors

    Science.gov (United States)

    Potočnik, Primož; Govekar, Edvard

    2017-09-01

    Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

  19. Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.

    Science.gov (United States)

    Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk

    2018-04-06

    Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry-wide problem in BPM, referred to as the "Low-N" problem, wherein a product has a limited production history. The current best industrial practice to address the Low-N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low-N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine-learning methods. To the best of authors' knowledge, this is the first article to provide a solution to the Low-N problem in biopharmaceutical manufacturing using machine-learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low-N scenarios. © 2018 Wiley Periodicals, Inc.

  20. Robust Machine Learning Variable Importance Analyses of Medical Conditions for Health Care Spending.

    Science.gov (United States)

    Rose, Sherri

    2018-03-11

    To propose nonparametric double robust machine learning in variable importance analyses of medical conditions for health spending. 2011-2012 Truven MarketScan database. I evaluate how much more, on average, commercially insured enrollees with each of 26 of the most prevalent medical conditions cost per year after controlling for demographics and other medical conditions. This is accomplished within the nonparametric targeted learning framework, which incorporates ensemble machine learning. Previous literature studying the impact of medical conditions on health care spending has almost exclusively focused on parametric risk adjustment; thus, I compare my approach to parametric regression. My results demonstrate that multiple sclerosis, congestive heart failure, severe cancers, major depression and bipolar disorders, and chronic hepatitis are the most costly medical conditions on average per individual. These findings differed from those obtained using parametric regression. The literature may be underestimating the spending contributions of several medical conditions, which is a potentially critical oversight. If current methods are not capturing the true incremental effect of medical conditions, undesirable incentives related to care may remain. Further work is needed to directly study these issues in the context of federal formulas. © Health Research and Educational Trust.

  1. Transformer ageing modern condition monitoring techniques and their interpretations

    CERN Document Server

    Purkait, Prithwiraj

    2017-01-01

    This book is a one-stop guide to state-of-the-art research in transformer ageing, condition monitoring and diagnosis. It is backed by rigorous research projects supported by the Australian Research Council in collaboration with several transmission and distribution companies. Many of the diagnostic techniques and tools developed in these projects have been applied by electricity utilities and would appeal to both researchers and practicing engineers. Important topics covered in this book include transformer insulation materials and their ageing behaviour, transformer condition monitoring techniques and detailed diagnostic techniques and their interpretation schemes. It also features a monitoring framework for smart transformers as well as a chapter on biodegradable oil.

  2. Feasibility study and technical proposal for seismic monitoring of tunnel boring machine in Olkiluoto

    Energy Technology Data Exchange (ETDEWEB)

    Saari, J.; Lakio, A. (AF-Consult Ltd, Vantaa (Finland))

    2009-01-15

    In Olkiluoto, Posiva Oy has operated a local seismic network since February 2002. The purpose of the microearthquake measurements at Olkiluoto is to improve understanding of the structure, behaviour and long term stability of the bedrock. The studies include both tectonic and excavation-induced microearthquakes. An additional task of monitoring is related to safeguarding of the ONKALO. The possibility to excavate an illegal access to the ONKALO, have been concerned when the safeguards are discussed. Therefore all recorded explosions in the Olkiluoto area and in the ONKALO are located. If a concentration of explosions is observed, the origin of that is found out. Also a concept of hidden illegal explosions, detonated at the same time as the real excavation blasts, has been examined. According to the experience gained in Olkiluoto, it can be concluded that, as long the seismic network is in operation and the results are analysed by a skilled person, it is practically impossible to do illegal excavation by blasts. In this report a possibility of seismic monitoring of illegal excavation done by tunnel boring machine (TBM) has been investigated. Characteristics of the seismic signal generated by the raise boring machine are described. According to this study, it can be concluded that the generated seismic signal can be detected and the source of the signal can be located. However, this task calls for different kind of monitoring system than that, which is currently used for monitoring microearthquakes and explosions. The presented technical proposal for seismic monitoring of TBM in Olkiluoto is capable to detect and locate TBM coming outside the ONKALO area about two months before it would reach the ONKALO. (orig.)

  3. Feasibility study and technical proposal for seismic monitoring of tunnel boring machine in Olkiluoto

    International Nuclear Information System (INIS)

    Saari, J.; Lakio, A.

    2009-01-01

    In Olkiluoto, Posiva Oy has operated a local seismic network since February 2002. The purpose of the microearthquake measurements at Olkiluoto is to improve understanding of the structure, behaviour and long term stability of the bedrock. The studies include both tectonic and excavation-induced microearthquakes. An additional task of monitoring is related to safeguarding of the ONKALO. The possibility to excavate an illegal access to the ONKALO, have been concerned when the safeguards are discussed. Therefore all recorded explosions in the Olkiluoto area and in the ONKALO are located. If a concentration of explosions is observed, the origin of that is found out. Also a concept of hidden illegal explosions, detonated at the same time as the real excavation blasts, has been examined. According to the experience gained in Olkiluoto, it can be concluded that, as long the seismic network is in operation and the results are analysed by a skilled person, it is practically impossible to do illegal excavation by blasts. In this report a possibility of seismic monitoring of illegal excavation done by tunnel boring machine (TBM) has been investigated. Characteristics of the seismic signal generated by the raise boring machine are described. According to this study, it can be concluded that the generated seismic signal can be detected and the source of the signal can be located. However, this task calls for different kind of monitoring system than that, which is currently used for monitoring microearthquakes and explosions. The presented technical proposal for seismic monitoring of TBM in Olkiluoto is capable to detect and locate TBM coming outside the ONKALO area about two months before it would reach the ONKALO. (orig.)

  4. GEOGLAM Crop Monitor Assessment Tool: Developing Monthly Crop Condition Assessments

    Science.gov (United States)

    McGaughey, K.; Becker Reshef, I.; Barker, B.; Humber, M. L.; Nordling, J.; Justice, C. O.; Deshayes, M.

    2014-12-01

    The Group on Earth Observations (GEO) developed the Global Agricultural Monitoring initiative (GEOGLAM) to improve existing agricultural information through a network of international partnerships, data sharing, and operational research. This presentation will discuss the Crop Monitor component of GEOGLAM, which provides the Agricultural Market Information System (AMIS) with an international, multi-source, and transparent consensus assessment of crop growing conditions, status, and agro-climatic conditions likely to impact global production. This activity covers the four primary crop types (wheat, maize, rice, and soybean) within the main agricultural producing regions of the AMIS countries. These assessments have been produced operationally since September 2013 and are published in the AMIS Market Monitor Bulletin. The Crop Monitor reports provide cartographic and textual summaries of crop conditions as of the 28th of each month, according to crop type. This presentation will focus on the building of international networks, data collection, and data dissemination.

  5. Beam conditions monitors at CMS and LHC using diamond sensors

    Energy Technology Data Exchange (ETDEWEB)

    Hempel, Maria; Lohmann, Wolfgang [Desy-Zeuthen, Platanenallee 6, 15738 Zeuthen (Germany); Brandenburgische Technische Universitaet Cottbus, Konrad-Wachsmann-Allee 1, 03046 Cottbus (Germany); Castro-Carballo, Maria-Elena; Lange, Wolfgang; Novgorodova, Olga [Desy-Zeuthen, Platanenallee 6, 15738 Zeuthen (Germany); Walsh, Roberval [Desy-Hamburg, Notkestrasse 85, 22607 Hamburg (Germany)

    2012-07-01

    The Fast Beam Conditions Monitor (BCM1F) is a particle detector based on diamonds. Eight modules comprising a single crystal diamond, front-end electronics and an optical link are installed on both sides of the interaction point inside the tracker of the CMS detector. The back-end uses ADCs, TDCs and scalers to measure the amplitudes, arrival time and rates of beam-halo particles and collision products. These data are used to protect the inner tracker from adverse beam conditions, perform a fast monitoring of the luminosity and e.g. beam-gas interactions. Recently two additional BCM1F modules have been installed at other positions of the LHC to supplement the beam-loss monitors by a flux measurement with nanosecond time resolution. In the talk essential parameters of the system are presented and examples of beam conditions monitoring are reported.

  6. Smoke composition and predicting relationships for international commercial cigarettes smoked with three machine-smoking conditions.

    Science.gov (United States)

    Counts, M E; Morton, M J; Laffoon, S W; Cox, R H; Lipowicz, P J

    2005-04-01

    The study objectives were to determine the effects of smoking machine puffing parameters on mainstream smoke composition and to express those effects as predicting relationships. Forty-eight commercial Philip Morris USA and Philip Morris International cigarettes from international markets and the 1R4F reference cigarette were machine-smoked using smoking conditions defined by the International Organization of Standardization (ISO), the Massachusetts Department of Public Health (MDPH), and Health Canada (HC). Cigarette tobacco fillers were analyzed for nitrate, nicotine, tobacco-specific nitrosamines (TSNA), and ammonia. Mainstream yields for tar and 44 individual smoke constituents and "smoke pH" were determined. Cigarette constituent yields typically increased in the order ISOrelationships were developed between ISO tar and ISO, MDPH, and HC constituent yields and between MDPH tar and HC tar and respective smoking condition yields. MDPH and HC constituent yields could be predicted with similar reliability using ISO tar or the corresponding smoking-condition tar. The reliability of the relationships varied from strong to weak, depending on particular constituents. Weak predicting relationships for nitrogen oxides and TSNA's, for example, were improved with inclusion of tobacco filler composition factors. "Smoke pH" was similar for all cigarettes at any one smoking condition, and overall marginally lower at HC conditions than at ISO or MDPH conditions.

  7. Optimization of Coolant Technique Conditions for Machining A319 Aluminium Alloy Using Response Surface Method (RSM)

    Science.gov (United States)

    Zainal Ariffin, S.; Razlan, A.; Ali, M. Mohd; Efendee, A. M.; Rahman, M. M.

    2018-03-01

    Background/Objectives: The paper discusses about the optimum cutting parameters with coolant techniques condition (1.0 mm nozzle orifice, wet and dry) to optimize surface roughness, temperature and tool wear in the machining process based on the selected setting parameters. The selected cutting parameters for this study were the cutting speed, feed rate, depth of cut and coolant techniques condition. Methods/Statistical Analysis Experiments were conducted and investigated based on Design of Experiment (DOE) with Response Surface Method. The research of the aggressive machining process on aluminum alloy (A319) for automotive applications is an effort to understand the machining concept, which widely used in a variety of manufacturing industries especially in the automotive industry. Findings: The results show that the dominant failure mode is the surface roughness, temperature and tool wear when using 1.0 mm nozzle orifice, increases during machining and also can be alternative minimize built up edge of the A319. The exploration for surface roughness, productivity and the optimization of cutting speed in the technical and commercial aspects of the manufacturing processes of A319 are discussed in automotive components industries for further work Applications/Improvements: The research result also beneficial in minimizing the costs incurred and improving productivity of manufacturing firms. According to the mathematical model and equations, generated by CCD based RSM, experiments were performed and cutting coolant condition technique using size nozzle can reduces tool wear, surface roughness and temperature was obtained. Results have been analyzed and optimization has been carried out for selecting cutting parameters, shows that the effectiveness and efficiency of the system can be identified and helps to solve potential problems.

  8. Real-time depth monitoring and control of laser machining through scanning beam delivery system

    International Nuclear Information System (INIS)

    Ji, Yang; Grindal, Alexander W; Fraser, James M; Webster, Paul J L

    2015-01-01

    Scanning optics enable many laser applications in manufacturing because their low inertia allows rapid movement of the process beam across the sample. We describe our method of inline coherent imaging for real-time (up to 230 kHz) micron-scale (7–8 µm axial resolution) tracking and control of laser machining depth through a scanning galvo-telecentric beam delivery system. For 1 cm trench etching in stainless steel, we collect high speed intrapulse and interpulse morphology which is useful for further understanding underlying mechanisms or comparison with numerical models. We also collect overall sweep-to-sweep depth penetration which can be used for feedback depth control. For trench etching in silicon, we show the relationship of etch rate with average power and scan speed by computer processing of depth information without destructive sample post-processing. We also achieve three-dimensional infrared continuous wave (modulated) laser machining of a 3.96 × 3.96 × 0.5 mm 3 (length × width × maximum depth) pattern on steel with depth feedback. To the best of our knowledge, this is the first successful demonstration of direct real-time depth monitoring and control of laser machining with scanning optics. (paper)

  9. Explosion Monitoring with Machine Learning: A LSTM Approach to Seismic Event Discrimination

    Science.gov (United States)

    Magana-Zook, S. A.; Ruppert, S. D.

    2017-12-01

    The streams of seismic data that analysts look at to discriminate natural from man- made events will soon grow from gigabytes of data per day to exponentially larger rates. This is an interesting problem as the requirement for real-time answers to questions of non-proliferation will remain the same, and the analyst pool cannot grow as fast as the data volume and velocity will. Machine learning is a tool that can solve the problem of seismic explosion monitoring at scale. Using machine learning, and Long Short-term Memory (LSTM) models in particular, analysts can become more efficient by focusing their attention on signals of interest. From a global dataset of earthquake and explosion events, a model was trained to recognize the different classes of events, given their spectrograms. Optimal recurrent node count and training iterations were found, and cross validation was performed to evaluate model performance. A 10-fold mean accuracy of 96.92% was achieved on a balanced dataset of 30,002 instances. Given that the model is 446.52 MB it can be used to simultaneously characterize all incoming signals by researchers looking at events in isolation on desktop machines, as well as at scale on all of the nodes of a real-time streaming platform. LLNL-ABS-735911

  10. A data-based technique for monitoring of wound rotor induction machines: A simulation study

    KAUST Repository

    Harrou, Fouzi

    2016-05-09

    Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM). The proposed fault detection approach is based on the use of principal components analysis (PCA). However, conventional PCA-based detection indices, such as the T2T2 and the Q statistics, are not well suited to detect small faults because these indices only use information from the most recent available samples. Detection of small faults is one of the most crucial and challenging tasks in the area of fault detection and diagnosis. In this paper, a new statistical system monitoring strategy is proposed for detecting changes resulting from small shifts in several variables associated with WRIM. The proposed approach combines modeling using PCA modeling with the exponentially weighted moving average (EWMA) control scheme. In the proposed approach, EWMA control scheme is applied on the ignored principal components to detect the presence of faults. The performance of the proposed method is compared with those of the traditional PCA-based fault detection indices. The simulation results clearly show the effectiveness of the proposed method over the conventional ones, especially in the presence of faults with small magnitudes.

  11. Computer-based diagnostic monitoring to enhance the human-machine interface of complex processes

    International Nuclear Information System (INIS)

    Kim, I.S.

    1992-02-01

    There is a growing interest in introducing an automated, on-line, diagnostic monitoring function into the human-machine interfaces (HMIs) or control rooms of complex process plants. The design of such a system should be properly integrated with other HMI systems in the control room, such as the alarms system or the Safety Parameter Display System (SPDS). This paper provides a conceptual foundation for the development of a Plant-wide Diagnostic Monitoring System (PDMS), along with functional requirements for the system and other advanced HMI systems. Insights are presented into the design of an efficient and robust PDMS, which were gained from a critical review of various methodologies developed in the nuclear power industry, the chemical process industry, and the space technological community

  12. A DSP-Based Beam Current Monitoring System for Machine Protection Using Adaptive Filtering

    International Nuclear Information System (INIS)

    J. Musson; H. Dong; R. Flood; C. Hovater; J. Hereford

    2001-01-01

    The CEBAF accelerator at Jefferson Lab is currently using an analog beam current monitoring (BCM) system for its machine protection system (MPS), which has a loss accuracy of 2 micro-amps. Recent burn-through simulations predict catastrophic beam line component failures below 1 micro-amp of loss, resulting in a blind spot for the MPS. Revised MPS requirements target an ultimate beam loss accuracy of 250 nA. A new beam current monitoring system has been developed which utilizes modern digital receiver technology and digital signal processing concepts. The receiver employs a direct-digital down converter integrated circuit, mated with a Jefferson Lab digital signal processor VME card. Adaptive filtering is used to take advantage of current-dependent burn-through rates. Benefits of such a system include elimination of DC offsets, generic algorithm development, extensive filter options, and interfaces to UNIX-based control systems

  13. Pump failure leads to alternative vertical pump condition monitoring technique

    International Nuclear Information System (INIS)

    DeVilliers, Adriaan; Glandon, Kevin

    2011-01-01

    Condition monitoring and detecting early signs of potential failure mechanisms present particular problems in vertical pumps. Most often, the majority of the pump assembly is not readily accessible for visual or audible inspection or conventional vibration monitoring techniques using accelerometers and/or proximity sensors. The root cause failure analysis of a 2-stage vertical centrifugal service-water pump at a nuclear power generating facility in the USA is presented, highlighting this long standing challenge in condition monitoring of vertical pumps. This paper will summarize the major findings of the root cause analysis (RCA), highlight the limitations of traditional monitoring techniques, and present an expanded application of motor current monitoring as a means to gain insight into the mechanical performance and condition of a pump. The 'real-world' example of failure, monitoring and correlation of the monitoring technique to a detailed pump disassembly inspection is also presented. This paper will explain some of the reasons behind well known design principles requiring natural frequency separation from known forcing frequencies, as well as explore an unexpected submerged brittle fracture failure mechanism, and how such issues may be avoided. (author)

  14. Effects of machining conditions on the specific cutting energy of carbon fibre reinforced polymer composites

    Science.gov (United States)

    Azmi, A. I.; Syahmi, A. Z.; Naquib, M.; Lih, T. C.; Mansor, A. F.; Khalil, A. N. M.

    2017-10-01

    This article presents an approach to evaluate the effects of different machining conditions on the specific cutting energy of carbon fibre reinforced polymer composites (CFRP). Although research works in the machinability of CFRP composites have been very substantial, the present literature rarely discussed the topic of energy consumption and the specific cutting energy. A series of turning experiments were carried out on two different CFRP composites in order to determine the power and specific energy constants and eventually evaluate their effects due to the changes in machining conditions. A good agreement between the power and material removal rate using a simple linear relationship. Further analyses revealed that a power law function is best to describe the effect of feed rate on the changes in the specific cutting energy. At lower feed rate, the specific cutting energy increases exponentially due to the nature of finishing operation, whereas at higher feed rate, the changes in specific cutting energy is minimal due to the nature of roughing operation.

  15. Demand Forecasting at Low Aggregation Levels using Factored Conditional Restricted Boltzmann Machine

    DEFF Research Database (Denmark)

    Mocanu, Elena; Nguyen, Phuong H.; Gibescu, Madeleine

    2016-01-01

    electric power consumption, local price and meteorological data collected from 1900 customers. The households are equipped with local generation and smart appliances capable of responding to realtime pricing signals. The results show that for the short-term (5 minute to 1 day ahead) prediction problems......The electrical demand forecasting problem can be regarded as a nonlinear time series prediction problem depending on many complex factors since it is required at various aggregation levels and at high temporal resolution. To solve this challenging problem, various time series and machine learning...... developed deep learning model for time series prediction, namely Factored Conditional Restricted Boltzmann Machine (FCRBM), and extend it for electrical demand forecasting. The assessment is made on the EcoGrid dataset, originating from the Bornholm island experiment in Denmark, consisting of aggregated...

  16. Monitoring of laser material processing using machine integrated low-coherence interferometry

    Science.gov (United States)

    Kunze, Rouwen; König, Niels; Schmitt, Robert

    2017-06-01

    Laser material processing has become an indispensable tool in modern production. With the availability of high power pico- and femtosecond laser sources, laser material processing is advancing into applications, which demand for highest accuracies such as laser micro milling or laser drilling. In order to enable narrow tolerance windows, a closedloop monitoring of the geometrical properties of the processed work piece is essential for achieving a robust manufacturing process. Low coherence interferometry (LCI) is a high-precision measuring principle well-known from surface metrology. In recent years, we demonstrated successful integrations of LCI into several different laser material processing methods. Within this paper, we give an overview about the different machine integration strategies, that always aim at a complete and ideally telecentric integration of the measurement device into the existing beam path of the processing laser. Thus, highly accurate depth measurements within machine coordinates and a subsequent process control and quality assurance are possible. First products using this principle have already found its way to the market, which underlines the potential of this technology for the monitoring of laser material processing.

  17. Muscular condition monitoring system using fiber bragg grating sensors

    International Nuclear Information System (INIS)

    Kim, Heon Young; Lee, Jin Hyuk; Kim, Dae Hyun

    2014-01-01

    Fiber optic sensors (FOS) have advantages such as electromagnetic interference (EMI) immunity, corrosion resistance and multiplexing capability. For these reasons, they are widely used in various condition monitoring systems (CMS). This study investigated a muscular condition monitoring system using fiber optic sensors (FOS). Generally, sensors for monitoring the condition of the human body are based on electro-magnetic devices. However, such an electrical system has several weaknesses, including the potential for electro-magnetic interference and distortion. Fiber Bragg grating (FBG) sensors overcome these weaknesses, along with simplifying the devices and increasing user convenience. To measure the level of muscle contraction and relaxation, which indicates the muscle condition, a belt-shaped FBG sensor module that makes it possible to monitor the movement of muscles in the radial and circumferential directions was fabricated in this study. In addition, a uniaxial tensile test was carried out in order to evaluate the applicability of this FBG sensor module. Based on the experimental results, a relationship was observed between the tensile stress and Bragg wavelength of the FBG sensors, which revealed the possibility of fabricating a muscular condition monitoring system based on FBG sensors.

  18. Muscular condition monitoring system using fiber bragg grating sensors

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Heon Young; Lee, Jin Hyuk; Kim, Dae Hyun [Seoul National University of Technology, Seoul (Korea, Republic of)

    2014-10-15

    Fiber optic sensors (FOS) have advantages such as electromagnetic interference (EMI) immunity, corrosion resistance and multiplexing capability. For these reasons, they are widely used in various condition monitoring systems (CMS). This study investigated a muscular condition monitoring system using fiber optic sensors (FOS). Generally, sensors for monitoring the condition of the human body are based on electro-magnetic devices. However, such an electrical system has several weaknesses, including the potential for electro-magnetic interference and distortion. Fiber Bragg grating (FBG) sensors overcome these weaknesses, along with simplifying the devices and increasing user convenience. To measure the level of muscle contraction and relaxation, which indicates the muscle condition, a belt-shaped FBG sensor module that makes it possible to monitor the movement of muscles in the radial and circumferential directions was fabricated in this study. In addition, a uniaxial tensile test was carried out in order to evaluate the applicability of this FBG sensor module. Based on the experimental results, a relationship was observed between the tensile stress and Bragg wavelength of the FBG sensors, which revealed the possibility of fabricating a muscular condition monitoring system based on FBG sensors.

  19. Introducing passive acoustic filter in acoustic based condition monitoring: Motor bike piston-bore fault identification

    Science.gov (United States)

    Jena, D. P.; Panigrahi, S. N.

    2016-03-01

    Requirement of designing a sophisticated digital band-pass filter in acoustic based condition monitoring has been eliminated by introducing a passive acoustic filter in the present work. So far, no one has attempted to explore the possibility of implementing passive acoustic filters in acoustic based condition monitoring as a pre-conditioner. In order to enhance the acoustic based condition monitoring, a passive acoustic band-pass filter has been designed and deployed. Towards achieving an efficient band-pass acoustic filter, a generalized design methodology has been proposed to design and optimize the desired acoustic filter using multiple filter components in series. An appropriate objective function has been identified for genetic algorithm (GA) based optimization technique with multiple design constraints. In addition, the sturdiness of the proposed method has been demonstrated in designing a band-pass filter by using an n-branch Quincke tube, a high pass filter and multiple Helmholtz resonators. The performance of the designed acoustic band-pass filter has been shown by investigating the piston-bore defect of a motor-bike using engine noise signature. On the introducing a passive acoustic filter in acoustic based condition monitoring reveals the enhancement in machine learning based fault identification practice significantly. This is also a first attempt of its own kind.

  20. Integrated reliability condition monitoring and maintenance of equipment

    CERN Document Server

    Osarenren, John

    2015-01-01

    Consider a Viable and Cost-Effective Platform for the Industries of the Future (IOF) Benefit from improved safety, performance, and product deliveries to your customers. Achieve a higher rate of equipment availability, performance, product quality, and reliability. Integrated Reliability: Condition Monitoring and Maintenance of Equipment incorporates reliable engineering and mathematical modeling to help you move toward sustainable development in reliability condition monitoring and maintenance. This text introduces a cost-effective integrated reliability growth monitor, integrated reliability degradation monitor, technological inheritance coefficient sensors, and a maintenance tool that supplies real-time information for predicting and preventing potential failures of manufacturing processes and equipment. The author highlights five key elements that are essential to any improvement program: improving overall equipment and part effectiveness, quality, and reliability; improving process performance with maint...

  1. Condition Monitoring Of Operating Pipelines With Operational Modal Analysis Application

    OpenAIRE

    Mironov Aleksey; Doronkin Pavel; Priklonsky Aleksander; Kabashkin Igor

    2015-01-01

    In the petroleum, natural gas and petrochemical industries, great attention is being paid to safety, reliability and maintainability of equipment. There are a number of technologies to monitor, control, and maintain gas, oil, water, and sewer pipelines. The paper focuses on operational modal analysis (OMA) application for condition monitoring of operating pipelines. Special focus is on the topicality of OMA for definition of the dynamic features of the pipeline (frequencies and mode shapes) i...

  2. Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control.

    Science.gov (United States)

    Adeleke, Jude Adekunle; Moodley, Deshendran; Rens, Gavin; Adewumi, Aderemi Oluyinka

    2017-04-09

    Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM 2 . 5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM 2 . 5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.

  3. Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control

    Directory of Open Access Journals (Sweden)

    Jude Adekunle Adeleke

    2017-04-01

    Full Text Available Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM 2 . 5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM 2 . 5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.

  4. Wireless pilot monitoring system for extreme race conditions.

    Science.gov (United States)

    Pino, Esteban J; Arias, Diego E; Aqueveque, Pablo; Melin, Pedro; Curtis, Dorothy W

    2012-01-01

    This paper presents the design and implementation of an assistive device to monitor car drivers under extreme conditions. In particular, this system is designed in preparation for the 2012 Atacama Solar Challenge to be held in the Chilean desert. Actual preliminary results show the feasibility of such a project including physiological and ambient sensors, real-time processing algorithms, wireless data transmission and a remote monitoring station. Implementation details and field results are shown along with a discussion of the main problems found in real-life telemetry monitoring.

  5. Condition Monitoring Of Operating Pipelines With Operational Modal Analysis Application

    Directory of Open Access Journals (Sweden)

    Mironov Aleksey

    2015-12-01

    Full Text Available In the petroleum, natural gas and petrochemical industries, great attention is being paid to safety, reliability and maintainability of equipment. There are a number of technologies to monitor, control, and maintain gas, oil, water, and sewer pipelines. The paper focuses on operational modal analysis (OMA application for condition monitoring of operating pipelines. Special focus is on the topicality of OMA for definition of the dynamic features of the pipeline (frequencies and mode shapes in operation. The research was conducted using two operating laboratory models imitated a part of the operating pipeline. The results of finite-element modeling, identification of pipe natural modes and its modification under the influence of virtual failure are discussed. The work considers the results of experimental research of dynamic behavior of the operating pipe models using one of OMA techniques and comparing dynamic properties with the modeled data. The study results demonstrate sensitivity of modal shape parameters to modification of operating pipeline technical state. Two strategies of pipeline repair – with continuously condition-based monitoring with proposed technology and without such monitoring, was discussed. Markov chain reliability models for each strategy were analyzed and reliability improvement factor for proposed technology of monitoring in compare with traditional one was evaluated. It is resumed about ability of operating pipeline condition monitoring by measuring dynamic deformations of the operating pipe and OMA techniques application for dynamic properties extraction.

  6. A data-based technique for monitoring of wound rotor induction machines: A simulation study

    KAUST Repository

    Harrou, Fouzi; Ramahaleomiarantsoa, Jacques F.; Nounou, Mohamed N.; Nounou, Hazem N.

    2016-01-01

    Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM

  7. Workshop on power plant cable condition monitoring: Proceedings

    International Nuclear Information System (INIS)

    Del Valle, L.

    1988-07-01

    A three-day workshop on cable condition monitoring was held in San Francisco on Fegruary 16--18, 1988. The workshop was cosponsored by the Nuclear Power, Electrical Systems, and Coal Combustion Systems Divisions of the Electric Power Research Institute. The primary objective of the workshop was to identify the state-of-the-art for cable condition monitoring. Twenty-five technical papers as well as EPRI research programs were presented at the technical sessions. Four working group sessions and one general session were held on each of two days. Each group session provided a forum for participants to exchange ideas and to discuss in more depth research for cable condition monitoring, existing and innovative testing technology, and utility and NRC needs for testing. Recommendations from the working groups were summarized and presented at the end of the workshop

  8. Wind Turbine Gearbox Oil Filtration and Condition Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Sheng, Shuangwen

    2015-10-25

    This is an invited presentation for a pre-conference workshop, titled advances and opportunities in lubrication: wind turbine, at the 2015 Society of Tribologists and Lubrication Engineers (STLE) Tribology Frontiers Conference held in Denver, CO. It gives a brief overview of wind turbine gearbox oil filtration and condition monitoring by highlighting typical industry practices and challenges. The presentation starts with an introduction by covering recent growth of global wind industry, reliability challenges, benefits of oil filtration and condition monitoring, and financial incentives to conduct wind operation and maintenance research, which includes gearbox oil filtration and condition monitoring work presented herein. Then, the presentation moves on to oil filtration by stressing the benefits of filtration, discussing typical main- and offline-loop practices, highlighting important factors considered when specifying a filtration system, and illustrating real-world application challenges through a cold-start example. In the next section on oil condition monitoring, a discussion on oil sample analysis, oil debris monitoring, oil cleanliness measurements and filter analysis is given based on testing results mostly obtained by and at NREL, and by pointing out a few challenges with oil sample analysis. The presentation concludes with a brief touch on future research and development (R and D) opportunities. It is hoping that the information presented can inform the STLE community to start or redirect their R and D work to help the wind industry advance.

  9. Disaggregation of remotely sensed soil moisture under all sky condition using machine learning approach in Northeast Asia

    Science.gov (United States)

    Kim, S.; Kim, H.; Choi, M.; Kim, K.

    2016-12-01

    Estimating spatiotemporal variation of soil moisture is crucial to hydrological applications such as flood, drought, and near real-time climate forecasting. Recent advances in space-based passive microwave measurements allow the frequent monitoring of the surface soil moisture at a global scale and downscaling approaches have been applied to improve the spatial resolution of passive microwave products available at local scale applications. However, most downscaling methods using optical and thermal dataset, are valid only in cloud-free conditions; thus renewed downscaling method under all sky condition is necessary for the establishment of spatiotemporal continuity of datasets at fine resolution. In present study Support Vector Machine (SVM) technique was utilized to downscale a satellite-based soil moisture retrievals. The 0.1 and 0.25-degree resolution of daily Land Parameter Retrieval Model (LPRM) L3 soil moisture datasets from Advanced Microwave Scanning Radiometer 2 (AMSR2) were disaggregated over Northeast Asia in 2015. Optically derived estimates of surface temperature (LST), normalized difference vegetation index (NDVI), and its cloud products were obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) for the purpose of downscaling soil moisture in finer resolution under all sky condition. Furthermore, a comparison analysis between in situ and downscaled soil moisture products was also conducted for quantitatively assessing its accuracy. Results showed that downscaled soil moisture under all sky condition not only preserves the quality of AMSR2 LPRM soil moisture at 1km resolution, but also attains higher spatial data coverage. From this research we expect that time continuous monitoring of soil moisture at fine scale regardless of weather conditions would be available.

  10. Diagnostic measurements on the great machines conditions of lignite surface mines

    Energy Technology Data Exchange (ETDEWEB)

    Helebrant, F.; Jurman, J.; Fries, J. [Technical University of Ostrava, Ostrava-Poruba (Czech Republic)

    2005-07-01

    An analysis of the diagnosis of loading and service dependability of a rail-mounted excavator used in surface lignite mining is described. Wheel power vibrations in electric motor bearings and electric motor input bearings to the gearbox were measured in situ, in horizontal, vertical, and axial directions. The data were analyzed using a mathematical relationship. The results are presented in a loading diagram that shows the deterioration and the acceptable lower bound of machine conditions over time. Work is continuing. 5 refs., 1 fig.

  11. Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring

    Directory of Open Access Journals (Sweden)

    Salman Ali

    2015-03-01

    Full Text Available The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs. CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

  12. Network challenges for cyber physical systems with tiny wireless devices: a case study on reliable pipeline condition monitoring.

    Science.gov (United States)

    Ali, Salman; Qaisar, Saad Bin; Saeed, Husnain; Khan, Muhammad Farhan; Naeem, Muhammad; Anpalagan, Alagan

    2015-03-25

    The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

  13. Monitoring and analysis of an absorption air-conditioning system

    Energy Technology Data Exchange (ETDEWEB)

    Perez de Vinaspre, M.; Bourouis, M.; Coronas, A. [Centro de Innovacion Tecnologica en Revalorizacion Energetica y Refrigeracion, Tarragona (Spain); Garcia, A.; Soto, V.; Pinazo, J.M. [E.T.S. Ingenieros Industriales, Valencia (Spain)

    2004-09-01

    In the last few years, high-energy consumption due to air-conditioning has led to a growing interest in the efficient use of energy in buildings. Although simulation programs have always been the main tools for analyzing energy in buildings, the reliability of their results is often compromised by a lack of certainty to reflect real conditions. The aim of this work is to monitorize and analyze the thermal behavior of an absorption-based air-conditioning installation of a university building in Tarragona, Spain. The existing monitoring system of the installation has been improved by implementing additional sensors and flow meters. The data has been stored during summer 2002 and used to assess the energy balance of the air-conditioning installation and the operational regime of the absorption chiller. [Author].

  14. Four-year monitoring of foodborne pathogens in raw milk sold by vending machines in Italy.

    Science.gov (United States)

    Giacometti, Federica; Bonilauri, Paolo; Serraino, Andrea; Peli, Angelo; Amatiste, Simonetta; Arrigoni, Norma; Bianchi, Manila; Bilei, Stefano; Cascone, Giuseppe; Comin, Damiano; Daminelli, Paolo; Decastelli, Lucia; Fustini, Mattia; Mion, Renzo; Petruzzelli, Annalisa; Rosmini, Roberto; Rugna, Gianluca; Tamba, Marco; Tonucci, Franco; Bolzoni, Giuseppe

    2013-11-01

    Prevalence data were collected from official microbiological records monitoring four selected foodborne pathogens (Salmonella, Listeria monocytogenes, Escherichia coli O157:H7, and Campylobacter jejuni) in raw milk sold by self-service vending machines in seven Italian regions (60,907 samples from 1,239 vending machines) from 2008 to 2011. Data from samples analyzed by both culture-based and real-time PCR methods were collected in one region. One hundred raw milk consumers in four regions were interviewed while purchasing raw milk from vending machines. One hundred seventy-eight of 60,907 samples were positive for one of the four foodborne pathogens investigated: 18 samples were positive for Salmonella, 83 for L. monocytogenes, 24 for E. coli O157:H7, and 53 for C. jejuni in the seven regions investigated. No significant differences in prevalence were found among regions, but a significant increase in C. jejuni prevalence was observed over the years of the study. A comparison of the two analysis methods revealed that real-time PCR was 2.71 to 9.40 times more sensitive than the culture-based method. Data on consumer habits revealed that some behaviors may enhance the risk of infection linked to raw milk consumption: 37% of consumers did not boil milk before consumption, 93% never used an insulated bag to transport raw milk home, and raw milk was consumed by children younger than 5 years of age. These results emphasize that end-product controls alone are not sufficient to guarantee an adequate level of consumer protection. The beta distribution of positive samples in this study and the data on raw milk consumer habits will be useful for the development of a national quantitative risk assessment of Salmonella, L. monocytogenes, E. coli O157, and C. jejuni infection associated with raw milk consumption.

  15. Vibro-acoustic condition monitoring of Internal Combustion Engines: A critical review of existing techniques

    Science.gov (United States)

    Delvecchio, S.; Bonfiglio, P.; Pompoli, F.

    2018-01-01

    This paper deals with the state-of-the-art strategies and techniques based on vibro-acoustic signals that can monitor and diagnose malfunctions in Internal Combustion Engines (ICEs) under both test bench and vehicle operating conditions. Over recent years, several authors have summarized what is known in critical reviews mainly focused on reciprocating machines in general or on specific signal processing techniques: no attempts to deal with IC engine condition monitoring have been made. This paper first gives a brief summary of the generation of sound and vibration in ICEs in order to place further discussion on fault vibro-acoustic diagnosis in context. An overview of the monitoring and diagnostic techniques described in literature using both vibration and acoustic signals is also provided. Different faulty conditions are described which affect combustion, mechanics and the aerodynamics of ICEs. The importance of measuring acoustic signals, as opposed to vibration signals, is due since the former seem to be more suitable for implementation on on-board monitoring systems in view of their non-intrusive behaviour, capability in simultaneously capturing signatures from several mechanical components and because of the possibility of detecting faults affecting airborne transmission paths. In view of the recent needs of the industry to (-) optimize component structural durability adopting long-life cycles, (-) verify the engine final status at the end of the assembly line and (-) reduce the maintenance costs monitoring the ICE life during vehicle operations, monitoring and diagnosing system requests are continuously growing up. The present review can be considered a useful guideline for test engineers in understanding which types of fault can be diagnosed by using vibro-acoustic signals in sufficient time in both test bench and operating conditions and which transducer and signal processing technique (of which the essential background theory is here reported) could be

  16. Development of new plant monitoring and control system with advanced man-machine interfaces NUCAMM-80

    International Nuclear Information System (INIS)

    Sato, Hideyuki; Joge, Toshio; Miyake, Masao; Kishi, Shoichi

    1981-01-01

    BWR type nuclear power stations are the typical plants adopting central monitoring system in view of the size of the scale of system and the prevention of radiation exposure. Central control boards became large as much informations and many operating tools are concentrated on them. Recently, the unit capacity has increased, and the safety has been strengthened, therefore more improvement of the man-machine interface is required concerning the monitoring of plant operation. Hitachi Ltd. developed the central monitoring and control system for nuclear power stations ''NUCAMM-80'', concentrating related fundamental techniques such as the collection of plant informations, the expansion of automatic operation, the ergonomic re-evaluation of the arrangement of panels and subsystems, and the effective use of functional hardwares such as controlling computers and cathode ray tubes, for the purposes of improving the reliability of plant operation and the rate of operation, the reduction of the burden of operators and drastic labor saving. The fundamental policy of the development, the construction of the system, panel layout and the collection of informations, the development of the system for plant automation, the development of plant diagnosis and prevention systems, computer system and the merits of this system are described. (Kako, I.)

  17. Human monitoring and decision-making in man/machine systems

    International Nuclear Information System (INIS)

    Johannsen, G.

    1979-01-01

    Monitoring and decision-making together are very well characterizing the role of the human operator in highly automated systems. In this report, the analysis of human monitoring and decision-making behavior as well as its modeling are described. The goal is to present a survey. 'Classic' and optimal control theoretic monitoring models are dealt with. The relationship between attention allocation and eye movements is discussed. As an example for applications, the evaluation of predictor displays by means of the optimal control model is explained. Fault detection in continuous signals and decision-making behavior of the human operator in fault diagnosis during different operation and maintenance situations are illustrated. The computer-aided decision-making is considered as a queueing problem. It is shown to what extent computer-aiding may be based on the state of human activity as measured by psychophysiological quantities. Finally, management information systems for different application areas are mentioned. As an appendix, the report includes an English-written paper in which the possibilities of mathematical modeling of human behavior in complex man-machine systems are critically assessed. (orig.) 891 GL/orig. 892 MKO [de

  18. Nonlinear Cointegration Approach for Condition Monitoring of Wind Turbines

    Directory of Open Access Journals (Sweden)

    Konrad Zolna

    2015-01-01

    Full Text Available Monitoring of trends and removal of undesired trends from operational/process parameters in wind turbines is important for their condition monitoring. This paper presents the homoscedastic nonlinear cointegration for the solution to this problem. The cointegration approach used leads to stable variances in cointegration residuals. The adapted Breusch-Pagan test procedure is developed to test for the presence of heteroscedasticity in cointegration residuals obtained from the nonlinear cointegration analysis. Examples using three different time series data sets—that is, one with a nonlinear quadratic deterministic trend, another with a nonlinear exponential deterministic trend, and experimental data from a wind turbine drivetrain—are used to illustrate the method and demonstrate possible practical applications. The results show that the proposed approach can be used for effective removal of nonlinear trends form various types of data, allowing for possible condition monitoring applications.

  19. Three State-of-the-Art Methods for Condition Monitoring

    NARCIS (Netherlands)

    Grimmelius, H.T.; Meiler, P.P.; Maas, H.L.M.M.; Bonnier, B.; Grevink, J.S.; Kuilenburg, R.F. van

    1999-01-01

    This paper describes and compares three different state-of-the-art condition monitoring techniques: first principles, feature extraction, and neural networks. The focus of the paper is on the application of the techniques, not on the underlying theory. Each technique is described briefly and is

  20. Groundwater detection monitoring system design under conditions of uncertainty

    NARCIS (Netherlands)

    Yenigül, N.B.

    2006-01-01

    Landfills represent a wide-spread and significant threat to groundwater quality. In this thesis a methodology was developed for the design of optimal groundwater moni-toring system design at landfill sites under conditions of uncertainty. First a decision analysis approach was presented for optimal

  1. Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning

    Directory of Open Access Journals (Sweden)

    Jongryun Roh

    2018-01-01

    Full Text Available Sitting posture monitoring systems (SPMSs help assess the posture of a seated person in real-time and improve sitting posture. To date, SPMS studies reported have required many sensors mounted on the backrest plate and seat plate of a chair. The present study, therefore, developed a system that measures a total of six sitting postures including the posture that applied a load to the backrest plate, with four load cells mounted only on the seat plate. Various machine learning algorithms were applied to the body weight ratio measured by the developed SPMS to identify the method that most accurately classified the actual sitting posture of the seated person. After classifying the sitting postures using several classifiers, average and maximum classification rates of 97.20% and 97.94%, respectively, were obtained from nine subjects with a support vector machine using the radial basis function kernel; the results obtained by this classifier showed a statistically significant difference from the results of multiple classifications using other classifiers. The proposed SPMS was able to classify six sitting postures including the posture with loading on the backrest and showed the possibility of classifying the sitting posture even though the number of sensors is reduced.

  2. Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.

    Science.gov (United States)

    Zhao, Rui; Yan, Ruqiang; Wang, Jinjiang; Mao, Kezhi

    2017-01-30

    In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.

  3. Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

    Directory of Open Access Journals (Sweden)

    Rui Zhao

    2017-01-01

    Full Text Available In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.

  4. Condition monitoring and maintenance of nuclear power plant concrete structures

    International Nuclear Information System (INIS)

    Orr, R.; Prasad, N.

    1988-01-01

    Nuclear power plant concrete structures are potentially subject to deterioration due to several environmental conditions, including weather exposure, ground water exposure, and sustained high temperature and radiation levels. The nuclear power plant are generally licensed for a term of 40 years. In order to maximize the return from the existing plants, feasibility studies are in progress for continued operation of many of these plants beyond the original licensed life span. This paper describes a study that was performed with an objective to define appropriate condition monitoring and maintenance procedures. A timely implementation of a condition monitoring and maintenance program would provide a valuable database and would provide justification for extension of the plant's design life. The study included concrete structures such as the containment buildings, interior structures, basemats, intake structures and cooling towers. Age-related deterioration at several operating power plants was surveyed and the potential degradation mechanisms have been identified

  5. New Electric Online Oil Condition Monitoring Sensor – an Innovation in Early Failure Detection of Industrial Gears

    Directory of Open Access Journals (Sweden)

    Manfred Mauntz

    2013-02-01

    Full Text Available A new online diagnostics system for the continuous condition monitoring of lubricating oils in industrial gearboxes is presented. Characteristic features of emerging component damage, such as wear, contamination or chemical aging, are identified in an early stage. The OilQSens® sensor effectively controls the proper operation conditions of bearings and cogwheels in gears. Also, the condition of insulating oils in transformers can be monitored. The online diagnostics system measures components of the specific complex impedance of oils. For instance, metal abrasion due to wear debris, broken oil molecules, forming acids or oil soaps result in an increase of the electrical conductivity, which directly correlates with the degree of contamination in the oil. The dielectrical properties of the oils are particularly determined by the water content that becomes accessible via an additional accurate measurement of the dielectric constant. For additivated oils, statements on the degradation of additives can also be derived from changes in the dielectric constant. For an efficient machine utilization and targeted damage prevention, the new OilQSens® online condition monitoring sensor system allows for timely preventative maintenance on demand rather than in rigid inspection intervals. The determination of impurities or reduction in the quality of the oil and the quasi continuous evaluation of wear and chemical aging follow the holistic approach of a real-time monitoring of a change in the condition of the oil-machine system. Once the oil condition monitoring sensors are installed on the plants, the measuring data can be displayed and evaluated elsewhere. The measuring signals are transmitted to a web-based condition monitoring system via LAN, WLAN or serial interfaces of the sensor system. Monitoring of the damage mechanisms during proper operation below the tolerance limits of the components enables specific preventive maintenance independent of rigid

  6. Integrated online condition monitoring system for nuclear power plants

    International Nuclear Information System (INIS)

    Hashemian, Hashem M.

    2010-01-01

    Online condition monitoring or online monitoring (OLM) uses data acquired while a nuclear power is operating to continuously assess the health of the plant's sensors, processes, and equipment; to measure the dynamic performance of the plant's process instrumentation; to verify in-situ the calibration of the process instrumentation; to detect blockages, voids, and leaks in the pressure sensing lines; to identify core flow anomalies; to extend the life of neutron detectors and other sensors; and to measure the vibration of reactor internals. Both the steady-state or DC output of plant sensors and their AC signal or noise output can be used to assess sensor health, depending on whether the application is monitoring a rapidly changing (e.g., core barrel motion) or a slowly changing (e.g., sensor calibration) process. The author has designed, developed, installed, and tested OLM systems (comprised of software/hardware-based data acquisition and data processing modules) that integrate low-frequency (1 mHz to 1 Hz) techniques such as RTD cross-calibration, pressure transmitter calibration monitoring, and equipment condition monitoring and high-frequency (1 Hz to 1 kHz) techniques such as the noise analysis technique. The author has demonstrated the noise analysis technique's effectiveness for measuring the dynamic response-time of pressure transmitters and their sensing lines; for performing predictive maintenance on reactor internals; for detecting core flow anomalies; and for extending neutron detector life. Integrated online condition monitoring systems can combine AC and DC data acquisition and signal processing techniques, can use data from existing process sensors during all modes of plant operation, including startup, normal operation, and shutdown; can be retrofitted into existing PWRs, BWRs, and other reactor types and will be integrated into next-generation plants. (orig.)

  7. Integrated online condition monitoring system for nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    Hashemian, Hashem M. [Analysis and Measurement Services Corporation, Knoxville, TN (United States). AMS Technology Center

    2010-09-15

    Online condition monitoring or online monitoring (OLM) uses data acquired while a nuclear power is operating to continuously assess the health of the plant's sensors, processes, and equipment; to measure the dynamic performance of the plant's process instrumentation; to verify in-situ the calibration of the process instrumentation; to detect blockages, voids, and leaks in the pressure sensing lines; to identify core flow anomalies; to extend the life of neutron detectors and other sensors; and to measure the vibration of reactor internals. Both the steady-state or DC output of plant sensors and their AC signal or noise output can be used to assess sensor health, depending on whether the application is monitoring a rapidly changing (e.g., core barrel motion) or a slowly changing (e.g., sensor calibration) process. The author has designed, developed, installed, and tested OLM systems (comprised of software/hardware-based data acquisition and data processing modules) that integrate low-frequency (1 mHz to 1 Hz) techniques such as RTD cross-calibration, pressure transmitter calibration monitoring, and equipment condition monitoring and high-frequency (1 Hz to 1 kHz) techniques such as the noise analysis technique. The author has demonstrated the noise analysis technique's effectiveness for measuring the dynamic response-time of pressure transmitters and their sensing lines; for performing predictive maintenance on reactor internals; for detecting core flow anomalies; and for extending neutron detector life. Integrated online condition monitoring systems can combine AC and DC data acquisition and signal processing techniques, can use data from existing process sensors during all modes of plant operation, including startup, normal operation, and shutdown; can be retrofitted into existing PWRs, BWRs, and other reactor types and will be integrated into next-generation plants. (orig.)

  8. USING CONDITION MONITORING TO PREDICT REMAINING LIFE OF ELECTRIC CABLES

    International Nuclear Information System (INIS)

    LOFARO, R.; SOO, P.; VILLARAN, M.; GROVE, E.

    2001-01-01

    Electric cables are passive components used extensively throughout nuclear power stations to perform numerous safety and non-safety functions. It is known that the polymers commonly used to insulate the conductors on these cables can degrade with time; the rate of degradation being dependent on the severity of the conditions in which the cables operate. Cables do not receive routine maintenance and, since it can be very costly, they are not replaced on a regular basis. Therefore, to ensure their continued functional performance, it would be beneficial if condition monitoring techniques could be used to estimate the remaining useful life of these components. A great deal of research has been performed on various condition monitoring techniques for use on electric cables. In a research program sponsored by the U.S. Nuclear Regulatory Commission, several promising techniques were evaluated and found to provide trendable information on the condition of low-voltage electric cables. These techniques may be useful for predicting remaining life if well defined limiting values for the aging properties being measured can be determined. However, each technique has advantages and limitations that must be addressed in order to use it effectively, and the necessary limiting values are not always easy to obtain. This paper discusses how condition monitoring measurements can be used to predict the remaining useful life of electric cables. The attributes of an appropriate condition monitoring technique are presented, and the process to be used in estimating the remaining useful life of a cable is discussed along with the difficulties that must be addressed

  9. Fast beam condition monitor for CMS. Performance and upgrade

    International Nuclear Information System (INIS)

    Leonard, Jessica L.; Bell, Alan; Burtowy, Piotr

    2014-05-01

    The CMS beam and radiation monitoring subsystem BCM1F (Fast Beam Condition Monitor) consists of 8 individual diamond sensors situated around the beam pipe within the pixel detector volume, for the purpose of fast bunch-by-bunch monitoring of beam background and collision products. In addition, effort is ongoing to use BCM1F as an online luminosity monitor. BCM1F will be running whenever there is beam in LHC, and its data acquisition is independent from the data acquisition of the CMS detector, hence it delivers luminosity even when CMS is not taking data. A report is given on the performance of BCM1F during LHC run I, including results of the van der Meer scan and on-line luminosity monitoring done in 2012. In order to match the requirements due to higher luminosity and 25 ns bunch spacing, several changes to the system must be implemented during the upcoming shutdown, including upgraded electronics and precise gain monitoring. First results from Run II preparation are shown.

  10. Fast Beam Condition Monitor for CMS: performance and upgrade

    CERN Document Server

    INSPIRE-00009152; Bell, Alan; Burtowy, Piotr; Dabrowski, Anne; Hempel, Maria; Henschel, Hans; Lange, Wolfgang; Lohmann, Wolfgang; Odell, Nathaniel; Penno, Marek; Pollack, Brian; Przyborowski, Dominik; Ryjov, Vladimir; Stickland, David; Walsh, Roberval; Warzycha, Weronika; Zagozdzinska, Agnieszka

    2014-11-21

    The CMS beam and radiation monitoring subsystem BCM1F (Fast Beam Condition Monitor) consists of 8 individual diamond sensors situated around the beam pipe within the pixel detector volume, for the purpose of fast bunch-by-bunch monitoring of beam background and collision products. In addition, effort is ongoing to use BCM1F as an online luminosity monitor. BCM1F will be running whenever there is beam in LHC, and its data acquisition is independent from the data acquisition of the CMS detector, hence it delivers luminosity even when CMS is not taking data. A report is given on the performance of BCM1F during LHC run I, including results of the van der Meer scan and on-line luminosity monitoring done in 2012. In order to match the requirements due to higher luminosity and 25 ns bunch spacing, several changes to the system must be implemented during the upcoming shutdown, including upgraded electronics and precise gain monitoring. First results from Run II preparation are shown.

  11. Development of hardware system using temperature and vibration maintenance models integration concepts for conventional machines monitoring: a case study

    Science.gov (United States)

    Adeyeri, Michael Kanisuru; Mpofu, Khumbulani; Kareem, Buliaminu

    2016-03-01

    This article describes the integration of temperature and vibration models for maintenance monitoring of conventional machinery parts in which their optimal and best functionalities are affected by abnormal changes in temperature and vibration values thereby resulting in machine failures, machines breakdown, poor quality of products, inability to meeting customers' demand, poor inventory control and just to mention a few. The work entails the use of temperature and vibration sensors as monitoring probes programmed in microcontroller using C language. The developed hardware consists of vibration sensor of ADXL345, temperature sensor of AD594/595 of type K thermocouple, microcontroller, graphic liquid crystal display, real time clock, etc. The hardware is divided into two: one is based at the workstation (majorly meant to monitor machines behaviour) and the other at the base station (meant to receive transmission of machines information sent from the workstation), working cooperatively for effective functionalities. The resulting hardware built was calibrated, tested using model verification and validated through principles pivoted on least square and regression analysis approach using data read from the gear boxes of extruding and cutting machines used for polyethylene bag production. The results got therein confirmed related correlation existing between time, vibration and temperature, which are reflections of effective formulation of the developed concept.

  12. Design of a New Collimation System to Prevent Interference between X-ray Machines and Radiation Portal Monitors

    International Nuclear Information System (INIS)

    Guzzardo, Tyler; Livesay, Jake

    2012-01-01

    Researchers at Oak Ridge National Laboratory (ORNL) developed a new collimation system that allows radiation portal monitors (RPMs) installed near x-ray machines to operate with a negligible false-positive alarm rate. RPMs are usually installed as far as possible from x-ray machines because false alarms are triggered by escaping x-rays; however, constraints at the installation site sometimes make it necessary that RPMs be installed near x-ray machines. Such RPMs are often plagued by high alarm rates resulting from the simultaneous operation of the RPMs and x-ray machines. Limitations on pedestrian flow, x-ray machine orientation, and RPM location often preclude a simple solution for lowering the alarm rate. Adding additional collimation to the x-ray machines to stop the x-rays at the source can reduce the alarm rate without interfering with site operations or adversely affecting the minimum detectable quantity of material (MDQ). A collimation design has been verified by measurements conducted at a RPM installation site and is applicable to all new and existing RPM installations near x-ray machines.

  13. An efficient recursive least square-based condition monitoring approach for a rail vehicle suspension system

    Science.gov (United States)

    Liu, X. Y.; Alfi, S.; Bruni, S.

    2016-06-01

    A model-based condition monitoring strategy for the railway vehicle suspension is proposed in this paper. This approach is based on recursive least square (RLS) algorithm focusing on the deterministic 'input-output' model. RLS has Kalman filtering feature and is able to identify the unknown parameters from a noisy dynamic system by memorising the correlation properties of variables. The identification of suspension parameter is achieved by machine learning of the relationship between excitation and response in a vehicle dynamic system. A fault detection method for the vertical primary suspension is illustrated as an instance of this condition monitoring scheme. Simulation results from the rail vehicle dynamics software 'ADTreS' are utilised as 'virtual measurements' considering a trailer car of Italian ETR500 high-speed train. The field test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real application. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the reference values. These results provide the supporting evidence that this fault diagnosis technique is capable of paving the way for the future vehicle condition monitoring system.

  14. Cloud Monitoring for Solar Plants with Support Vector Machine Based Fault Detection System

    Directory of Open Access Journals (Sweden)

    Hong-Chan Chang

    2014-01-01

    Full Text Available This study endeavors to develop a cloud monitoring system for solar plants. This system incorporates numerous subsystems, such as a geographic information system, an instantaneous power-consumption information system, a reporting system, and a failure diagnosis system. Visual C# was integrated with ASP.NET and SQL technologies for the proposed monitoring system. A user interface for database management system was developed to enable users to access solar power information and management systems. In addition, by using peer-to-peer (P2P streaming technology and audio/video encoding/decoding technology, real-time video data can be transmitted to the client end, providing instantaneous and direct information. Regarding smart failure diagnosis, the proposed system employs the support vector machine (SVM theory to train failure mathematical models. The solar power data are provided to the SVM for analysis in order to determine the failure types and subsequently eliminate failures at an early stage. The cloud energy-management platform developed in this study not only enhances the management and maintenance efficiency of solar power plants but also increases the market competitiveness of solar power generation and renewable energy.

  15. Condition monitoring through advanced sensor and computational technology

    International Nuclear Information System (INIS)

    Kim, Jung Taek; Hur, S.; Seong, S. H.; Hwang, Il Soon; Lee, Joon Hyun; You, Jun; Lee, Sang Jung

    2004-01-01

    In order to successfully implement the extended-life operation plan of the nuclear power plant (NPP), predictive maintenance based on on-line monitoring of deteriorated components becomes highly important. In this work, we present progresses in the development of an advanced monitoring system to detect the health condition on check valve failures and pipe wall-thinning phenomena. The failures of check valves have resulted in significant maintenance efforts, on occasion, have resulted in water hammer, over-pressurization of low-pressure systems, and damage to flow system components. Pipe wall-thinning is usually caused by Flow-Accelerated Corrosion (FAC) under the undesirable combination of water chemistry, flow velocity and material composition. A piping elbow in the moisture separator/reheater drain line on the secondary waterside of a PWR is chosen as a monitoring target

  16. Condition monitoring of steam turbo generators of captive power plant at HWP (Manuguru) through vibration analysis

    International Nuclear Information System (INIS)

    Krishnareddy, G.; Chandramouli, M.; Gupta, R.V.

    2002-01-01

    Turbo Generator is a critical equipment in steam based power plant circuit. Any failure causes loss of production and hence as applicable to Heavy Water Plant, Manuguru, it results in loss of heavy water production as the captive power plant at Manuguru is solely designed to supply steam and power to Main Plant, which is meant for production of heavy water. Thereby condition monitoring is very much essential and required as part of predictive maintenance program for the turbo generators which are in continuous operation. This paper focuses on identification of the turbo generator system through vibration spectrum, characterising and differentiating the fault mechanisms, trending the faults through changes in vibration spectrums and orbit plots and subsequently planning for corrective actions/measures after evaluating the changes in machine conditions

  17. Condition monitoring with wind turbine SCADA data using Neuro-Fuzzy normal behavior models

    DEFF Research Database (Denmark)

    Schlechtingen, Meik; Santos, Ilmar

    2012-01-01

    System (ANFIS) models are employed to learn the normal behavior in a training phase, where the component condition can be considered healthy. In the application phase the trained models are applied to predict the target signals, e.g. temperatures, pressures, currents, power output, etc. The behavior......This paper presents the latest research results of a project that focuses on normal behavior models for condition monitoring of wind turbines and their components, via ordinary Supervisory Control And Data Acquisition (SCADA) data. In this machine learning approach Adaptive Neuro-Fuzzy Interference...... of the prediction error is used as an indicator for normal and abnormal behavior, with respect to the learned behavior. The advantage of this approach is that the prediction error is widely decoupled from the typical fluctuations of the SCADA data caused by the different turbine operational modes. To classify...

  18. Bridge condition assessment based on long-term strain monitoring

    Science.gov (United States)

    Sun, LiMin; Sun, Shouwang

    2011-04-01

    In consideration of the important role that bridges play as transportation infrastructures, their safety, durability and serviceability have always been deeply concerned. Structural Health Monitoring Systems (SHMS) have been installed to many long-span bridges to provide bridge engineers with the information needed in making rational decisions for maintenance. However, SHMS also confronted bridge engineers with the challenge of efficient use of monitoring data. Thus, methodologies which are robust to random disturbance and sensitive to damage become a subject on which many researches in structural condition assessment concentrate. In this study, an innovative probabilistic approach for condition assessment of bridge structures was proposed on the basis of long-term strain monitoring on steel girder of a cable-stayed bridge. First, the methodology of damage detection in the vicinity of monitoring point using strain-based indices was investigated. Then, the composition of strain response of bridge under operational loads was analyzed. Thirdly, the influence of temperature and wind on strains was eliminated and thus strain fluctuation under vehicle loads is obtained. Finally, damage evolution assessment was carried out based on the statistical characteristics of rain-flow cycles derived from the strain fluctuation under vehicle loads. The research conducted indicates that the methodology proposed is qualified for structural condition assessment so far as the following respects are concerned: (a) capability of revealing structural deterioration; (b) immunity to the influence of environmental variation; (c) adaptability to the random characteristic exhibited by long-term monitoring data. Further examination of the applicability of the proposed methodology in aging bridge may provide a more convincing validation.

  19. The CMS Fast Beams Condition Monitor Backend Electronics based on MicroTCA technology

    CERN Document Server

    Zagozdzinska, Agnieszka Anna

    2016-01-01

    The Fast Beams Condition Monitor (BCM1F), upgraded for LHC Run II, is one sub-system of the Beam Radiation Instrumentation and Luminosity Project of the CMS experiment. It is based on 24 single crystal CVD diamond sensors. Each sensor is metallised with two pads, being read out by a dedicated fast frontend chip produced in 130 nm CMOS technology. Signals for real time monitoring are processed by custom-made back-end electronics to measure separately rates corresponding to LHC collision products, machine induced background and residual activation exploiting different arrival times. The system is built in MicroTCA technology and uses high speed analog-to-digital converters. The data processing module designed for the FPGA allows a distinguishing of collision and machine induced background, both synchronous to the LHC clock, from the residual activation products. In operational modes of high rates, consecutive events, spaced in time by less than 12.5 ns, may partially overlap. Hence, novel signal processing tec...

  20. Wire system aging assessment and condition monitoring (WASCO)

    International Nuclear Information System (INIS)

    Fantoni, P.F.

    2007-04-01

    Nuclear facilities rely on electrical wire systems to perform a variety of functions for successful operation. Many of these functions directly support the safe operation of the facility; therefore, the continued reliability of wire systems, even as they age, is critical. Condition Monitoring (CM) of installed wire systems is an important part of any aging program, both during the first 40 years of the qualified life and even more in anticipation of the license renewal for a nuclear power plant. This report contains some test results of a method for wire system condition monitoring, developed at the Halden Reactor Project, called LIRA (LIne Resonance Analysis), which can be used on-line to detect any local or global changes in the cable electrical parameters as a consequence of insulation faults or degradation. (au)

  1. Monitoring of Double Stud Wall Moisture Conditions in the Northeast

    Energy Technology Data Exchange (ETDEWEB)

    Ueno, K. [Building Science Corporation, Westford, MA (United States)

    2015-03-01

    Double-stud walls insulated with cellulose or low-density spray foam can have R-values of 40 or higher. However, double stud walls have a higher risk of interior-sourced condensation moisture damage, when compared with high-R approaches using exterior insulating sheathing.; Moisture conditions in double stud walls were monitored in Zone 5A (Massachusetts); three double stud assemblies were compared.

  2. Why is it important to monitor social conditions in wilderness?

    Science.gov (United States)

    Alan E. Watson

    1990-01-01

    “Social conditions in wilderness” refers to all aspects of human use of the wilderness that pose the possibility of impact to the resource and visitor experiences. The reasons for monitoring (1) use levels and use trends (including characteristics of use and users) and (2) the quality of the recreation experiences provided (ability to provide naturalness, privacy, and...

  3. Artificial intelligence-based condition monitoring for practical electrical drives

    OpenAIRE

    Ashari, Djoni; Pislaru, Crinela; Ball, Andrew; Gu, Fengshou

    2012-01-01

    The main types of existing Condition Monitoring methods (MCSA, GA, IAS) for electrical drives are\\ud described. Then the steps for the design of expert systems are presented: problem identification and analysis, system specification, development tool selection, knowledge based, prototyping and testing. The employment of SOMA (Self-Organizing Migrating Algorithm) used for the optimization of ambient\\ud vibration energy harvesting is analyzed. The power electronics devices are becoming smaller ...

  4. Monitoring the Microcirculation in Critical Conditions: Possibilities and Limitations

    Directory of Open Access Journals (Sweden)

    T. O. Tokmakova

    2012-01-01

    Full Text Available The paper provides an analytical review of the references on the role of microcirculatory disorders in the development of critical conditions, the significance of circulatory monitoring, specifically, to make a presumptive prognosis of multiple organ dysfunction. It defines main directions in the diagnosis and correction of microcirculatory disorders as direct (infusion therapy and indirect (influence on the components of a systemic inflammatory response, by extending to microcirculatory correction microcirculatory exposures. Key words: critical conditions, multiple organ dysfunction, procedures to evaluate and correct microcirculation.

  5. Machine Learning Techniques for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

    DEFF Research Database (Denmark)

    Thrane, Jakob; Wass, Jesper; Piels, Molly

    2017-01-01

    Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, while the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal...... detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical...

  6. Luminosity measurement and beam condition monitoring at CMS

    Energy Technology Data Exchange (ETDEWEB)

    Leonard, Jessica Lynn [DESY, Zeuthen (Germany)

    2015-07-01

    The BRIL system of CMS consists of instrumentation to measure the luminosity online and offline, and to monitor the LHC beam conditions inside CMS. An accurate luminosity measurement is essential to the CMS physics program, and measurement of the beam background is necessary to ensure safe operation of CMS. In expectation of higher luminosity and denser proton bunch spacing during LHC Run II, many of the BRIL subsystems are being upgraded and others are being added to complement the existing measurements. The beam condition monitor (BCM) consists of several sets of diamond sensors used to measure online luminosity and beam background with a single-bunch-crossing resolution. The BCM also detects when beam conditions become unfavorable for CMS running and may trigger a beam abort to protect the detector. The beam halo monitor (BHM) uses quartz bars to measure the background of the incoming beams at larger radii. The pixel luminosity telescope (PLT) consists of telescopes of silicon sensors designed to provide a CMS online and offline luminosity measurement. In addition, the forward hadronic calorimeter (HF) will deliver an independent luminosity measurement, making the whole system robust and allowing for cross-checks of the systematics. Data from each of the subsystems will be collected and combined in the BRIL DAQ framework, which will publish it to CMS and LHC. The current status of installation and commissioning results for the BRIL subsystems are given.

  7. Surface Acoustic Wave (SAW Resonators for Monitoring Conditioning Film Formation

    Directory of Open Access Journals (Sweden)

    Siegfried Hohmann

    2015-05-01

    Full Text Available We propose surface acoustic wave (SAW resonators as a complementary tool for conditioning film monitoring. Conditioning films are formed by adsorption of inorganic and organic substances on a substrate the moment this substrate comes into contact with a liquid phase. In the case of implant insertion, for instance, initial protein adsorption is required to start wound healing, but it will also trigger immune reactions leading to inflammatory responses. The control of the initial protein adsorption would allow to promote the healing process and to suppress adverse immune reactions. Methods to investigate these adsorption processes are available, but it remains difficult to translate measurement results into actual protein binding events. Biosensor transducers allow user-friendly investigation of protein adsorption on different surfaces. The combination of several transduction principles leads to complementary results, allowing a more comprehensive characterization of the adsorbing layer. We introduce SAW resonators as a novel complementary tool for time-resolved conditioning film monitoring. SAW resonators were coated with polymers. The adsorption of the plasma proteins human serum albumin (HSA and fibrinogen onto the polymer-coated surfaces were monitored. Frequency results were compared with quartz crystal microbalance (QCM sensor measurements, which confirmed the suitability of the SAW resonators for this application.

  8. Vibration condition monitoring of planetary gearbox under varying external load

    Energy Technology Data Exchange (ETDEWEB)

    Bartelmus, W.; Zimroz, R. [Wroclaw University of Technology, Wroclaw (Poland)

    2009-01-15

    The paper shows that for condition monitoring of planetary gearboxes it is important to identify the external varying load condition. In the paper, systematic consideration has been taken of the influence of many factors on the vibration signals generated by a system in which a planetary gearbox is included. These considerations give the basis for vibration signal interpretation, development of the means of condition monitoring, and for the scenario of the degradation of the planetary gearbox. Real measured vibration signals obtained in the industrial environment are processed. The signals are recorded during normal operation of the diagnosed objects, namely planetary gearboxes, which are a part of the driving system used in a bucket wheel excavator, used in lignite mines. It has been found that the most important factor of the proper planetary gearbox condition is connected with perturbation of arm rotation, where an arm rotation gives rise to a specific vibration signal whose properties are depicted by a short-time Fourier transform (STFT) and Wigner-Ville distribution presented as a time-frequency map. The paper gives evidence that there are two dominant low-frequency causes that influence vibration signal modulation, i.e. the varying load, which comes from the nature of the bucket wheel digging process, and the arm/carrier rotation. These two causes determine the condition of the planetary gearboxes considered.

  9. Development of Client-Server Application by Using UDP Socket Programming for Remotely Monitoring CNC Machine Environment in Fixture Process

    Directory of Open Access Journals (Sweden)

    Darmawan Darmawan

    2016-08-01

    Full Text Available The use of computer technology in manufacturing industries can improve manufacturing flexibility significantly, especially in manufacturing processes; many software applications have been utilized to improve machining performance. However, none of them has discussed the abilities to perform direct machining. In this paper, an integrated system for remote operation and monitoring of Computer Numerical Control (CNC machines is put into consideration. The integrated system includes computerization, network technology, and improved holding mechanism. The work proposed by this research is mainly on the software development for such integrated system. It uses Java three-dimensional (3D programming and Virtual Reality Modeling Language (VRML at the client side for visualization of machining environment. This research is aimed at developing a control system to remotely operate and monitor a self-reconfiguration fixture mechanism of a CNC milling machine through internet connection and integration of Personal Computer (PC-based CNC controller, a server side, a client side and CNC milling. The performance of the developed system was evaluated by testing with one type of common protocols particularly User Datagram Protocol (UDP.  Using UDP, the developed system requires 3.9 seconds to complete the close clamping, less than 1 second to release the clamping and it can deliver 463 KiloByte.

  10. The environment, international standards, asset health management and condition monitoring: An integrated strategy

    Energy Technology Data Exchange (ETDEWEB)

    Roe, S. [CSD, British Institute of Non-Destructive Testing (BINDT) (United Kingdom); Mba, D. [School of Engineering, Cranfield University, MK43 0AL, Bedfordshire (United Kingdom)], E-mail: d.mba@cranfield.ac.uk

    2009-02-15

    Asset Health Management (AHM), supported by condition monitoring (CM) and performance measuring technologies, together with trending, modelling and diagnostic frameworks, is not only critical to the reliability of high-value machines, but also to a companies Overall Equipment Efficiency (OEE), system safety and profitability. In addition these protocols are also critical to the global concern of the environment. Industries involved with monitoring key performances indicators (KPI) to improve OEE would benefit from a standardised qualification and certification scheme for their personnel, particularly if it is based on internationally accepted procedures for the various CM technologies that also share the same objectives as AH and CM. Furthermore, the development of 'models' for implementation of a Carbon tax is intrinsically dependent on the integrity and accuracy of measurements contributing to these indicators. This paper reviews the global picture of condition monitoring, the environment and related international standards and then considers their relationship and equivalent global objectives. In addition, it presents the methods behind the development of such standards for certification of competence in personnel involved with data collection, modelling and measurements of KPIs. Two case studies are presented that highlight the integrated strategy in practise.

  11. The environment, international standards, asset health management and condition monitoring: An integrated strategy

    International Nuclear Information System (INIS)

    Roe, S.; Mba, D.

    2009-01-01

    Asset Health Management (AHM), supported by condition monitoring (CM) and performance measuring technologies, together with trending, modelling and diagnostic frameworks, is not only critical to the reliability of high-value machines, but also to a companies Overall Equipment Efficiency (OEE), system safety and profitability. In addition these protocols are also critical to the global concern of the environment. Industries involved with monitoring key performances indicators (KPI) to improve OEE would benefit from a standardised qualification and certification scheme for their personnel, particularly if it is based on internationally accepted procedures for the various CM technologies that also share the same objectives as AH and CM. Furthermore, the development of 'models' for implementation of a Carbon tax is intrinsically dependent on the integrity and accuracy of measurements contributing to these indicators. This paper reviews the global picture of condition monitoring, the environment and related international standards and then considers their relationship and equivalent global objectives. In addition, it presents the methods behind the development of such standards for certification of competence in personnel involved with data collection, modelling and measurements of KPIs. Two case studies are presented that highlight the integrated strategy in practise

  12. Effect of processing conditions on residual stress distributions by bead-on-plate welding after surface machining

    International Nuclear Information System (INIS)

    Ihara, Ryohei; Mochizuki, Masahito

    2014-01-01

    Residual stress is important factor for stress corrosion cracking (SCC) that has been observed near the welded zone in nuclear power plants. Especially, surface residual stress is significant for SCC initiation. In the joining processes of pipes, butt welding is conducted after surface machining. Residual stress is generated by both processes, and residual stress distribution due to surface machining is varied by the subsequent butt welding. In previous paper, authors reported that residual stress distribution generated by bead on plate welding after surface machining has a local maximum residual stress near the weld metal. The local maximum residual stress shows approximately 900 MPa that exceeds the stress threshold for SCC initiation. Therefore, for the safety improvement of nuclear power plants, a study on the local maximum residual stress is important. In this study, the effect of surface machining and welding conditions on residual stress distribution generated by welding after surface machining was investigated. Surface machining using lathe machine and bead on plate welding with tungsten inert gas (TIG) arc under various conditions were conducted for plate specimens made of SUS316L. Then, residual stress distributions were measured by X-ray diffraction method (XRD). As a result, residual stress distributions have the local maximum residual stress near the weld metal in all specimens. The values of the local maximum residual stresses are almost the same. The location of the local maximum residual stress is varied by welding condition. It could be consider that the local maximum residual stress is generated by same generation mechanism as welding residual stress in surface machined layer that has high yield stress. (author)

  13. ASSESSMENT OF CABLE AGING USING CONDITION MONITORING TECHNIQUES

    International Nuclear Information System (INIS)

    GROVE, E.; LOFARO, R.; SOO, P.; VILLARAN, M.; HSU, F.

    2000-01-01

    Electric cables in nuclear power plants suffer degradation during service as a result of the thermal and radiation environments in which they are installed. Instrumentation and control cables are one type of cable that provide an important role in reactor safety. Should the polymeric cable insulation material become embrittled and cracked during service, or during a loss-of-coolant-accident (LOCA) and when steam and high radiation conditions are anticipated, failure could occur and prevent the cables from fulfilling their intended safety function(s). A research program is being conducted at Brookhaven National Laboratory to evaluate condition monitoring (CM) techniques for estimating the amount of cable degradation experienced during in-plant service. The objectives of this program are to assess the ability of the cables to perform under a simulated LOCA without losing their ability to function effectively, and to identify CM techniques which may be used to determine the effective lifetime of cables. The cable insulation materials tested include ethylene propylene rubber (EPR) and cross-linked polyethylene (XLPE). Accelerated aging (thermal and radiation) to the equivalent of 40 years of service was performed, followed by exposure to simulated LOCA conditions. The effectiveness of chemical, electrical, and mechanical condition monitoring techniques are being evaluated. Results indicate that several of these methods can detect changes in material parameters with increasing age. However, each has its limitations, and a combination of methods may provide an effective means for trending cable degradation in order to assess the remaining life of cables

  14. Condition Monitoring Through Advanced Sensor and Computational Technology

    International Nuclear Information System (INIS)

    Kim, Jung Taek; Park, Won Man; Kim, Jung Soo; Seong, Soeng Hwan; Hur, Sub; Cho, Jae Hwan; Jung, Hyung Gue

    2005-05-01

    The overall goal of this joint research project was to develop and demonstrate advanced sensors and computational technology for continuous monitoring of the condition of components, structures, and systems in advanced and next-generation nuclear power plants (NPPs). This project included investigating and adapting several advanced sensor technologies from Korean and US national laboratory research communities, some of which were developed and applied in non-nuclear industries. The project team investigated and developed sophisticated signal processing, noise reduction, and pattern recognition techniques and algorithms. The researchers installed sensors and conducted condition monitoring tests on two test loops, a check valve (an active component) and a piping elbow (a passive component), to demonstrate the feasibility of using advanced sensors and computational technology to achieve the project goal. Acoustic emission (AE) devices, optical fiber sensors, accelerometers, and ultrasonic transducers (UTs) were used to detect mechanical vibratory response of check valve and piping elbow in normal and degraded configurations. Chemical sensors were also installed to monitor the water chemistry in the piping elbow test loop. Analysis results of processed sensor data indicate that it is feasible to differentiate between the normal and degraded (with selected degradation mechanisms) configurations of these two components from the acquired sensor signals, but it is questionable that these methods can reliably identify the level and type of degradation. Additional research and development efforts are needed to refine the differentiation techniques and to reduce the level of uncertainties

  15. Wireless condition monitoring for the RA-6 research reactor

    International Nuclear Information System (INIS)

    Garcia Peyrano, O.; Calzeta, O.; Rico, N.; Damiani, H.; Coutsiers, E.

    1999-01-01

    The vibration laboratory at C.A.B. has a great experience with the analysis and diagnostic of symptoms of failures in the rotating equipment of the R-6 research reactor and in our longest NPP (CANDU 600 Mw), located in Embalse town, Cordoba City, Argentina. Objective: The standard condition monitoring instrumentation system were designed for large equipment operating under different environmental conditions and sensitivities. The signal processing is not flexible and the diagnostic is an expensive method for the small poll type research reactors. This papers describes the research and development which are related whit the new concept, cheaper and flexible condition monitoring instrumentation system. Implementing a vibration analysis measurements technique with a sensor inside (in the pool) of the nuclear reactor RA-6, and mainly based on fft signal processing, an extensive program for vibration source identification was done. Different nuclear power conditions were monitored as full power and in zero power, also. This zero power shows the best acoustical environmental, because the cooling pumps are stop, and the core is cooling by natural convection. Two sensors were mainly used as the detector's subsystem. One of these detectors was an accelerometer attached to the top of the fine control rod and the other one was a water resistant omnidirectional microphone which was located underwater at different distances from the nuclear core. All the signal measurement by this two sensors were recorded and then was processed. Both signal was acquired at the same time for correlation analysis purposes. The analysis was composed by a 'Spectral Dynamics SD380' connected to a P.C. with dedicated post processing software. On the other hand, some calibration and sensitivity comparison was done using an SKFCM40, dual channel data collector and analyzer. (author)

  16. Ionization beam profile monitor for operation under hard environmental conditions

    International Nuclear Information System (INIS)

    Teterev, Yu.G.; Kaminski, G.; Phi Thanh Huong; Kaminski, G.; Kozik, E.

    2010-01-01

    The design and the performance of the Ionization Beam Profile Monitor (IBPM) operating on the residual gas ionization principle are described. The main advantage of the constructed device is the non-contact measuring method. Operating under hard environmental conditions it delivers the information about the primary beam position, profile and intensity in 'on-line' regime. It was found out that the device is capable to operate in vacuum in the range of 10 -6 /10 -3 mbar without the loss of the resolution power at the beam current as low as a few nA. The IBPM is prospective for beam profile monitoring due to long time. Emergency situations do not lead to decrease of its operability.

  17. Cable condition monitoring in a pressurized water reactor environment

    International Nuclear Information System (INIS)

    Al-Hussaini, T.J.

    1988-01-01

    Oconee Nuclear Station is the first nuclear plant designed, engineered and constructed by Duke Power Company. Even though the accelerated aging method was available to determine the life expectancy of the cable used in the reactor building, no natural aging data was available at that time. In order to be able to verify the condition of the reactor building cable over the life of the plant, an on-going cable monitoring plan was instituted. Various types of cable were selected to be monitored, and they were installed in cable life evaluation circuits in the reactor building. At five year intervals over the life of the plant, cable samples would be removed from these cable life evaluation circuits and tested to determine the effects of the reactor building environment on the integrity of the cable. A review of the cable life evaluation circuits and the results of the evaluation program to date is presented

  18. Research on Land Ecological Condition Investigation and Monitoring Technology

    Science.gov (United States)

    Lv, Chunyan; Guo, Xudong; Chen, Yuqi

    2017-04-01

    The ecological status of land reflects the relationship between land use and environmental factors. At present, land ecological situation in China is worrying. According to the second national land survey data, there are about 149 million acres of arable land located in forests and grasslands area in Northeast and Northwest of China, Within the limits of the highest flood level, at steep slope above 25 degrees; about 50 million acres of arable land has been in heavy pollution; grassland degradation is still serious. Protected natural forests accounted for only 6% of the land area, and forest quality is low. Overall, the ecological problem has been eased, but the local ecological destruction intensified, natural ecosystem in degradation. It is urgent to find out the situation of land ecology in the whole country and key regions as soon as possible. The government attaches great importance to ecological environment investigation and monitoring. Various industries and departments from different angles carry out related work, most of it about a single ecological problem, the lack of a comprehensive surveying and assessment of land ecological status of the region. This paper established the monitoring index system of land ecological condition, including Land use type area and distribution, quality of cultivated land, vegetation status and ecological service, arable land potential and risk, a total of 21 indicators. Based on the second national land use survey data, annual land use change data and high resolution remote sensing data, using the methods of sample monitoring, field investigation and statistical analysis to obtain the information of each index, this paper established the land ecological condition investigation and monitoring technology and method system. It has been improved, through the application to Beijing-Tianjin-Hebei Urban Agglomeration, the northern agro-pastoral ecological fragile zone, and 6 counties (cities).

  19. Condition monitoring of pumps with co-relating field observations

    International Nuclear Information System (INIS)

    Mishra, S.K.; Prasad, V.; Sharma, R.B.

    1994-01-01

    The maintenance of 40 MWth research reactor, Cirus has been carried out for over 30 years following the time based maintenance schedule. With the commissioning of indigenously built 100 MWth nuclear research reactor Dhruva in the year 1985, a systematic work on condition monitoring has been commissioned. Apart from process parameters, which are recorded on hourly basis, vibration, noise, temperature, kurtosis etc. are measured for assessment of condition of pumps. The bearings of flywheel assembly of main pumps, Dhruva broke down almost abruptly during the initial years after first commissioning. The regular measurements of vibration level and kurtosis have greatly helped in avoiding breakdown. In a recent case one newly procured herringbone gear box (300 hp, 1475/1760 rpm) for the primary coolant pump was showing high vibration. In further checking using Fast Fourier Transform (FFT) analyser in a time domain plot the gear teeth damage was indicated. The pump was shut down for inspection and when the gear box was dismantled teeth were found broken. An attempt has been made in this paper to discuss a few interesting field experiences with condition monitoring and correlating field observations on pumps. (author). 3 figs

  20. Wind Turbine Gearbox Condition Monitoring Round Robin Study - Vibration Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Sheng, S.

    2012-07-01

    The Gearbox Reliability Collaborative (GRC) at the National Wind Technology Center (NWTC) tested two identical gearboxes. One was tested on the NWTCs 2.5 MW dynamometer and the other was field tested in a turbine in a nearby wind plant. In the field, the test gearbox experienced two oil loss events that resulted in damage to its internal bearings and gears. Since the damage was not severe, the test gearbox was removed from the field and retested in the NWTCs dynamometer before it was disassembled. During the dynamometer retest, some vibration data along with testing condition information were collected. These data enabled NREL to launch a Wind Turbine Gearbox Condition Monitoring Round Robin project, as described in this report. The main objective of this project was to evaluate different vibration analysis algorithms used in wind turbine condition monitoring (CM) and find out whether the typical practices are effective. With involvement of both academic researchers and industrial partners, the project sets an example on providing cutting edge research results back to industry.

  1. Mechanical Seal Opening Condition Monitoring Based on Acoustic Emission Technology

    Directory of Open Access Journals (Sweden)

    Erqing Zhang

    2014-06-01

    Full Text Available Since the measurement of mechanical sealing film thickness and just-lift-off time is very difficult, the sealing film condition monitoring method based on acoustic emission signal is proposed. The mechanical seal acoustic emission signal present obvious characteristics of time-varying nonlinear and pulsating. In this paper, the acoustic emission signal is used to monitor the seal end faces just-lift-off time and friction condition. The acoustic emission signal is decomposed by empirical mode decomposition into a series of intrinsic mode function with independent characteristics of different time scales and different frequency band. The acoustic emission signal only generated by end faces friction is obtained by eliminating the false intrinsic mode function components. The correlation coefficient of acoustic emission signal and Multi-scale Laplace Wavelet is calculated. It is proved that the maximum frequency (8000 Hz of the correlation coefficient is appeared at the spindle speed of 300 rpm. And at this time (300 rpm the end faces have just lifted off. By a set of mechanical oil seal running test, it is demonstrated that this method could accurately identify mechanical seal end faces just-lift-off time and friction condition.

  2. Optimization of Remediation Conditions using Vadose Zone Monitoring Technology

    Science.gov (United States)

    Dahan, O.; Mandelbaum, R.; Ronen, Z.

    2010-12-01

    Success of in-situ bio-remediation of the vadose zone depends mainly on the ability to change and control hydrological, physical and chemical conditions of subsurface. These manipulations enables the development of specific, indigenous, pollutants degrading bacteria or set the environmental conditions for seeded bacteria. As such, the remediation efficiency is dependent on the ability to implement optimal hydraulic and chemical conditions in deep sections of the vadose zone. Enhanced bioremediation of the vadose zone is achieved under field conditions through infiltration of water enriched with chemical additives. Yet, water percolation and solute transport in unsaturated conditions is a complex process and application of water with specific chemical conditions near land surface dose not necessarily result in promoting of desired chemical and hydraulic conditions in deeper sections of the vadose zone. A newly developed vadose-zone monitoring system (VMS) allows continuous monitoring of the hydrological and chemical properties of the percolating water along deep sections of the vadose zone. Implementation of the VMS at sites that undergoes active remediation provides real time information on the chemical and hydrological conditions in the vadose zone as the remediation process progresses. Manipulating subsurface conditions for optimal biodegradation of hydrocarbons is demonstrated through enhanced bio-remediation of the vadose zone at a site that has been contaminated with gasoline products in Tel Aviv. The vadose zone at the site is composed of 6 m clay layer overlying a sandy formation extending to the water table at depth of 20 m bls. The upper 5 m of contaminated soil were removed for ex-situ treatment, and the remaining 15 m vadose zone is treated in-situ through enhanced bioremedaition. Underground drip irrigation system was installed below the surface on the bottom of the excavation. Oxygen and nutrients releasing powder (EHCO, Adventus) was spread below the

  3. Guaranteeing robustness of structural condition monitoring to environmental variability

    Science.gov (United States)

    Van Buren, Kendra; Reilly, Jack; Neal, Kyle; Edwards, Harry; Hemez, François

    2017-01-01

    Advances in sensor deployment and computational modeling have allowed significant strides to be recently made in the field of Structural Health Monitoring (SHM). One widely used SHM strategy is to perform a vibration analysis where a model of the structure's pristine (undamaged) condition is compared with vibration response data collected from the physical structure. Discrepancies between model predictions and monitoring data can be interpreted as structural damage. Unfortunately, multiple sources of uncertainty must also be considered in the analysis, including environmental variability, unknown model functional forms, and unknown values of model parameters. Not accounting for these sources of uncertainty can lead to false-positives or false-negatives in the structural condition assessment. To manage the uncertainty, we propose a robust SHM methodology that combines three technologies. A time series algorithm is trained using "baseline" data to predict the vibration response, compare predictions to actual measurements collected on a potentially damaged structure, and calculate a user-defined damage indicator. The second technology handles the uncertainty present in the problem. An analysis of robustness is performed to propagate this uncertainty through the time series algorithm and obtain the corresponding bounds of variation of the damage indicator. The uncertainty description and robustness analysis are both inspired by the theory of info-gap decision-making. Lastly, an appropriate "size" of the uncertainty space is determined through physical experiments performed in laboratory conditions. Our hypothesis is that examining how the uncertainty space changes throughout time might lead to superior diagnostics of structural damage as compared to only monitoring the damage indicator. This methodology is applied to a portal frame structure to assess if the strategy holds promise for robust SHM. (Publication approved for unlimited, public release on October-28

  4. Detection and classification of alarm threshold violations in condition monitoring systems working in highly varying operational conditions

    Science.gov (United States)

    Strączkiewicz, M.; Barszcz, T.; Jabłoński, A.

    2015-07-01

    All commonly used condition monitoring systems (CMS) enable defining alarm thresholds that enhance efficient surveillance and maintenance of dynamic state of machinery. The thresholds are imposed on the measured values such as vibration-based indicators, temperature, pressure, etc. For complex machinery such as wind turbine (WT) the total number of thresholds might be counted in hundreds multiplied by the number of operational states. All the parameters vary not only due to possible machinery malfunctions, but also due to changes in operating conditions and these changes are typically much stronger than the former ones. Very often, such a behavior may lead to hundreds of false alarms. Therefore, authors propose a novel approach based on parameterized description of the threshold violation. For this purpose the novelty and severity factors are introduced. The first parameter refers to the time of violation occurrence while the second one describes the impact of the indicator-increase to the entire machine. Such approach increases reliability of the CMS by providing the operator with the most useful information of the system events. The idea of the procedure is presented on a simulated data similar to those from a wind turbine.

  5. Detection and classification of alarm threshold violations in condition monitoring systems working in highly varying operational conditions

    International Nuclear Information System (INIS)

    Strączkiewicz, M; Barszcz, T; Jabłoński, A

    2015-01-01

    All commonly used condition monitoring systems (CMS) enable defining alarm thresholds that enhance efficient surveillance and maintenance of dynamic state of machinery. The thresholds are imposed on the measured values such as vibration-based indicators, temperature, pressure, etc. For complex machinery such as wind turbine (WT) the total number of thresholds might be counted in hundreds multiplied by the number of operational states. All the parameters vary not only due to possible machinery malfunctions, but also due to changes in operating conditions and these changes are typically much stronger than the former ones. Very often, such a behavior may lead to hundreds of false alarms. Therefore, authors propose a novel approach based on parameterized description of the threshold violation. For this purpose the novelty and severity factors are introduced. The first parameter refers to the time of violation occurrence while the second one describes the impact of the indicator-increase to the entire machine. Such approach increases reliability of the CMS by providing the operator with the most useful information of the system events. The idea of the procedure is presented on a simulated data similar to those from a wind turbine. (paper)

  6. Testbeam results of the upgraded fast beam condition monitor at CMS

    Energy Technology Data Exchange (ETDEWEB)

    Hempel, Maria; Karacheban, Olena; Lohmann, Wolfgang [BTU, Cottbus (Germany); DESY, Zeuthen (Germany); Afanaciev, Konstantin [NCPHEP, Minsk (Belarus); Burtowy, Piotr; Ryjov, Vladimir; Zagozdzinska, Agnieszka [CERN, Geneva (Switzerland); Henschel, Hans; Lange, Wolfgang; Leonard, Jessica Lynn [DESY, Zeuthen (Germany); Levy, Itamar [Tel Aviv University, Tel Aviv (Israel); Przyborowski, Dominik [AGH-UST, Cracow (Poland); Schuwalow, Sergej; Walsh, Roberval [DESY, Hamburg (Germany)

    2016-07-01

    The Fast Beam Condition Monitor BCM1F at CMS is based on single-crystal diamond sensor with nanosecond time resolution. BCM1F delivered luminosity and machine induced background information to the CMS and LHC control room during the first running period of the LHC. A major upgrade to BCM1F was developed and built during the long shutdown of the LHC in 2014. The increased rate and the 25ns spacing should be handled with sensors subdivided by a double pad metallization and a faster new front-end ASIC. A prototype with these new components was investigated in the testbeam at DESY-II. The results are presented and also verified by Superfish simulations.

  7. Diamond pad detector telescope for beam conditions and luminosity monitoring in ATLAS

    Energy Technology Data Exchange (ETDEWEB)

    Mikuz, M. [Jozef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana (Slovenia)], E-mail: Marko.Mikuz@ijs.si; Cindro, V.; Dolenc, I. [Jozef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana (Slovenia); Frais-Koelbl, H. [University of Applied Sciences Wiener Neustadt and Fotec, Wiener Neustadt (Austria); Gorisek, A. [CERN, Geneva (Switzerland); Griesmayer, E. [University of Applied Sciences Wiener Neustadt and Fotec, Wiener Neustadt (Austria); Kagan, H. [Ohio State University, Columbus (United States); Kramberger, G.; Mandic, I. [Jozef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana (Slovenia); Niegl, M. [University of Applied Sciences Wiener Neustadt and Fotec, Wiener Neustadt (Austria); Pernegger, H. [CERN, Geneva (Switzerland); Trischuk, W. [University of Toronto, Toronto (Canada); Weilhammer, P. [CERN, Geneva (Switzerland); Zavrtanik, M. [Jozef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana (Slovenia)

    2007-09-01

    Beam conditions and the potential detector damage resulting from their anomalies have pushed the LHC experiments to plan their own monitoring devices in addition to those provided by the machine. ATLAS decided to build a telescope composed of two stations with four diamond pad detector modules each, placed symmetrically around the interaction point at z={+-}183.8cm and r{approx}55mm ({eta}{approx}4.2). Equipped with fast electronics it allows time-of-flight separation of events resulting from beam anomalies from normally occurring p-p interactions. In addition it will provide a coarse measurement of the LHC luminosity in ATLAS. Ten detector modules have been assembled and subjected to tests, from characterization of bare diamonds to source and beam tests. Preliminary results of beam test in the CERN PS indicate a signal-to-noise ratio of 14{+-}2.

  8. Diamond pad detector telescope for beam conditions and luminosity monitoring in ATLAS

    International Nuclear Information System (INIS)

    Mikuz, M.; Cindro, V.; Dolenc, I.; Frais-Koelbl, H.; Gorisek, A.; Griesmayer, E.; Kagan, H.; Kramberger, G.; Mandic, I.; Niegl, M.; Pernegger, H.; Trischuk, W.; Weilhammer, P.; Zavrtanik, M.

    2007-01-01

    Beam conditions and the potential detector damage resulting from their anomalies have pushed the LHC experiments to plan their own monitoring devices in addition to those provided by the machine. ATLAS decided to build a telescope composed of two stations with four diamond pad detector modules each, placed symmetrically around the interaction point at z=±183.8cm and r∼55mm (η∼4.2). Equipped with fast electronics it allows time-of-flight separation of events resulting from beam anomalies from normally occurring p-p interactions. In addition it will provide a coarse measurement of the LHC luminosity in ATLAS. Ten detector modules have been assembled and subjected to tests, from characterization of bare diamonds to source and beam tests. Preliminary results of beam test in the CERN PS indicate a signal-to-noise ratio of 14±2

  9. A new luminometer and beam conditions monitor for the CMS experiment

    Energy Technology Data Exchange (ETDEWEB)

    Karacheban, Olena; Hempel, Maria [Brandenburg University of Technology, Cottbus (Germany); DESY, Zeuthen (Germany); Dabrowski, Anne; Ryjov, Vladimir; Stickland, David; Zagozdzinska, Agnieszka [CERN, Geneva (Switzerland); Henschel, Hans; Lange, Wolfgang [DESY, Zeuthen (Germany); Leonard, Jessica; Walsh, Roberval [DESY, Hamburg (Germany); Levy, Itamar [Tel Aviv University, Tel Aviv (Israel); Lohmann, Wolfgang [Brandenburg University of Technology, Cottbus (Germany); RWTH Aachen University, Aachen (Germany); Przyborowski, Dominik [AGH-UST University, Cracow (Poland); Schuwalow, Sergej [DESY, Zeuthen (Germany); DESY, Hamburg (Germany)

    2016-07-01

    The luminosity is a key quantity of any collider, which allows for the determination of the absolute cross sections from the observed rate in a detector. The Fast Beam Conditions Monitor (BCM1F) was upgraded in the last LHC long technical stop (LS1) to 24 diamond sensors read out by a dedicated fast ASIC in 130 nm CMOS technology. The backend comprises a deadtime-less histogramming unit, with a 6.25 ns bin width, in VME standard. A microTCA system with better time resolution is in development. BCM1F is used for luminosity and machine induced background measurements at the CMS experiment. The performance of the detector in the first running period, as well as results on the calibration (Van-der-Meer scan) and the measurements of the luminosity are presented.

  10. Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage

    Directory of Open Access Journals (Sweden)

    Björn J. P. van der Ster

    2018-01-01

    Full Text Available Introduction: In the initial phase of hypovolemic shock, mean blood pressure (BP is maintained by sympathetically mediated vasoconstriction rendering BP monitoring insensitive to detect blood loss early. Late detection can result in reduced tissue oxygenation and eventually cellular death. We hypothesized that a machine learning algorithm that interprets currently used and new hemodynamic parameters could facilitate in the detection of impending hypovolemic shock.Method: In 42 (27 female young [mean (sd: 24 (4 years], healthy subjects central blood volume (CBV was progressively reduced by application of −50 mmHg lower body negative pressure until the onset of pre-syncope. A support vector machine was trained to classify samples into normovolemia (class 0, initial phase of CBV reduction (class 1 or advanced CBV reduction (class 2. Nine models making use of different features were computed to compare sensitivity and specificity of different non-invasive hemodynamic derived signals. Model features included: volumetric hemodynamic parameters (stroke volume and cardiac output, BP curve dynamics, near-infrared spectroscopy determined cortical brain oxygenation, end-tidal carbon dioxide pressure, thoracic bio-impedance, and middle cerebral artery transcranial Doppler (TCD blood flow velocity. Model performance was tested by quantifying the predictions with three methods: sensitivity and specificity, absolute error, and quantification of the log odds ratio of class 2 vs. class 0 probability estimates.Results: The combination with maximal sensitivity and specificity for classes 1 and 2 was found for the model comprising volumetric features (class 1: 0.73–0.98 and class 2: 0.56–0.96. Overall lowest model error was found for the models comprising TCD curve hemodynamics. Using probability estimates the best combination of sensitivity for class 1 (0.67 and specificity (0.87 was found for the model that contained the TCD cerebral blood flow velocity

  11. Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage.

    Science.gov (United States)

    van der Ster, Björn J P; Bennis, Frank C; Delhaas, Tammo; Westerhof, Berend E; Stok, Wim J; van Lieshout, Johannes J

    2017-01-01

    Introduction: In the initial phase of hypovolemic shock, mean blood pressure (BP) is maintained by sympathetically mediated vasoconstriction rendering BP monitoring insensitive to detect blood loss early. Late detection can result in reduced tissue oxygenation and eventually cellular death. We hypothesized that a machine learning algorithm that interprets currently used and new hemodynamic parameters could facilitate in the detection of impending hypovolemic shock. Method: In 42 (27 female) young [mean (sd): 24 (4) years], healthy subjects central blood volume (CBV) was progressively reduced by application of -50 mmHg lower body negative pressure until the onset of pre-syncope. A support vector machine was trained to classify samples into normovolemia (class 0), initial phase of CBV reduction (class 1) or advanced CBV reduction (class 2). Nine models making use of different features were computed to compare sensitivity and specificity of different non-invasive hemodynamic derived signals. Model features included : volumetric hemodynamic parameters (stroke volume and cardiac output), BP curve dynamics, near-infrared spectroscopy determined cortical brain oxygenation, end-tidal carbon dioxide pressure, thoracic bio-impedance, and middle cerebral artery transcranial Doppler (TCD) blood flow velocity. Model performance was tested by quantifying the predictions with three methods : sensitivity and specificity, absolute error, and quantification of the log odds ratio of class 2 vs. class 0 probability estimates. Results: The combination with maximal sensitivity and specificity for classes 1 and 2 was found for the model comprising volumetric features (class 1: 0.73-0.98 and class 2: 0.56-0.96). Overall lowest model error was found for the models comprising TCD curve hemodynamics. Using probability estimates the best combination of sensitivity for class 1 (0.67) and specificity (0.87) was found for the model that contained the TCD cerebral blood flow velocity derived

  12. Upgraded Fast Beam Conditions Monitor for CMS online luminosity measurement

    CERN Document Server

    Leonard, Jessica Lynn; Hempel, Maria; Henschel, Hans; Karacheban, Olena; Lange, Wolfgang; Lohmann, Wolfgang; Novgorodova, Olga; Penno, Marek; Walsh, Roberval; Dabrowski, Anne; Guthoff, Moritz; Loos, R; Ryjov, Vladimir; Burtowy, Piotr; Lokhovitskiy, Arkady; Odell, Nathaniel; Przyborowski, Dominik; Stickland, David P; Zagozdzinska, Agnieszka

    2014-01-01

    The CMS beam condition monitoring subsystem BCM1F during LHC Run I consisted of 8 individual diamond sensors situated around the beam pipe within the tracker detector volume, for the purpose of fast monitoring of beam background and collision products. Effort is ongoing to develop the use of BCM1F as an online bunch-by-bunch luminosity monitor. BCM1F will be running whenever there is beam in LHC, and its data acquisition is independent from the data acquisition of the CMS detector, hence it delivers luminosity even when CMS is not taking data. To prepare for the expected increase in the LHC luminosity and the change from 50 ns to 25 ns bunch separation, several changes to the system are required, including a higher number of sensors and upgraded electronics. In particular, a new real-time digitizer with large memory was developed and is being integrated into a multi-subsystem framework for luminosity measurement. Current results from Run II preparation will be discussed, including results from the January 201...

  13. Monitoring of lubrication conditions in journal bearing by acoustic emission

    International Nuclear Information System (INIS)

    Yoon, Dong Jin; Kwon, Oh Yang; Jung, Min Hwa

    1993-01-01

    Systems with journal bearings generally operate in large scale and under severe loading conditions such as steam generator turbines and internal combustion engines, in contrast to the machinery using rolling element bearings. Failure of the bearings in these machinery can result in the system breakdown. To avoid the time consuming repair and considerable economic loss, the detection of incipient failure in journal bearings becomes very important. In this experimental approach, acoustic emission monitoring is employed to the detection of incipient failure caused by intervention of foreign particles most probable in the journal bearing systems. It has been known that the intervention of foreign materials, insufficient lubrication and misassembly etc. are principal factors to cause bearing failure and distress. The experiment was conducted under such designed conditions as inserting alumina particles to the lubrication layer in the simulated journal bearing system. The results showed that acoustic emission could be an effective tool to detect the incipient failure in journal bearings.

  14. VegScape: U.S. Crop Condition Monitoring Service

    Science.gov (United States)

    mueller, R.; Yang, Z.; Di, L.

    2013-12-01

    Since 1995, the US Department of Agriculture (USDA)/National Agricultural Statistics Service (NASS) has provided qualitative biweekly vegetation condition indices to USDA policymakers and the public on a weekly basis during the growing season. Vegetation indices have proven useful for assessing crop condition and identifying the areal extent of floods, drought, major weather anomalies, and vulnerabilities of early/late season crops. With growing emphasis on more extreme weather events and food security issues rising to the forefront of national interest, a new vegetation condition monitoring system was developed. The new vegetation condition portal named VegScape was initiated at the start of the 2013 growing season. VegScape delivers web mapping service based interactive vegetation indices. Users can use an interactive map to explore, query and disseminate current crop conditions. Vegetation indices like Normal Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), and mean, median, and ratio comparisons to prior years can be constructed for analytical purposes and on-demand crop statistics. The NASA MODIS satellite with 250 meter (15 acres) resolution and thirteen years of data history provides improved spatial and temporal resolutions and delivers improved detailed timely (i.e., daily) crop specific condition and dynamics. VegScape thus provides supplemental information to support NASS' weekly crop reports. VegScape delivers an agricultural cultivated crop mask and the most recent Cropland Data Layer (CDL) product to exploit the agricultural domain and visualize prior years' planted crops. Additionally, the data can be directly exported to Google Earth for web mashups or delivered via web mapping services for uses in other applications. VegScape supports the ethos of data democracy by providing free and open access to digital geospatial data layers using open geospatial standards, thereby supporting transparent and collaborative government

  15. A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

    Science.gov (United States)

    Zimmerman, Naomi; Presto, Albert A.; Kumar, Sriniwasa P. N.; Gu, Jason; Hauryliuk, Aliaksei; Robinson, Ellis S.; Robinson, Allen L.; Subramanian, R.

    2018-01-01

    Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16-19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error), and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that

  16. Condition monitoring of machinery using motor current signature analysis

    International Nuclear Information System (INIS)

    Kryter, R.C.; Haynes, H.D.

    1989-01-01

    Motor current signature analysis (MCSA) is a powerful monitoring tool for motor-driven equipment that provides a nonintrusive means for detecting the presence of mechanical and electrical abnormalities in the motor and the driven equipment, including altered conditions in the process ''downstream'' of the motor-driven equipment. It was developed at the Oak Ridge National Laboratory as a means for determining the effects of aging and service wear systems, but it is applicable to a broad range of machinery. MCSA is based on the recognition that an electric motor (ac or dc) driving a mechanical load acts as an efficient and permanently available transducer by sensing mechanical load variations, large and small, long-term and rapid, and converting them into variations in the induced current generated in the motor windings. These motor current variations are carried by the electrical cables processes as desired. Motor current signatures, obtained in both time and over time to provide early indication of degradation. Successful applications of MCSA technology (patent applied for) include not only motor-operated valves but also pumps of various designs, blowers, and air conditioning systems. Examples are presented briefly, and speculation regarding the applicability of MCSA to a broader range of equipment monitoring and production line testing is also given. 1 ref., 13 figs

  17. Wire system aging assessment and condition monitoring (WASCO)

    Energy Technology Data Exchange (ETDEWEB)

    Fantoni, P.F. [Institutt for energiteknikk (Norway); Nordlund, A. [Chalmers Univ. of Technology (Sweden)

    2006-04-15

    Nuclear facilities rely on electrical wire systems to perform a variety of functions for successful operation. Many of these functions directly support the safe operation of the facility; therefore, the continued reliability of wire systems, even as they age, is critical. Condition Monitoring (CM) of installed wire systems is an important part of any aging program, both during the first 40 years of the qualified life and even more in anticipation of the license renewal for a nuclear power plant. This report describes a method for wire system condition monitoring, developed at the Halden Reactor Project, which is based on Frequency Domain Reflectometry. This method resulted in the development of a system called LIRA (LIne Resonance Analysis), which can be used on-line to detect any local or global changes in the cable electrical parameters as a consequence of insulation faults or degradation. LIRA is composed of a signal generator, a signal analyser and a simulator that can be used to simulate several failure/degradation scenarios and assess the accuracy and sensitivity of the LIRA system. Chapter 5 of this report describes an complementary approach based on positron measurement techniques, used widely in defect physics due to the high sensitivity to micro defects, in particular open volume defects. This report describes in details these methodologies, the results of field experiments and the proposed future work. (au)

  18. Wire system aging assessment and condition monitoring (WASCO)

    International Nuclear Information System (INIS)

    Fantoni, P.F.; Nordlund, A.

    2006-04-01

    Nuclear facilities rely on electrical wire systems to perform a variety of functions for successful operation. Many of these functions directly support the safe operation of the facility; therefore, the continued reliability of wire systems, even as they age, is critical. Condition Monitoring (CM) of installed wire systems is an important part of any aging program, both during the first 40 years of the qualified life and even more in anticipation of the license renewal for a nuclear power plant. This report describes a method for wire system condition monitoring, developed at the Halden Reactor Project, which is based on Frequency Domain Reflectometry. This method resulted in the development of a system called LIRA (LIne Resonance Analysis), which can be used on-line to detect any local or global changes in the cable electrical parameters as a consequence of insulation faults or degradation. LIRA is composed of a signal generator, a signal analyser and a simulator that can be used to simulate several failure/degradation scenarios and assess the accuracy and sensitivity of the LIRA system. Chapter 5 of this report describes an complementary approach based on positron measurement techniques, used widely in defect physics due to the high sensitivity to micro defects, in particular open volume defects. This report describes in details these methodologies, the results of field experiments and the proposed future work. (au)

  19. Control of Greenhouse Environmental Conditions with IOT Based Monitoring and Analysis System

    Directory of Open Access Journals (Sweden)

    Ali Çaylı

    2017-10-01

    Full Text Available Wireless sensor networks applications and inter-machine communication (M2M, called the Internet of Things, help decision-makers to control complex systems thanks to the low data-rate and cost-effective data collection and analysis. These technologies offer new possibilities to monitor environmental management and agricultural policies, and to improve agricultural production, especially in low-income rural areas. In this study, IoT is proposed with a low cost, flexible and scalable data collection and analysis system. For this purpose, open source hardware microprocessor cards and sensors are stored in the greenhouse computer database using the IEEE 802.15.4 Zigbee wireless communication protocol. The data can be analyzed by greenhouse computer analysis software, which is developed with the PHP programming language. It is possible to monitor the real time data from the greenhouse computer. Also alert rules definitions can be made and the system was tested in greenhouse conditions. It has been observed that it performs operations steadily such as data transfer, sensor measurements and data processing. The proposed system may be useful for monitoring indoor climate and controlling ventilation, irrigation and heating systems, especially for small enterprises due to the modular structure.

  20. Aspects of structural health and condition monitoring of offshore wind turbines.

    Science.gov (United States)

    Antoniadou, I; Dervilis, N; Papatheou, E; Maguire, A E; Worden, K

    2015-02-28

    Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector.

  1. Aspects of structural health and condition monitoring of offshore wind turbines

    Science.gov (United States)

    Antoniadou, I.; Dervilis, N.; Papatheou, E.; Maguire, A. E.; Worden, K.

    2015-01-01

    Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector. PMID:25583864

  2. Catalytic aided electrical discharge machining of polycrystalline diamond - parameter analysis of finishing condition

    Science.gov (United States)

    Haikal Ahmad, M. A.; Zulafif Rahim, M.; Fauzi, M. F. Mohd; Abdullah, Aslam; Omar, Z.; Ding, Songlin; Ismail, A. E.; Rasidi Ibrahim, M.

    2018-01-01

    Polycrystalline diamond (PCD) is regarded as among the hardest material in the world. Electrical Discharge Machining (EDM) typically used to machine this material because of its non-contact process nature. This investigation was purposely done to compare the EDM performances of PCD when using normal electrode of copper (Cu) and newly proposed graphitization catalyst electrode of copper nickel (CuNi). Two level full factorial design of experiment with 4 center points technique was used to study the influence of main and interaction effects of the machining parameter namely; pulse-on, pulse-off, sparking current, and electrode materials (categorical factor). The paper shows interesting discovery in which the newly proposed electrode presented positive impact to the machining performance. With the same machining parameters of finishing, CuNi delivered more than 100% better in Ra and MRR than ordinary Cu electrode.

  3. Condition Assessment of Foundation Piles and Utility Poles Based on Guided Wave Propagation Using a Network of Tactile Transducers and Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Ulrike Dackermann

    2017-12-01

    Full Text Available This paper presents a novel non-destructive testing and health monitoring system using a network of tactile transducers and accelerometers for the condition assessment and damage classification of foundation piles and utility poles. While in traditional pile integrity testing an impact hammer with broadband frequency excitation is typically used, the proposed testing system utilizes an innovative excitation system based on a network of tactile transducers to induce controlled narrow-band frequency stress waves. Thereby, the simultaneous excitation of multiple stress wave types and modes is avoided (or at least reduced, and targeted wave forms can be generated. The new testing system enables the testing and monitoring of foundation piles and utility poles where the top is inaccessible, making the new testing system suitable, for example, for the condition assessment of pile structures with obstructed heads and of poles with live wires. For system validation, the new system was experimentally tested on nine timber and concrete poles that were inflicted with several types of damage. The tactile transducers were excited with continuous sine wave signals of 1 kHz frequency. Support vector machines were employed together with advanced signal processing algorithms to distinguish recorded stress wave signals from pole structures with different types of damage. The results show that using fast Fourier transform signals, combined with principal component analysis as the input feature vector for support vector machine (SVM classifiers with different kernel functions, can achieve damage classification with accuracies of 92.5% ± 7.5%.

  4. High speed machinability of the aerospace alloy AA7075 T6 under different cooling conditions

    Science.gov (United States)

    Imbrogno, Stano; Rinaldi, Sergio; Suarez, Asier Gurruchaga; Arrazola, Pedro J.; Umbrello, Domenico

    2018-05-01

    This paper describes the results of an experimental investigation aimed to st udy the machinability of AA7075 T6 (160 HV) for aerospace industry at high cutting speeds. The paper investigates the effects of different lubri-cooling strategies (cryogenic, M QL and dry) during high speed turning process on cutting forces, tool wear, chip morphology and cutting temperatures. The cutting speeds selected were 1000m/min, 1250m/min and 1500 m/min, while the feed rate values used were 0.1mm/rev and 0.3 mm/rev. The results of cryogenic and M QL application is compared with dry application. It was found that the cryogenic and M QL lubri-cooling techniques could represent a functional alternative to the common dry cutting application in order to implement a more effect ive high speed turning process. Higher cuttingparameters would be able to increase the productivity and reduce the production costs. The effects of the cutting parameters and on the variables object of study were investigated and the role of the different lubri-cooling conditions was assessed.

  5. Mimic sensor to monitor condition of human health; Mimic sensor wo riyoshita taicho monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Nagata, Y. [Mechanical Engineering Lab., Tokyo (Japan)

    2000-04-01

    In the aging society where the birth rate decreases and the number of nuclear families increases, it is very important to inquire after the aged or physically handicapped people, to monitor their physical conditions, and to take steps to keep them healthy. As for the in-home physical measurement for the aged or physically handicapped people and the work of health management for them based on such measurement, it is feared that under the present conditions the invalid themselves or their family members or nurses will inevitably have to bear the burden and that nobody can deny the difficulty of continuing such nursing care. If daily physical condition measurement and related data collection are automatically carried out, however, interested people' burden will lessen and in-home heath management will become actually feasible. In this paper, a mimic sensor for realizing such a situation is described, which will measure physical conditions without interfering with the daily life of interested people. Serving as the mimic sensor is a blood flow sensor embedded in a telephone receiver, and changes in the blood flow during telephone conversation and changes in the gaps between peeks will be monitored. The feasibility is shown of continual collection of information necessary for the measurement of physical conditions of the aged or physically handicapped persons. (NEDO)

  6. Analysis of surface integrity in machining of AISI 304 stainless steel under various cooling and cutting conditions

    Science.gov (United States)

    Klocke, F.; Döbbeler, B.; Lung, S.; Seelbach, T.; Jawahir, I. S.

    2018-05-01

    Recent studies have shown that machining under specific cooling and cutting conditions can be used to induce a nanocrystalline surface layer in the workspiece. This layer has beneficial properties, such as improved fatigue strength, wear resistance and tribological behavior. In machining, a promising approach for achieving grain refinement in the surface layer is the application of cryogenic cooling. The aim is to use the last step of the machining operation to induce the desired surface quality to save time-consuming and expensive post machining surface treatments. The material used in this study was AISI 304 stainless steel. This austenitic steel suffers from low yield strength that limits its technological applications. In this paper, liquid nitrogen (LN2) as cryogenic coolant, as well as minimum quantity lubrication (MQL), was applied and investigated. As a reference, conventional flood cooling was examined. Besides the cooling conditions, the feed rate was varied in four steps. A large rounded cutting edge radius and finishing cutting parameters were chosen to increase the mechanical load on the machined surface. The surface integrity was evaluated at both, the microstructural and the topographical levels. After turning experiments, a detailed analysis of the microstructure was carried out including the imaging of the surface layer and hardness measurements at varying depths within the machined layer. Along with microstructural investigations, different topological aspects, e.g., the surface roughness, were analyzed. It was shown that the resulting microstructure strongly depends on the cooling condition. This study also shows that it was possible to increase the micro hardness in the top surface layer significantly.

  7. Condition monitoring of electrical equipment in nuclear power plants

    International Nuclear Information System (INIS)

    Sugarman, A.

    1986-01-01

    Condition monitoring (CM) is a subset of maintenance testing. It is a quantitative, predictive technique for assessing the effects of all types of aging (environmental, cyclic, operational, etc) on the ''health'' of the equipment. A difference between CM and maintenance testing is that the latter is neither quantitative (i.e., measures the relative condition of the component or material as opposed to merely verifying that its condition is acceptable) nor predictive (i.e., makes judgments, on the ability of the component to perform at a future time). A common example of the principle of CM can be illustrated with the automobile which has a lifetime that is small enough to observe all the periods (break in, random failure, wear out) that occur throughout aging. There are several weak link components in the car (e.g., water hoses, contacts in the distributor, generator, spark plug cables, solenoid, etc) which if they fail will cause failure of the automobile to either start or run. From the day the car is put on the road and is subjected to heat and vibration, significant aging of these components occurs. Degradation in the water hoses, for example is manifested by the elastomeric casing becoming brittle and cracking

  8. Distributed acoustic fibre optic sensors for condition monitoring of pipelines

    Science.gov (United States)

    Hussels, Maria-Teresa; Chruscicki, Sebastian; Habib, Abdelkarim; Krebber, Katerina

    2016-05-01

    Industrial piping systems are particularly relevant to public safety and the continuous availability of infrastructure. However, condition monitoring systems based on many discrete sensors are generally not well-suited for widespread piping systems due to considerable installation effort, while use of distributed fibre-optic sensors would reduce this effort to a minimum. Specifically distributed acoustic sensing (DAS) is employed for detection of third-party threats and leaks in oil and gas pipelines in recent years and can in principle also be applied to industrial plants. Further possible detection routes amenable by DAS that could identify damage prior to emission of medium are subject of a current project at BAM, which aims at qualifying distributed fibre optic methods such as DAS as a means for spatially continuous monitoring of industrial piping systems. Here, first tests on a short pipe are presented, where optical fibres were applied directly to the surface. An artificial signal was used to define suitable parameters of the measurement system and compare different ways of applying the sensor.

  9. Gearbox Fatigue Load Estimation for Condition Monitoring of Wind Turbines

    DEFF Research Database (Denmark)

    Perisic, Nevena; Pedersen, Bo Juul; Kirkegaard, Poul Henning

    2012-01-01

    control and data acquisition (SCADA) system. Estimated loads can be further used for prediction of remaining operating lifetime of turbine components, detection of high stress level or fault detection. An augmented Kalman filter is chosen as the fatigue load estimator because its characteristics well suit......The focus of the paper is on a design of a fatigue load estimator for predictive condition monitoring systems (CMS) of wind turbines. In order to avoid high-price measurement equipment required for direct load measuring, an indirect approach is suggested using only measurements from supervisory...... for the real time application. This paper presents results of the estimation of the gearbox fatigue load, often called shaft torque, using simulated data of wind turbine. Noise sensitivity of the algorithm is investigated by assuming different levels of measurement noise. Shaft torque estimations are compared...

  10. Maintenance Planning of Offshore Wind Turbine using Condition Monitoring Information

    DEFF Research Database (Denmark)

    Ramírez, José G. Rangel; Sørensen, John Dalsgaard

    2009-01-01

    Deterioration processes such as fatigue and corrosion are typically affecting offshore structures. To "control" this deterioration, inspection and maintenance activities are developed. Probabilistic methodologies represent an important tool to identify the suitable strategy to inspect and control...... the deterioration in structures such as offshore wind turbines (OWT). Besides these methods, the integration of condition monitoring information (CMI) can optimize the mitigation activities as an updating tool. In this paper, a framework for risk-based inspection and maintenance planning (RBI) is applied for OWT....... With the integration of CMI by means Bayesian inference, a slightly change of first inspection times are coming up, influenced by the reduction of the uncertainty and harsher or milder external agents....

  11. Condition monitoring with Mean field independent components analysis

    DEFF Research Database (Denmark)

    Pontoppidan, Niels Henrik; Sigurdsson, Sigurdur; Larsen, Jan

    2005-01-01

    We discuss condition monitoring based on mean field independent components analysis of acoustic emission energy signals. Within this framework it is possible to formulate a generative model that explains the sources, their mixing and also the noise statistics of the observed signals. By using...... a novelty approach we may detect unseen faulty signals as indeed faulty with high precision, even though the model learns only from normal signals. This is done by evaluating the likelihood that the model generated the signals and adapting a simple threshold for decision. Acoustic emission energy signals...... from a large diesel engine is used to demonstrate this approach. The results show that mean field independent components analysis gives a better detection of fault compared to principal components analysis, while at the same time selecting a more compact model...

  12. Design and realization of high voltage disconnector condition monitoring system

    Science.gov (United States)

    Shi, Jinrui; Xu, Tianyang; Yang, Shuixian; Li, Buoyang

    2017-08-01

    The operation status of the high voltage disconnector directly affects the safe and stable operation of the power system. This article uses the wireless frequency hopping communication technology of the communication module to achieve the temperature acquisition of the switch contacts and high voltage bus, to introduce the current value of the loop in ECS, and judge the operation status of the disconnector by considering the ambient temperature, calculating the temperature rise; And through the acquisition of the current of drive motor in the process of switch closing and opening, and fault diagnosis of the disconnector by analyzing the change rule of the drive motor current, the condition monitoring of the high voltage disconnector is realized.

  13. Study on Surface Integrity of AISI 1045 Carbon Steel when machined by Carbide Cutting Tool under wet conditions

    Directory of Open Access Journals (Sweden)

    Tamin N. Fauzi

    2017-01-01

    Full Text Available This paper presents the evaluation of surface roughness and roughness profiles when machining carbon steel under wet conditions with low and high cutting speeds. The workpiece materials and cutting tools selected in this research were AISI 1045 carbon steel and canela carbide inserts graded PM25, respectively. The cutting tools undergo machining tests by CNC turning operations and their performances were evaluated by their surface roughness value and observation of the surface roughness profile. The machining tests were held at varied cutting speeds of 35 to 53 m/min, feed rate of 0.15 to 0.50 mm/rev and a constant depth of cut of 1 mm. From the analysis, it was found that surface roughness increased as the feed rate increased. Varian of surface roughness was suspected due to interaction between cutting speeds and feed rates as well as nose radius conditions; whether from tool wear or the formation of a built-up edge. This study helps us understand the effect of cutting speed and feed rate on surface integrity, when machining AISI 1045 carbon steel using carbide cutting tools, under wet cutting conditions.

  14. Machine Learning for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

    DEFF Research Database (Denmark)

    Wass, J.; Thrane, Jakob; Piels, Molly

    2016-01-01

    Supervised machine learning methods are applied and demonstrated experimentally for inband OSNR estimation and modulation format classification in optical communication systems. The proposed methods accurately evaluate coherent signals up to 64QAM using only intensity information....

  15. Online Condition Monitoring of Gripper Cylinder in TBM Based on EMD Method

    Science.gov (United States)

    Li, Lin; Tao, Jian-Feng; Yu, Hai-Dong; Huang, Yi-Xiang; Liu, Cheng-Liang

    2017-11-01

    The gripper cylinder that provides braced force for Tunnel Boring Machine (TBM) might fail due to severe vibration when the TBM excavates in the tunnel. Early fault diagnosis of the gripper cylinder is important for the safety and efficiency of the whole tunneling project. In this paper, an online condition monitoring system based on the Empirical Mode Decomposition (EMD) method is established for fault diagnosis of the gripper cylinder while TBM is working. Firstly, the lumped mass parameter model of the gripper cylinder is established considering the influence of the variable stiffness at the rock interface, the equivalent stiffness of the oil, the seals, and the copper guide sleeve. The dynamic performance of the gripper cylinder is investigated to provide basis for its health condition evaluation. Then, the EMD method is applied to identify the characteristic frequencies of the gripper cylinder for fault diagnosis and a field test is used to verify the accuracy of the EMD method for detection of the characteristic frequencies. Furthermore, the contact stiffness at the interface between the barrel and the rod is calculated with Hertz theory and the relationship between the natural frequency and the stiffness varying with the health condition of the cylinder is simulated based on the dynamic model. The simulation shows that the characteristic frequencies decrease with the increasing clearance between the barrel and the rod, thus the defects could be indicated by monitoring the natural frequency. Finally, a health condition management system of the gripper cylinder based on the vibration signal and the EMD method is established, which could ensure the safety of TBM.

  16. Abnormal condition detector for a local power range monitor

    International Nuclear Information System (INIS)

    Akiyama, Takao.

    1976-01-01

    Object: to permit determination of abnormal condition by a number of local power range monitors (LPRM) to be quickly made through precise estimation of the ratio between the true rate of change in neutron flux and true change in the neutron flux by making use of the fact that the status of the neutron distribution does not widely change with a change in the core flow rate for a short period of time. Structure: While carrying out power control according to the core flow rate, detection values from LPRM which are disposed in a three-dimensional fashion within the reactor core are indicated on an indicator. The average value of rates of change in the indicated values for a group of LPRM under substantially the same fluid dynamic condition as that for each LPRM is determined while measuring time-wise change rate in the indicated value of each of the LPRM. The average value is successively divided by the rate of change in the indicated value for each LPRM and the amplifier gain thereof to obtain the reference value. When the difference between the average value and reference value obtained in this way exceeds a prescribed value, the corresponding LPRM is determined to be defective. (Moriyama, K.)

  17. A modern diagnostic approach for automobile systems condition monitoring

    Science.gov (United States)

    Selig, M.; Shi, Z.; Ball, A.; Schmidt, K.

    2012-05-01

    An important topic in automotive research and development is the area of active and passive safety systems. In general, it is grouped in active safety systems to prevent accidents and passive systems to reduce the impact of a crash. An example for an active system is ABS while a seat belt tensioner represents the group of passive systems. Current developments in the automotive industry try to link active with passive system components to enable a complete event sequence, beginning with the warning of the driver about a critical situation till the automatic emergency call after an accident. The cross-linking has an impact on the current diagnostic approach, which is described in this paper. Therefore, this contribution introduces a new diagnostic approach for automotive mechatronic systems. The concept is based on monitoring the messages which are exchanged via the automotive communication systems, e.g. the CAN bus. According to the authors' assumption, the messages on the bus are changing between faultless and faulty vehicle condition. The transmitted messages of the sensors and control units are different depending on the condition of the car. First experiments are carried and in addition, the hardware design of a suitable diagnostic interface is presented. Finally, first results will be presented and discussed.

  18. A modern diagnostic approach for automobile systems condition monitoring

    International Nuclear Information System (INIS)

    Selig, M; Ball, A; Shi, Z; Schmidt, K

    2012-01-01

    An important topic in automotive research and development is the area of active and passive safety systems. In general, it is grouped in active safety systems to prevent accidents and passive systems to reduce the impact of a crash. An example for an active system is ABS while a seat belt tensioner represents the group of passive systems. Current developments in the automotive industry try to link active with passive system components to enable a complete event sequence, beginning with the warning of the driver about a critical situation till the automatic emergency call after an accident. The cross-linking has an impact on the current diagnostic approach, which is described in this paper. Therefore, this contribution introduces a new diagnostic approach for automotive mechatronic systems. The concept is based on monitoring the messages which are exchanged via the automotive communication systems, e.g. the CAN bus. According to the authors' assumption, the messages on the bus are changing between faultless and faulty vehicle condition. The transmitted messages of the sensors and control units are different depending on the condition of the car. First experiments are carried and in addition, the hardware design of a suitable diagnostic interface is presented. Finally, first results will be presented and discussed.

  19. Experimental Aspects in the Vibration-Based Condition Monitoring of Large Hydrogenerators

    Directory of Open Access Journals (Sweden)

    Geraldo Carvalho Brito Junior

    2017-01-01

    Full Text Available Based on experimental observations on a set of twenty 700 MW hydrogenerators, compiled from several technical reports issued over the last three decades and collected from the reprocessing of the vibration signals recorded during the last commissioning tests, this paper shows that the accurate determination of the journal bearings operating conditions may be a difficult task. It shows that the outsize bearing brackets of large hydrogenerators are subject to substantial dimensional changes caused by external agents, like the generator electromagnetic field and the bearing cooling water temperature. It also shows that the shaft eccentricity of a journal bearing of a healthy large hydrogenerator, operating in steady-state condition, may experience unpredictable, sudden, and significant changes without apparent reasons. Some of these phenomena are reproduced in ordinary commissioning tests or may be noticed even during normal operation, while others are rarely observed or are only detected through special tests. These phenomena modify journal bearings stiffness and damping, changing the hydrogenerator dynamics, creating discrepancies between theoretical predictions and experimental measurements, and making damage detection and diagnostics difficult. Therefore, these phenomena must be analyzed and considered in the application of vibration-based condition monitoring to these rotating machines.

  20. Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations

    Directory of Open Access Journals (Sweden)

    Cunji Zhang

    2016-05-01

    Full Text Available Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z are acquired during milling operations, and the raw signals are de-noised by wavelet analysis. These features of de-noised signals are extracted in the time, frequency and time–frequency domains. The key features are selected based on Pearson’s Correlation Coefficient (PCC. The Neuro-Fuzzy Network (NFN is adopted to predict the tool wear and Remaining Useful Life (RUL. In comparison with Back Propagation Neural Network (BPNN and Radial Basis Function Network (RBFN, the results show that the NFN has the best performance in the prediction of tool wear and RUL.

  1. Machine and plasma diagnostic instrumentation systems for the Tandem Mirror Experiment Upgrade

    International Nuclear Information System (INIS)

    Coutts, G.W.; Coffield, F.E.; Lang, D.D.; Hornady, R.S.

    1981-01-01

    To evaluate performance of a second generation Tandem Mirror Machine, an extensive instrumentation system is being designed and installed as part of the major device fabrication. The systems listed will be operational during the start-up phase of the TMX Upgrade machine and provide bench marks for future performance data. In addition to plasma diagnostic instrumentation, machine parameter monitoring systems will be installed prior to machine operation. Simultaneous recording of machine parameters will permit evaluation of plasma parameters sensitive to machine conditions

  2. Condition monitoring of rotor blades of modern wind power systems; Ueberwachung mit Hertz. Condition Monitoring von Rotorblaettern moderner Windenergieanlagen

    Energy Technology Data Exchange (ETDEWEB)

    Fecht, Nikolaus

    2010-06-15

    With seven wind turbines, the Austrian wind farm ''Sternwald'' is the biggest wind farm in Upper Austria. It is the only wind farm in a forest, and all turbines are therefore equipped with automatic fire fighting equipment. The mountain range on which the wind farm is located is about 1000 m high, with strong wind and much ice and snow in the winter season. For this reason, the owner decided to instal a condition monitoring system with ice detectors. The piezoelectric sensors are mounted directly on the rotor blades as measurements on the nacelle will always be incorrect. Installation on the rotor blades, on the other hand, makes high demands on the fastenings and sensors as the velocity of the blade tips may be up to 250 km per hour. (orig.)

  3. Conductive ink print on PA66 gear for manufacturing condition monitoring sensors

    Science.gov (United States)

    Futagawa, Shintaro; Iba, Daisuke; Kamimoto, Takahiro; Nakamura, Morimasa; Miura, Nanako; Iizuka, Takashi; Masuda, Arata; Sone, Akira; Moriwaki, Ichiro

    2018-03-01

    Failures detection of rotating machine elements, such as gears, is an important issue. The purpose of this study was to try to solve this issue by printing conductive ink on gears to manufacture condition-monitoring sensors. In this work, three types of crack detection sensor were designed and the sprayed conductive ink was directly sintered on polyimide (PI) - coated polyamide (PA) 66 gears by laser. The result showed that it was possible to produce narrow circuit lines of the conductive ink including Ag by laser sintering technique and the complex shape sensors on the lateral side of the PA66 gears, module 1.0 mm and tooth number 48. A preliminary operation test was carried out for investigation of the function of the sensors. As a result of the test, the sensors printed in this work should be effective for detecting cracks at tooth root of the gears and will allow for the development of better equipment and detection techniques for health monitoring of gears.

  4. Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System

    Directory of Open Access Journals (Sweden)

    A. Romero

    2016-01-01

    Full Text Available Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry. This paper presents a novel approach for health condition monitoring (CM and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on a machine learning algorithm that generates a baseline for the identification of deviations from the normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information. The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature.

  5. CONDITIONS FOR STABLE CHIP BREAKING AND PROVISION OF MACHINED SURFACE QUALITY WHILE TURNING WITH ASYMMETRIC TOOL VIBRATIONS

    Directory of Open Access Journals (Sweden)

    V. K. Sheleh

    2015-01-01

    Full Text Available The paper considers a process of turning structural steel with asymmetric tool vibrations directed along feeding. Asymmetric vibrations characterized by asymmetry coefficient of vibration cycle, their frequency and amplitude are additionally transferred to the tool in the turning process with the purpose to crush chips. Conditions of stable chip breaking and obtaining optimum dimensions of chip elements have been determined in the paper. In order to reduce a negative impact of the vibration amplitude on a cutting process and quality of the machined surfaces machining must be carried out with its minimum value. In this case certain ratio of the tool vibration frequency to the work-piece rotation speed has been ensured in the paper. A formula has been obtained for calculation of this ratio with due account of the expected length of chip elements and coefficient of vibration cycle asymmetry.Influence of the asymmetric coefficient of the tool vibration cycle on roughness of the machined surfaces and cutting tool wear has been determined in the paper. According to the results pertaining to machining of work-pieces made of 45 and ШХ15 steel the paper presents mathematical relationships of machined surface roughness with cutting modes and asymmetry coefficient of tool vibration cycle. Tool feeding being one of the cutting modes exerts the most significant impact on the roughness value and increase of the tool feeding entails increase in roughness. Reduction in coefficient of vibration cycle asymmetry contributes to surface roughness reduction. However, the cutting tool wear occurs more intensive. Coefficient of the vibration cycle asymmetry must be increased in order to reduce wear rate. Therefore, the choice of the coefficient of the vibration cycle asymmetry is based on the parameters of surface roughness which must be obtained after machining and intensity of tool wear rate.The paper considers a process of turning structural steel with asymmetric

  6. Physical working conditions as covered in European monitoring questionnaires

    Directory of Open Access Journals (Sweden)

    Tore Tynes

    2017-06-01

    Full Text Available Abstract Background The prevalence of workers with demanding physical working conditions in the European work force remains high, and occupational physical exposures are considered important risk factors for musculoskeletal disorders (MSD, a major burden for both workers and society. Exposures to physical workloads are therefore part of the European nationwide surveys to monitor working conditions and health. An interesting question is to what extent the same domains, dimensions and items referring to the physical workloads are covered in the surveys. The purpose of this paper is to determine 1 which domains and dimensions of the physical workloads are monitored in surveys at the national level and the EU level and 2 the degree of European consensus among these surveys regarding coverage of individual domains and dimensions. Method Items on physical workloads used in one European wide/Spanish and five other European nationwide work environment surveys were classified into the domains and dimensions they cover, using a taxonomy agreed upon among all participating partners. Results The taxonomy reveals that there is a modest overlap between the domains covered in the surveys, but when considering dimensions, the results indicate a lower agreement. The phrasing of items and answering categories differs between the surveys. Among the domains, the three domains covered by all surveys are “lifting, holding & carrying of loads/pushing & pulling of loads”, “awkward body postures” and “vibrations”. The three domains covered less well, that is only by three surveys or less, are “physical work effort”, “working sitting”, and “mixed exposure”. Conclusions This is the fırst thorough overview to evaluate the coverage of domains and dimensions of self-reported physical workloads in a selection of European nationwide surveys. We hope the overview will provide input to the revisions and updates of the individual countries’ surveys in

  7. Study of the Induction Machine Unsymmetrical Condition Using In Total Fluxes Equations

    Directory of Open Access Journals (Sweden)

    SIMION, A.

    2010-02-01

    Full Text Available On the basis of the mathematical model, called in total fluxes in a previous paper, and which is proper for the analysis of transient operation of the two-phase induction machine, one obtains the symmetrical steady-state equations, which are valid for three-phase machines, as well. The obtained mathematical expressions are much more simple and easier to use than the consecrated ones, which are generally applied in scientific literature. Moreover, considerations are to be made upon the space-time rotational vectors, emphasizing their importance in understanding the physical phenomena that characterize induction machines. The use of these space vectors is further tested out for the study of unsymmetrical supply, which gives a much faster method in obtaining the electromagnetic torque expression. Finally, the results are compared with the ones that come out from the traditional methods, more exactly, the symmetric component method.

  8. Stochastic Estimation Methods for Induction Motor Transient Thermal Monitoring Under Non Linear Condition

    Directory of Open Access Journals (Sweden)

    Mellah HACEN

    2012-08-01

    Full Text Available The induction machine, because of its robustness and low-cost, is commonly used in the industry. Nevertheless, as every type of electrical machine, this machine suffers of some limitations. The most important one is the working temperature which is the dimensioning parameter for the definition of the nominal working point and the machine lifetime. Due to a strong demand concerning thermal monitoring methods appeared in the industry sector. In this context, the adding of temperature sensors is not acceptable and the studied methods tend to use sensorless approaches such as observators or parameters estimators like the extended Kalman Filter (EKF. Then the important criteria are reliability, computational cost ad real time implementation.

  9. DESIGN OF CAMERA MOUNT AND ITS APPLICATION FOR MONITORING MACHINING PROCESS

    Directory of Open Access Journals (Sweden)

    Nadežda Čuboňová

    2015-05-01

    Full Text Available The article deals with the solution to the problem of holding a scanning device – GoPro camera in the vicinity of milling machine EMCO Concept MILL 105, practical part solves the design and production of the fixture. The proposal of the fixture includes the best placing of the fixture within the milling area. On this basis individual variants of this solution are elaborated. The best variant for holding of the camera was selected and fixture production was experimentally performed on a 3D printer – Easy 3D Maker. Fixture functionality was verified on the milling machine.

  10. Development of Beam Conditions Monitor for the ATLAS experiment

    CERN Document Server

    Dolenc Kittelmann, Irena; Mikuž, M

    2008-01-01

    If there is a failure in an element of the accelerator the resulting beam losses could cause damage to the inner tracking devices of the experiments. This thesis presents the work performed during the development phase of a protection system for the ATLAS experiment at the LHC. The Beam Conditions Monitor (BCM) system is a stand-alone system designed to detect early signs of beam instabilities and trigger a beam abort in case of beam failures. It consists of two detector stations positioned at z=±1.84m from the interaction point. Each station comprises four BCM detector modules installed symmetrically around the beam pipe with sensors located at r=55 mm. This structure will allow distinguishing between anomalous events (beam gas and beam halo interactions, beam instabilities) and normal events due to proton-proton interaction by measuring the time-of-flight as well as the signal pulse amplitude from detector modules on the timescale of nanoseconds. Additionally, the BCM system aims to provide a coarse instan...

  11. Condition Monitoring of Sensors in a NPP Using Optimized PCA

    Directory of Open Access Journals (Sweden)

    Wei Li

    2018-01-01

    Full Text Available An optimized principal component analysis (PCA framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, including data preprocessing stage, modeling parameter selection stage, and fault detection and isolation stage. Then, the model’s performance is greatly improved through these optimizations. Finally, sensor measurements from a real NPP are used to train the optimized PCA model in order to guarantee the credibility and reliability of the simulation results. Meanwhile, artificial faults are sequentially imposed to sensor measurements to estimate the fault detection and isolation ability of the proposed PCA model. Simulation results show that the optimized PCA model is capable of detecting and isolating the sensors regardless of whether they exhibit major or small failures. Meanwhile, the quantitative evaluation results also indicate that better performance can be obtained in the optimized PCA method compared with the common PCA method.

  12. Structural health monitoring methodology for aircraft condition-based maintenance

    Science.gov (United States)

    Saniger, Jordi; Reithler, Livier; Guedra-Degeorges, Didier; Takeda, Nobuo; Dupuis, Jean Pierre

    2001-06-01

    Reducing maintenance costs while keeping a constant level of safety is a major issue for Air Forces and airlines. The long term perspective is to implement condition based maintenance to guarantee a constant safety level while decreasing maintenance costs. On this purpose, the development of a generalized Structural Health Monitoring System (SHMS) is needed. The objective of such a system is to localize the damages and to assess their severity, with enough accuracy to allow low cost corrective actions. The present paper describes a SHMS based on acoustic emission technology. This choice was driven by its reliability and wide use in the aerospace industry. The described SHMS uses a new learning methodology which relies on the generation of artificial acoustic emission events on the structure and an acoustic emission sensor network. The calibrated acoustic emission events picked up by the sensors constitute the knowledge set that the system relies on. With this methodology, the anisotropy of composite structures is taken into account, thus avoiding the major cause of errors of classical localization methods. Moreover, it is adaptive to different structures as it does not rely on any particular model but on measured data. The acquired data is processed and the event's location and corrected amplitude are computed. The methodology has been demonstrated and experimental tests on elementary samples presented a degree of accuracy of 1cm.

  13. Design and setting up of a system for remote monitoring and control on auxiliary machines in electric vehicles

    Directory of Open Access Journals (Sweden)

    Dimitrov Vasil

    2017-01-01

    Full Text Available Systems for remote monitoring and control of the proper operation, energy consumption, and efficiency of the controlled objects are very often used in different spheres of industry, in the electricity distribution network, etc. Various types of intelligent energy meters, PLCs and other control devices are involved in such systems. Proper operation of the auxiliary machines in electric vehicles is of great importance and implementation of a system for their remote monitoring and control is useful and ensures reliability and increased efficiency. A system has been designed and built using contemporary devices. An asynchronous motor is controlled by a soft starter and opportunities for remote monitoring (by an intelligent energy meter and control (by a PLC and Touch panel have been provided. Soft starters are widely used in industry for control on asynchronous drives when speed regulation is not a mandatory requirement. They are cheaper than inverters and frequency converters and allow for temporal reduction of the torque and current surge during start-up, as well as smooth deceleration. Therefore they can also be used in electric vehicles to control auxiliary machines (pumps, fans, air coolers, compressors, etc.. The present paper presents a methodology for their design and setting up.

  14. Influence of Chatter of VMC Arising During End Milling Operation and Cutting Conditions on Quality of Machined Surface

    Directory of Open Access Journals (Sweden)

    A.K.M.N. Amin, M.A. Rizal, and M. Razman

    2012-08-01

    Full Text Available Machine tool chatter is a dynamic instability of the cutting process. Chatter results in poor part surface finish, damaged cutting tool, and an irritating and unacceptable noise. Exten¬sive research has been undertaken to study the mechanisms of chatter formation. Efforts have been also made to prevent the occurrence of chatter vibration. Even though some progress have been made, fundamental studies on the mechanics of metal cutting are necessary to achieve chatter free operation of CNC machine tools to maintain their smooth operating cycle. The same is also true for Vertical Machining Centres (VMC, which operate at high cutting speeds and are capable of offering high metal removal rates. The present work deals with the effect of work materials, cutting conditions and diameter of end mill cutters on the frequency-amplitude characteristics of chatter and on machined surface roughness. Vibration data were recorded using an experimental rig consisting of KISTLER 3-component dynamometer model 9257B, amplifier, scope meters and a PC.  Three different types of vibrations were observed. The first type was a low frequency vibration, associated with the interrupted nature of end mill operation. The second type of vibration was associated with the instability of the chip formation process and the third type was due to chatter. The frequency of the last type remained practically unchanged over a wide range of cutting speed.  It was further observed that chip-tool contact processes had considerable effect on the roughness of the machined surface.Key Words: Chatter, Cutting Conditions, Stable Cutting, Surface Roughness.

  15. Evaluation of service conditions of the machines within the furniture joinery

    Directory of Open Access Journals (Sweden)

    Łukasz Zalejski

    2015-12-01

    Full Text Available The aim of this thesis is to present the operation of machines within the furniture joinery, presentation of the risks for this branch, discussion of quality and technical documentation within the analysed enterprise, characteristics of the Total Productive Maintenance (TPM rate, evaluation of the level of materialized technology using Parker's scale.

  16. Evaluation of service conditions of the machines within the furniture joinery

    OpenAIRE

    Łukasz Zalejski; Noga Mariusz; Wojtysiak Marcin; Foszmanowicz Krzysztof

    2015-01-01

    The aim of this thesis is to present the operation of machines within the furniture joinery, presentation of the risks for this branch, discussion of quality and technical documentation within the analysed enterprise, characteristics of the Total Productive Maintenance (TPM) rate, evaluation of the level of materialized technology using Parker's scale.

  17. Elevating Virtual Machine Introspection for Fine-Grained Process Monitoring: Techniques and Applications

    Science.gov (United States)

    Srinivasan, Deepa

    2013-01-01

    Recent rapid malware growth has exposed the limitations of traditional in-host malware-defense systems and motivated the development of secure virtualization-based solutions. By running vulnerable systems as virtual machines (VMs) and moving security software from inside VMs to the outside, the out-of-VM solutions securely isolate the anti-malware…

  18. Structural health monitoring for bolt loosening via a non-invasive vibro-haptics human-machine cooperative interface

    Science.gov (United States)

    Pekedis, Mahmut; Mascerañas, David; Turan, Gursoy; Ercan, Emre; Farrar, Charles R.; Yildiz, Hasan

    2015-08-01

    For the last two decades, developments in damage detection algorithms have greatly increased the potential for autonomous decisions about structural health. However, we are still struggling to build autonomous tools that can match the ability of a human to detect and localize the quantity of damage in structures. Therefore, there is a growing interest in merging the computational and cognitive concepts to improve the solution of structural health monitoring (SHM). The main object of this research is to apply the human-machine cooperative approach on a tower structure to detect damage. The cooperation approach includes haptic tools to create an appropriate collaboration between SHM sensor networks, statistical compression techniques and humans. Damage simulation in the structure is conducted by releasing some of the bolt loads. Accelerometers are bonded to various locations of the tower members to acquire the dynamic response of the structure. The obtained accelerometer results are encoded in three different ways to represent them as a haptic stimulus for the human subjects. Then, the participants are subjected to each of these stimuli to detect the bolt loosened damage in the tower. Results obtained from the human-machine cooperation demonstrate that the human subjects were able to recognize the damage with an accuracy of 88 ± 20.21% and response time of 5.87 ± 2.33 s. As a result, it is concluded that the currently developed human-machine cooperation SHM may provide a useful framework to interact with abstract entities such as data from a sensor network.

  19. Development of condition monitoring and diagnosis system for standby diesel generator

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Kwang Hee; Park, Jong Hyuck; Park, Jong Eun [Korea Electric Power Research Institute, Daejeon (Korea, Republic of)

    2009-05-15

    The emergency diesel generator (EDG) of the nuclear power plant is designed to supply the power to the nuclear on Station Black Out (SBO) condition. The operation reliability of onsite emergency diesel generator should be ensured by a condition monitoring system designed to monitor and analysis the condition of diesel generator. For this purpose, we have developed the online condition monitoring and diagnosis system for the wolsong unit 3 and 4 standby diesel generator including diesel engine performance. In this paper, technologies of condition monitoring and diagnosis system (SDG MDS) for the wolsong standby diesel generator are described. By using the condition monitoring module of the SDG MDS, performance monitoring function for major operating parameters of EDG reliability program required by Reg. guide 1.155 can be operated as on line monitoring system.

  20. Development of condition monitoring and diagnosis system for standby diesel generator

    International Nuclear Information System (INIS)

    Choi, Kwang Hee; Park, Jong Hyuck; Park, Jong Eun

    2009-01-01

    The emergency diesel generator (EDG) of the nuclear power plant is designed to supply the power to the nuclear on Station Black Out (SBO) condition. The operation reliability of onsite emergency diesel generator should be ensured by a condition monitoring system designed to monitor and analysis the condition of diesel generator. For this purpose, we have developed the online condition monitoring and diagnosis system for the wolsong unit 3 and 4 standby diesel generator including diesel engine performance. In this paper, technologies of condition monitoring and diagnosis system (SDG MDS) for the wolsong standby diesel generator are described. By using the condition monitoring module of the SDG MDS, performance monitoring function for major operating parameters of EDG reliability program required by Reg. guide 1.155 can be operated as on line monitoring system

  1. Autonomous Scanning Probe Microscopy in Situ Tip Conditioning through Machine Learning.

    Science.gov (United States)

    Rashidi, Mohammad; Wolkow, Robert A

    2018-05-23

    Atomic-scale characterization and manipulation with scanning probe microscopy rely upon the use of an atomically sharp probe. Here we present automated methods based on machine learning to automatically detect and recondition the quality of the probe of a scanning tunneling microscope. As a model system, we employ these techniques on the technologically relevant hydrogen-terminated silicon surface, training the network to recognize abnormalities in the appearance of surface dangling bonds. Of the machine learning methods tested, a convolutional neural network yielded the greatest accuracy, achieving a positive identification of degraded tips in 97% of the test cases. By using multiple points of comparison and majority voting, the accuracy of the method is improved beyond 99%.

  2. Automated system of monitoring and positioning of functional units of mining technological machines for coal-mining enterprises

    Directory of Open Access Journals (Sweden)

    Meshcheryakov Yaroslav

    2018-01-01

    Full Text Available This article is show to the development of an automated monitoring and positioning system for functional nodes of mining technological machines. It describes the structure, element base, algorithms for identifying the operating states of a walking excavator; various types of errors in the functioning of microelectromechanical gyroscopes and accelerometers, as well as methods for their correction based on the Madgwick fusion filter. The results of industrial tests of an automated monitoring and positioning system for functional units on one of the opencast coal mines of Kuzbass are presented. This work is addressed to specialists working in the fields of the development of embedded systems and control systems, radio electronics, mechatronics, and robotics.

  3. A Machine-Learning Approach to Predict Main Energy Consumption under Realistic Operational Conditions

    DEFF Research Database (Denmark)

    Petersen, Joan P; Winther, Ole; Jacobsen, Daniel J

    2012-01-01

    The paper presents a novel and publicly available set of high-quality sensory data collected from a ferry over a period of two months and overviews exixting machine-learning methods for the prediction of main propulsion efficiency. Neural networks are applied on both real-time and predictive...... settings. Performance results for the real-time models are shown. The presented models were successfully developed in a trim optimisation application onboard a product tanker....

  4. Investigation of Permanent Magnet Demagnetization in Synchronous Machines during Multiple Short-Circuit Fault Conditions

    Directory of Open Access Journals (Sweden)

    Stefan Sjökvist

    2017-10-01

    Full Text Available Faults in electrical machines can vary in severity and affect different parts of the machine. This study focuses on various kinds of short-circuits on the terminal side of a generic 20 kW surface mounted permanent magnet synchronous generator and how successive faults affect the performance of the machine. The study was conducted with the commercially available finite element method software COMSOL Multiphysics ® , and two time-dependent models for demagnetization of permanent magnets were compared, one using only internal models and the other using a proprietary external function. The study is simulation based and the two models were compared to a previously experimentally verified stationary model. Results showed that the power output decreased by more than 30% after five successive faults. In addition, the no-load voltage had become unsymmetrical, which was explained by the uneven demagnetization of the permanent magnets. The permanent magnet with the lowest reduction in average remanence was decreased by 0.8%, while the highest average reduction was 23.8% in another permanent magnet. The internal simulation model was about four times faster than the external model, but slightly overestimated the demagnetization.

  5. Commonality and Variability Analysis for Xenon Family of Separation Virtual Machine Monitors (CVAX)

    Science.gov (United States)

    2017-07-18

    the sponsor (e.g., military, intelligence community, other government, commercial, medical ) and upon the type of system (e.g., application in the...loads. • Machine memory. Xen’s terminology for hardware memory present on a chip. • Misuse case. Abuse case. Attacker-product interaction that the...on connections between domains. • Physical memory. Xen’s terminology , short for pseudo-physical memory. Physical memory is the Xen term for the

  6. Condition monitoring and signature analysis techniques as applied to Madras Atomic Power Station (MAPS) [Paper No.: VIA - 1

    International Nuclear Information System (INIS)

    Rangarajan, V.; Suryanarayana, L.

    1981-01-01

    The technique of vibration signature analysis for identifying the machine troubles in their early stages is explained. The advantage is that a timely corrective action can be planned to avoid breakdowns and unplanned shutdowns. At the Madras Atomic Power Station (MAPS), this technique is applied to regularly monitor vibrations of equipment and thus is serving as a tool for doing corrective maintenance of equipment. Case studies of application of this technique to main boiler feed pumps, moderation pump motors, centrifugal chiller, ventilation system fans, thermal shield ventilation fans, filtered water pumps, emergency process sea water pumps, and antifriction bearings of MAPS are presented. Condition monitoring during commissioning and subsequent operation could indicate defects. Corrective actions which were taken are described. (M.G.B.)

  7. Design, development and test of the gearbox condition monitoring system using sound signal processing

    Directory of Open Access Journals (Sweden)

    M Zamani

    2016-09-01

    Full Text Available Introduction One of the ways used for minimizing the cost of maintenance and repairs of rotating industrial equipment is condition monitoring using acoustic analysis. One of the most important problems which always have been under consideration in industrial equipment application is confidence possibility. Each dynamic, electrical, hydraulic or thermal system has certain characteristics which show the normal condition of the machine during function. Any changes of the characteristics can be a signal of a problem in the machine. The aim of condition monitoring is system condition determination using measurements of the signals of characteristics and using this information for system impairment prognostication. There are a lot of ways for condition monitoring of different systems, but sound analysis is accepted and used extensively as a method for condition investigation of rotating machines. The aim of this research is the design and construction of considered gearbox and using of obtaining data in frequency and time spectrum in order to analyze the sound and diagnosis. Materials and Methods This research was conducted at the department of mechanical biosystem workshop at Aboureihan College at Tehran University in February 15th.2015. In this research, in order to investigate the trend of diagnosis and gearbox condition, a system was designed and then constructed. The sound of correct and damaged gearbox was investigated by audiometer and stored in computer for data analysis. Sound measurement was done in three pinions speed of 749, 1050 and 1496 rpm and for correct gearboxes, damage of the fracture of a tooth and a tooth wear. Gearbox design and construction: In order to conduct the research, a gearbox with simple gearwheels was designed according to current needs. Then mentioned gearbox and its accessories were modeled in CATIA V5-R20 software and then the system was constructed. Gearbox is a machine that is used for mechanical power transition

  8. Remediating radium contaminated legacy sites: Advances made through machine learning in routine monitoring of “hot” particles

    Energy Technology Data Exchange (ETDEWEB)

    Varley, Adam, E-mail: a.l.varley@stir.ac.uk [Department of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA (United Kingdom); Tyler, Andrew, E-mail: a.n.tyler@stir.ac.uk [Department of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA (United Kingdom); Smith, Leslie, E-mail: l.s.smith@cs.stir.ac.uk [Department of Computing Science and Mathematics, University of Stirling, Stirling FK9 4LA (United Kingdom); Dale, Paul, E-mail: paul.dale@sepa.org.uk [Scottish Environmental Protection Agency, Radioactive Substances, Strathallan House, Castle Business Park, Stirling FK9 4TZ (United Kingdom); Davies, Mike, E-mail: Mike.Davies@nuvia.co.uk [Nuvia Limited, The Library, Eight Street, Harwell Oxford, Didcot, Oxfordshire OX11 0RL (United Kingdom)

    2015-07-15

    The extensive use of radium during the 20th century for industrial, military and pharmaceutical purposes has led to a large number of contaminated legacy sites across Europe and North America. Sites that pose a high risk to the general public can present expensive and long-term remediation projects. Often the most pragmatic remediation approach is through routine monitoring operating gamma-ray detectors to identify, in real-time, the signal from the most hazardous heterogeneous contamination (hot particles); thus facilitating their removal and safe disposal. However, current detection systems do not fully utilise all spectral information resulting in low detection rates and ultimately an increased risk to the human health. The aim of this study was to establish an optimised detector-algorithm combination. To achieve this, field data was collected using two handheld detectors (sodium iodide and lanthanum bromide) and a number of Monte Carlo simulated hot particles were randomly injected into the field data. This allowed for the detection rate of conventional deterministic (gross counts) and machine learning (neural networks and support vector machines) algorithms to be assessed. The results demonstrated that a Neural Network operated on a sodium iodide detector provided the best detection capability. Compared to deterministic approaches, this optimised detection system could detect a hot particle on average 10 cm deeper into the soil column or with half of the activity at the same depth. It was also found that noise presented by internal contamination restricted lanthanum bromide for this application. - Highlights: • Land contaminated with radium is hazardous to human health. • Routine monitoring permits identification and removal of radioactive hot particles. • Current alarm algorithms do not provide reliable hot particle detection. • Spectral processing using Machine Learning significantly improves detection.

  9. Remediating radium contaminated legacy sites: Advances made through machine learning in routine monitoring of “hot” particles

    International Nuclear Information System (INIS)

    Varley, Adam; Tyler, Andrew; Smith, Leslie; Dale, Paul; Davies, Mike

    2015-01-01

    The extensive use of radium during the 20th century for industrial, military and pharmaceutical purposes has led to a large number of contaminated legacy sites across Europe and North America. Sites that pose a high risk to the general public can present expensive and long-term remediation projects. Often the most pragmatic remediation approach is through routine monitoring operating gamma-ray detectors to identify, in real-time, the signal from the most hazardous heterogeneous contamination (hot particles); thus facilitating their removal and safe disposal. However, current detection systems do not fully utilise all spectral information resulting in low detection rates and ultimately an increased risk to the human health. The aim of this study was to establish an optimised detector-algorithm combination. To achieve this, field data was collected using two handheld detectors (sodium iodide and lanthanum bromide) and a number of Monte Carlo simulated hot particles were randomly injected into the field data. This allowed for the detection rate of conventional deterministic (gross counts) and machine learning (neural networks and support vector machines) algorithms to be assessed. The results demonstrated that a Neural Network operated on a sodium iodide detector provided the best detection capability. Compared to deterministic approaches, this optimised detection system could detect a hot particle on average 10 cm deeper into the soil column or with half of the activity at the same depth. It was also found that noise presented by internal contamination restricted lanthanum bromide for this application. - Highlights: • Land contaminated with radium is hazardous to human health. • Routine monitoring permits identification and removal of radioactive hot particles. • Current alarm algorithms do not provide reliable hot particle detection. • Spectral processing using Machine Learning significantly improves detection

  10. Technical Needs for Enhancing Risk Monitors with Equipment Condition Assessment for Advanced Small Modular Reactors

    Energy Technology Data Exchange (ETDEWEB)

    Coble, Jamie B.; Coles, Garill A.; Ramuhalli, Pradeep; Meyer, Ryan M.; Berglin, Eric J.; Wootan, David W.; Mitchell, Mark R.

    2013-04-04

    Advanced small modular reactors (aSMRs) can provide the United States with a safe, sustainable, and carbon-neutral energy source. The controllable day-to-day costs of aSMRs are expected to be dominated by operation and maintenance costs. Health and condition assessment coupled with online risk monitors can potentially enhance affordability of aSMRs through optimized operational planning and maintenance scheduling. Currently deployed risk monitors are an extension of probabilistic risk assessment (PRA). For complex engineered systems like nuclear power plants, PRA systematically combines event likelihoods and the probability of failure (POF) of key components, so that when combined with the magnitude of possible adverse consequences to determine risk. Traditional PRA uses population-based POF information to estimate the average plant risk over time. Currently, most nuclear power plants have a PRA that reflects the as-operated, as-modified plant; this model is updated periodically, typically once a year. Risk monitors expand on living PRA by incorporating changes in the day-by-day plant operation and configuration (e.g., changes in equipment availability, operating regime, environmental conditions). However, population-based POF (or population- and time-based POF) is still used to populate fault trees. Health monitoring techniques can be used to establish condition indicators and monitoring capabilities that indicate the component-specific POF at a desired point in time (or over a desired period), which can then be incorporated in the risk monitor to provide a more accurate estimate of the plant risk in different configurations. This is particularly important for active systems, structures, and components (SSCs) proposed for use in aSMR designs. These SSCs may differ significantly from those used in the operating fleet of light-water reactors (or even in LWR-based SMR designs). Additionally, the operating characteristics of aSMRs can present significantly different

  11. Systems and method for lagrangian monitoring of flooding conditions

    KAUST Repository

    Claudel, Christian G.; Shamim, Atif; Farooqui, Muhammad Fahad

    2015-01-01

    A traffic monitoring system and method for mapping traffic speed and density while preserving privacy. The system can include fixed stations that make up a network and mobile probes that are associated with vehicles. The system and method do

  12. Structural damage monitoring of harbor caissons with interlocking condition

    Energy Technology Data Exchange (ETDEWEB)

    Huynh, Thanh Canh; Lee, So Young; Nauyen, Khac Duy; Kim, Jeong Tae [Pukyong National Univ., Busan (Korea, Republic of)

    2012-12-15

    The objective of this study is to monitor the health status of harbor caissons which have potential foundation damage. To obtain the objective, the following approaches are performed. Firstly, a structural damage monitoring(SDM) method is designed for interlocked multiple caisson structures. The SDM method utilizes the change in modal strain energy to monitor the foundation damage in a target caisson unit. Secondly, a finite element model of a caisson system which consists of three caisson units is established to verify the feasibility of the proposed method. In the finite element simulation, the caisson units are constrained each other by shear key connections. The health status of the caisson system against various levels of foundation damage is monitored by measuring relative modal displacements between the adjacent caissons.

  13. Structural damage monitoring of harbor caissons with interlocking condition

    International Nuclear Information System (INIS)

    Huynh, Thanh Canh; Lee, So Young; Nauyen, Khac Duy; Kim, Jeong Tae

    2012-01-01

    The objective of this study is to monitor the health status of harbor caissons which have potential foundation damage. To obtain the objective, the following approaches are performed. Firstly, a structural damage monitoring(SDM) method is designed for interlocked multiple caisson structures. The SDM method utilizes the change in modal strain energy to monitor the foundation damage in a target caisson unit. Secondly, a finite element model of a caisson system which consists of three caisson units is established to verify the feasibility of the proposed method. In the finite element simulation, the caisson units are constrained each other by shear key connections. The health status of the caisson system against various levels of foundation damage is monitored by measuring relative modal displacements between the adjacent caissons

  14. Integrating statistical machine learning in a semantic sensor web for proactive monitoring and control

    CSIR Research Space (South Africa)

    Adeleke, Jude Adekunle

    2017-04-01

    Full Text Available in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM 2 . 5 pollution situations is integrated into the proactive monitoring and control framework. Results show...

  15. MONITORING OF THE FINANCIAL CONDITION OF THE COMPANY

    Directory of Open Access Journals (Sweden)

    V. E. Gladkova

    2015-01-01

    Full Text Available Topic: nowadays, many companies are on the market of high competition and are in need of new methods of needs assessment in the market in their products. In this study the methodology of calculation of the breakeven point and its projection of the dynamics of changes in the time lag will allow new businesses to forecast and take into account seasonal fl uctuations in demand for their products.Goals/objectives: the Authors of this publication have set ourselves three main goals: to improve the classical method of determining the breakeven point; to identify the dynamics and patterns of basic mathematical relations that determine the interdependence between the volume of sales (income and total costs; the possibility of applying this methodology economists in production to implement predict the future costs of production.Methodology: the Authors used the conventional scientifi c approaches and methods to analyse and identify mathematical relationships that take into account the specifi c economic and industry conditions and can be further used as template functions to predict the break-even point at a certain time lag.Results: the study authors derived a mathematical relation of volume of sales and total costs, which allows the maximization of the profi ts of Russian companies [1, 2].Discussion/application (if any: explores options graphs break-even point. Revealed that some products have a life cycle with two break-even point (at the fi rst point shows the future profi tability of the enterprise, and in the second point shows the beginning of a losing period and the need to remove product from production.Conclusions/signifi cance: Further research allowed for the monitoring of break-even point in time for which the article demonstrates the possibility of charting the break-even point in three-dimensional space that allows you to track the profi tability of the enterprise and to avoid a possible bankruptcy at a certain time lag.

  16. PROBLEMS OF CREATION THE MONITORING SYSTEM CONCERNING THE CONDITION OF INFORMATIZATION OF THE GENERAL EDUCATION INSTITUTIONS

    Directory of Open Access Journals (Sweden)

    Valeriy Yu. Bykov

    2010-08-01

    Full Text Available In the article the problems, which appear under the creation of monitoring systems concerning the condition of informatization of general educational institutions, such as definition of monitoring object and list of parameters that will be traced during the monitoring, technologies of obtaining and actualization of data parameters, that are to be monitored, formats of data submission and ways of its processing, monitoring time period etc. are considered. In the article some decision of these problems are offered. Here is also mentioned the data of some characteristics and possibilities of the creation of monitoring systems concerning the condition of informatization of general educational institutions in Ukraine.

  17. Investigation and Evaluation on Influence of Machining (CNC Conditions on Surface Quality of Paulownia Wood

    Directory of Open Access Journals (Sweden)

    Mohammad Aghajani

    2012-01-01

    Full Text Available The aim of this study was to investigate the effective factors on surface quality of paulownia wood during machining by advanced computer numerical controled (CNC machine. For this aim paulownia logs were provided and were converted to proper sizes (2.5 x 10 x 15 cm and then air dried. The Variable of this study were cutting speed (8.37 and 15.07 m/s, feeding rate (6 and 12 m/min, cutting depth (1and 5 mm, cutting method (down and up-milling and cutting pattern (tangential and radial. Roughness of cut specimens edge were evaluated by profilometer method according to ISO 13565 standard. For evaluation of surface quality, average roughness (Ra, maximum roughness (R max, valley roughness (Rv and peak roughness (Rp were used. Degrees of effectiveness of the parameters were evaluated by fractional factorial design as completely random design at confidence level of 95%. The result showed that cutting speed, cutting method and feed rate are influencive factors on surface quality of machined specimens and their effects were significant. With increasing cutting speed and decreasing feeding rate the roughness decreased and surface quality improved. In up-milling cutting method, degree of roughness was higher and consequently surface quality was inferior. It is to be noted that cutting method in comparison to other factors had the high influence on surface quality. The rest variables did now have independent influence on surface quality at 95% Confidence level. This study for achieving the optimum surface quality recommends that cutting speed of 15.07 m/s, feeding rate of 6 m/min, cutting method of down-milling and cutting depth of 1 mm for tangential cross section.

  18. Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface.

    Science.gov (United States)

    Sakurai, Yoshio; Song, Kichan; Tachibana, Shota; Takahashi, Susumu

    2014-01-01

    In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them.

  19. Real-time personal exposure and health condition monitoring system

    Energy Technology Data Exchange (ETDEWEB)

    Saitou, Isamu; Kanda, Hiroaki; Asai, Akio; Takeishi, Naoki; Ota, Yoshito [Hitachi Aloka Medical, Ltd., Measuring Systems Engineering Dept., Tokyo (Japan); Hanawa, Nobuhiro; Ueda, Hisao; Kusunoki, Tsuyoshi; Ishitsuka, Etsuo; Kawamura, Hiroshi [Japan Atomic Energy Agency, Oarai Research and Development Center, Oarai, Ibaraki (Japan)

    2012-03-15

    JAEA (Japan Atomic Energy Agency) and HAM (Hitachi Aloka Medical, Ltd) have proposed novel monitoring system for workers of nuclear facility. In these facilities, exposure management for workers is mainly used access control and personal exposure recordings. This system is currently only for reports management but is not confirmative for surveillance when work in progress. Therefore, JAEA and HAM integrate access control and personal exposure recordings and two real-time monitoring systems which are position sensing and vital sign monitor. Furthermore change personal exposure management to real-time management, this system integration prevents workers from risk of accidents, and makes possible take appropriate action quickly. This novel system is going to start for tentative operation, using position sensing and real-time personal dosimeter with database in Apr. 2012. (author)

  20. Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage

    NARCIS (Netherlands)

    van der Ster, Björn J. P.; Bennis, Frank C.; Delhaas, Tammo; Westerhof, Berend E.; Stok, Wim J.; van Lieshout, Johannes J.

    2018-01-01

    Introduction: In the initial phase of hypovolemic shock, mean blood pressure (BP) is maintained by sympathetically mediated vasoconstriction rendering BP monitoring insensitive to detect blood loss early. Late detection can result in reduced tissue oxygenation and eventually cellular death. We

  1. Sixth international conference on electrical machines and drives

    International Nuclear Information System (INIS)

    1993-01-01

    This volume contains 111 papers presented at the Sixth International Conference on Electrical Machines and Drives. The topics covered include: miniature and micro motors; induction motors; DC machines; reluctance motors; condition monitoring; synchronous machines and drives; induction machines; induction generators; simulation; design; and operating experience; linear machines; noise and vibration; special machines. Separate abstracts have been prepared for a paper on linear step motors for control rod drives and for a paper on a motor drive for gas filtration in gas-cooled reactors. (UK)

  2. A study on the condition monitoring for safety-related electric cables

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Chul Hwan; Ahn, S. P.; Yeo, S. M.; Kang, Y. S.; Ahn, S. M.; Kim, I. S.; Kim, D. S.; Kang, J. S. [Sungkyunkwan Univ., Suwon (Korea, Republic of)

    2002-03-15

    In this report, we have studied compositions and characteristics of various types of insulation material for cables in Nuclear Power Plant. We arrange relationship with condition monitoring methods. Also, we propose new condition monitoring method using third harmonic frequency. We test the proposed method with CV cables. We also describe about feature of condition monitoring such as application, theory, characteristic, thereby other engineer can confirm to advantage and disadvantage for each method, and possibly choice adequate condition monitoring method for various types of cables.

  3. Systems and method for lagrangian monitoring of flooding conditions

    KAUST Repository

    Claudel, Christian G.

    2015-12-17

    A traffic monitoring system and method for mapping traffic speed and density while preserving privacy. The system can include fixed stations that make up a network and mobile probes that are associated with vehicles. The system and method do not gather, store, or transmit any unique or identifying information, and thereby preserves the privacy of members of traffic. The system and method provide real-time traffic density and speed mapping. The system and method can further be integrated with a complementary flood monitoring system and method.

  4. Enhanced way of securing automated teller machine to track the misusers using secure monitor tracking analysis

    Science.gov (United States)

    Sadhasivam, Jayakumar; Alamelu, M.; Radhika, R.; Ramya, S.; Dharani, K.; Jayavel, Senthil

    2017-11-01

    Now a days the people's attraction towards Automated Teller Machine(ATM) has been increasing even in rural areas. As of now the security provided by all the bank is ATM pin number. Hackers know the way to easily identify the pin number and withdraw money if they haven stolen the ATM card. Also, the Automated Teller Machine is broken and the money is stolen. To overcome these disadvantages, we propose an approach “Automated Secure Tracking System” to secure and tracking the changes in ATM. In this approach, while creating the bank account, the bank should scan the iris known (a part or movement of our eye) and fingerprint of the customer. The scanning can be done with the position of the eye movements and fingerprints identified with the shortest measurements. When the card is swiped then ATM should request the pin, scan the iris and recognize the fingerprint and then allow the customer to withdraw money. If somebody tries to break the ATM an alert message is given to the nearby police station and the ATM shutter is automatically closed. This helps in avoiding the hackers who withdraw money by stealing the ATM card and also helps the government in identifying the criminals easily.

  5. Wireless sensor network for monitoring soil moisture and weather conditions

    Science.gov (United States)

    A wireless sensor network (WSN) was developed and deployed in three fields to monitor soil water status and collect weather data for irrigation scheduling. The WSN consists of soil-water sensors, weather sensors, wireless data loggers, and a wireless modem. Soil-water sensors were installed at three...

  6. Robot dispatching Scenario for Accident Condition Monitoring of NPP

    International Nuclear Information System (INIS)

    Kim, Jongseog

    2013-01-01

    In March of 2011, unanticipated big size of tsunami attacks Fukushima NPP, this accident results in explosion of containment building. Tokyo electric power of Japan couldn't dispatch a robot for monitoring of containment inside. USA Packbot robot used for desert war in Iraq was supplied to Fukushima NPP for monitoring of high radiation area. Packbot also couldn't reach deep inside of Fukushima NPP due to short length of power cable. Japanese robot 'Queens' also failed to complete a mission due to communication problem between robot and operator. I think major reason of these robot failures is absence of robot dispatching scenario. If there was a scenario and a rehearsal for monitoring during or after accident, these unanticipated obstacles could be overcome. Robot dispatching scenario studied for accident of nuclear power plant was described herein. Study on scenario of robot dispatching is performed. Flying robot is regarded as good choice for accident monitoring. Walking robot with arm equipped is good for emergency valve close. Short time work and shift work by several robots can be a solution for high radiation area. Thin and soft cable with rolling reel can be a good solution for long time work and good communication

  7. Monitoring operational conditions of vehicle tyre pressure levels and ...

    African Journals Online (AJOL)

    Compliance with vehicle tyre inflation pressure and tread depth standard specifications and legal requirements were monitored by survey study in Kumasi Metropolis, Ghana. The survey covered 400 vehicles, comprising cars (28 %), medium buses (25 %), large capacity buses (15 %) and trucks (32 %). There were wide ...

  8. Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine

    Energy Technology Data Exchange (ETDEWEB)

    Xing, Zhanqiang; Qu, Jianfeng; Chai, Yi; Tang, Qiu; Zhou, Yuming [Chongqing University, Chongqing (China)

    2017-02-15

    The gear vibration signal is nonlinear and non-stationary, gear fault diagnosis under variable conditions has always been unsatisfactory. To solve this problem, an intelligent fault diagnosis method based on Intrinsic time-scale decomposition (ITD)-Singular value decomposition (SVD) and Support vector machine (SVM) is proposed in this paper. The ITD method is adopted to decompose the vibration signal of gearbox into several Proper rotation components (PRCs). Subsequently, the singular value decomposition is proposed to obtain the singular value vectors of the proper rotation components and improve the robustness of feature extraction under variable conditions. Finally, the Support vector machine is applied to classify the fault type of gear. According to the experimental results, the performance of ITD-SVD exceeds those of the time-frequency analysis methods with EMD and WPT combined with SVD for feature extraction, and the classifier of SVM outperforms those for K-nearest neighbors (K-NN) and Back propagation (BP). Moreover, the proposed approach can accurately diagnose and identify different fault types of gear under variable conditions.

  9. The machined surface of magnesium AZ31 after rotary turning at air cooling condition

    Science.gov (United States)

    Akhyar, G.; Purnomo, B.; Hamni, A.; Harun, S.; Burhanuddin, Y.

    2018-04-01

    Magnesium is a lightweight metal that is widely used as an alternative to iron and steel. Magnesium has been applied in the automotive industry to reduce the weight of a component, but the machining process has the disadvantage that magnesium is highly flammable because it has a low flash point. High temperature can cause the cutting tool wear and contributes to the quality of the surface roughness. The purpose of this study is to obtain the value of surface roughness and implement methods of rotary cutting tool and air cooling output vortex tube cooler to minimize the surface roughness values. Machining parameters that is turning using rotary cutting tool at speed the workpiece of (Vw) 50, 120, 160 m/min, cutting speed of rotary tool of (Vt) 25, 50, 75 m/min, feed rate of (f) 0.1, 0.15, 0.2 mm/rev, and depth of cut of 0.3 mm. Type of tool used is a carbide tool diameter of 16 mm and air cooling pressure of 6 bar. The results show the average value of the lowest surface roughness on the speed the workpiece of 80 m/min, cutting speed of rotary tool of 50 m/min, feed rate of 0.2 mm/rev, and depth of cut of 0.3 mm. While the average value of the highest surface roughness on the speed the workpiece of 160 m/min, cutting speed of rotary tool of 50 m/min, feed rate of 0.2 mm/rev, and depth of cut of 0.3 mm. The influence of machining parameters concluded the higher the speed of the workpiece the surface roughness value higher. Otherwise the higher cutting speed of rotary tool then the lower the surface roughness value. The observation on the surface of the rotary tool, it was found that no uniform tool wear which causes non-uniform surface roughness. The use of rotary cutting tool contributing to lower surface roughness values generated.

  10. DIAGNOSTICS OF WORKPIECE SURFACE CONDITION BASED ON CUTTING TOOL VIBRATIONS DURING MACHINING

    Directory of Open Access Journals (Sweden)

    Jerzy Józwik

    2015-05-01

    Full Text Available The paper presents functional relationships between surface geometry parameters, feed and vibrations level in the radial direction of the workpiece. Time characteristics of the acceleration of cutting tool vibration registered during C45 steel and stainless steel machining for separate axes (X, Y, Z were presented as a function of feedrate f. During the tests surface geometric accuracy assessment was performed and 3D surface roughness parameters were determined. The Sz parameter was selected for the analysis, which was then collated with RMS vibration acceleration and feedrate f. The Sz parameter indirectly provides information on peak to valley height and is characterised by high generalising potential i.e. it is highly correlated to other surface and volume parameters of surface roughness. Test results presented in this paper may constitute a valuable source of information considering the influence of vibrations on geometric accuracy of elements for engineers designing technological processes.

  11. Intelligent condition monitoring of railway catenary systems : A Bayesian Network approach

    NARCIS (Netherlands)

    Wang, H.; Nunez Vicencio, Alfredo; Dollevoet, R.P.B.J.; Liu, Zhigang; Chen, Junwen; Spiryagin, Maksym; Gordon, Timothy; Cole, Colin; McSweeney, Tim

    2017-01-01

    This study proposes a Bayesian network (BN) dedicated for the intelligent condition monitoring of railway catenary systems. It combines five types of measurements related to catenary condition, namely the contact wire stagger, contact wire height, pantograph head displacement, pantograph head

  12. Monitoring parameters of technical condition and safety of aircraft using control charts

    Directory of Open Access Journals (Sweden)

    В.І. Чепіженко

    2007-03-01

    Full Text Available  The opportunity of control cards use for monitoring of a technical condition parameters and reliability of aviation techniques is considered at its operation on a technical condition.

  13. Non Invasive Sensors for Monitoring the Efficiency of AC Electrical Rotating Machines

    Directory of Open Access Journals (Sweden)

    Thierry Jacq

    2010-08-01

    Full Text Available This paper presents a non invasive method for estimating the energy efficiency of induction motors used in industrial applications. This method is innovative because it is only based on the measurement of the external field emitted by the motor. The paper describes the sensors used, how they should be placed around the machine in order to decouple the external field components generated by both the air gap flux and the winding end-windings. The study emphasizes the influence of the eddy currents flowing in the yoke frame on the sensor position. A method to estimate the torque from the external field use is proposed. The measurements are transmitted by a wireless module (Zig-Bee and they are centralized and stored on a PC computer.

  14. Non Invasive Sensors for Monitoring the Efficiency of AC Electrical Rotating Machines

    Science.gov (United States)

    Zidat, Farid; Lecointe, Jean-Philippe; Morganti, Fabrice; Brudny, Jean-François; Jacq, Thierry; Streiff, Frédéric

    2010-01-01

    This paper presents a non invasive method for estimating the energy efficiency of induction motors used in industrial applications. This method is innovative because it is only based on the measurement of the external field emitted by the motor. The paper describes the sensors used, how they should be placed around the machine in order to decouple the external field components generated by both the air gap flux and the winding end-windings. The study emphasizes the influence of the eddy currents flowing in the yoke frame on the sensor position. A method to estimate the torque from the external field use is proposed. The measurements are transmitted by a wireless module (Zig-Bee) and they are centralized and stored on a PC computer. PMID:22163631

  15. Study on sampling conditions for the monitoring of waste air

    International Nuclear Information System (INIS)

    Moeller, T.J.; Buetefisch, K.A.

    1998-01-01

    The technical codes for radiological monitoring of the waste air released from a radwaste repository demand that sampling for determination of aerosol-borne radioactivity is to be made with a screener equipped with a suitable number of measuring probes extending over the entire cross-sectional surface of the vent. Another requirement is to ensure that the waste air stream passing through the measuring channel is representative, containing the typical, operation-induced distribution of aerosols across the surface to be scanned. The study reported was intended to determine in a scaled-down model (1:10) of a repository ventilating duct the typical spatial distribution of aerosols (3D particulate density) in order to establish information on the type of typical distributions of aerosols, to be used for optimisation of the measuring site and monitoring instruments. (orig./CB) [de

  16. Condition Monitoring of a Process Filter Applying Wireless Vibration Analysis

    Directory of Open Access Journals (Sweden)

    Pekka KOSKELA

    2011-05-01

    Full Text Available This paper presents a novel wireless vibration-based method for monitoring the degree of feed filter clogging. In process industry, these filters are applied to prevent impurities entering the process. During operation, the filters gradually become clogged, decreasing the feed flow and, in the worst case, preventing it. The cleaning of the filter should therefore be carried out predictively in order to avoid equipment damage and unnecessary process downtime. The degree of clogging is estimated by first calculating the time domain indices from low frequency accelerometer samples and then taking the median of the processed values. Nine different statistical quantities are compared based on the estimation accuracy and criteria for operating in resource-constrained environments with particular focus on energy efficiency. The initial results show that the method is able to detect the degree of clogging, and the approach may be applicable to filter clogging monitoring.

  17. Upgraded Fast Beam Conditions Monitor for CMS online luminosity measurement

    CERN Document Server

    Leonard, Jessica Lynn

    2014-01-01

    The CMS beam and radiation monitoring subsystem BCM1F during LHC Run I consisted of 8 individual diamond sensors situated around the beam pipe within the tracker detector volume, for the purpose of fast monitoring of beam background and collision products. Effort is ongoing to develop the use of BCM1F as an online bunch-by-bunch luminosity monitor. BCM1F will be running whenever there is beam in LHC, and its data acquisition is independent from the data acquisition of the CMS detector, hence it delivers luminosity even when CMS is not taking data. To prepare for the expected increase in the LHC luminosity and the change from 50 ns to 25 ns bunch separation, several changes to the system are required, including a higher number of sensors and upgraded electronics. In particular, a new real-time digitizer with large memory was developed and is being integrated into a multi-subsystem framework for luminosity measurement. Current results from Run II preparation will be shown, including results from the January 201...

  18. Recent trends in the condition monitoring of transformers theory, implementation and analysis

    CERN Document Server

    Chakravorti, Sivaji; Chatterjee, Biswendu

    2013-01-01

    Recent Trends in the Condition Monitoring of Transformers reflects the current interest in replacing traditional techniques used in power transformer condition monitoring with non-invasive measures such as polarization/depolarization current measurement, recovery voltage measurement, frequency domain spectroscopy and frequency response analysis. The book stresses the importance of scrutinizing the condition of transformer insulation which may fail under present day conditions of intensive use with the resulting degradation of dielectric properties causing functional failure of the transformer.

  19. Instantaneous angular speed monitoring of gearboxes under non-cyclic stationary load conditions

    Science.gov (United States)

    Stander, C. J.; Heyns, P. S.

    2005-07-01

    Recent developments in the condition monitoring and asset management market have led to the commercialisation of online vibration-monitoring systems. These systems are primarily utilised to monitor large mineral mining equipment such as draglines, continuous miners and hydraulic shovels. Online monitoring systems make diagnostic information continuously available for asset management, production outsourcing and maintenance alliances with equipment manufacturers. However, most online vibration-monitoring systems are based on conventional vibration-monitoring technologies, which are prone to giving false equipment deterioration warnings on gears that operate under fluctuating load conditions. A simplified mathematical model of a gear system was developed to illustrate the feasibility of monitoring the instantaneous angular speed (IAS) as a means of monitoring the condition of gears that are subjected to fluctuating load conditions. A distinction is made between cyclic stationary load modulation and non-cyclic stationary load modulation. It is shown that rotation domain averaging will suppress the modulation caused by non-cyclic stationary load conditions but will not suppress the modulation caused by cyclic stationary load conditions. An experimental investigation on a test rig indicated that the IAS of a gear shaft could be monitored with a conventional shaft encoder to indicate a deteriorating gear fault condition.

  20. Accuracy Enhancement with Processing Error Prediction and Compensation of a CNC Flame Cutting Machine Used in Spatial Surface Operating Conditions

    Directory of Open Access Journals (Sweden)

    Shenghai Hu

    2017-04-01

    Full Text Available This study deals with the precision performance of the CNC flame-cutting machine used in spatial surface operating conditions and presents an accuracy enhancement method based on processing error modeling prediction and real-time compensation. Machining coordinate systems and transformation matrix models were established for the CNC flame processing system considering both geometric errors and thermal deformation effects. Meanwhile, prediction and compensation models were constructed related to the actual cutting situation. Focusing on the thermal deformation elements, finite element analysis was used to measure the testing data of thermal errors, the grey system theory was applied to optimize the key thermal points, and related thermal dynamics models were carried out to achieve high-precision prediction values. Comparison experiments between the proposed method and the teaching method were conducted on the processing system after performing calibration. The results showed that the proposed method is valid and the cutting quality could be improved by more than 30% relative to the teaching method. Furthermore, the proposed method can be used under any working condition by making a few adjustments to the prediction and compensation models.

  1. Monitoring fate and behaviour of Nanoceria under relevant environmental conditions

    CSIR Research Space (South Africa)

    Tancu, Y

    2014-11-01

    Full Text Available ). The results revealed significant tendency of nCeO¬2 to undergo aggregation, agglomeration and certain degree of deagglomeration processes under different environmental conditions. Moreover, the findings suggested that both electrostatic and steric interactions...

  2. Analysis of acoustic data from the PFR SGU condition monitor

    International Nuclear Information System (INIS)

    Rowley, R.; Airey, J.

    1990-01-01

    This paper gives an outline description of an acoustic monitoring system which has been installed on the SGU of the Prototype Fast Reactor (PFR) at Dounreay with the objective of giving early warning of any change in noise output which could be related to potentially damaging vibrations within the units. Data obtained from this PFR monitoring system is playing an important part in the development of acoustic instrumentation for leak detection although this had not been the primary objective of this particular installation. The PFR has three secondary circuits each containing an evaporator, a superheater and a reheater giving a total of nine SGUs. Although the design of the units is different from that intended for EFR, the measurements provide a valuable source of information on the character and amplitude of acoustic background noise in operational steam generator units. The vibration monitoring system uses the waveguides originally installed during reactor commissioning for leak detection studies. Twelve acoustic waveguides are fitted to the shell of each of the units. The superheaters and reheaters have three waveguides at each of four axial levels, while the evaporators have four waveguides at each of three axial levels. In addition the evaporators have a small number of waveguides attached to the top flange of the unit. Each waveguide is fitted with an accelerometer to record the acoustic signal from the SGU. Tape recordings of the acoustic noise from each unit are made on a regular basis and the tapes analysed on an automated analysis system which has been developed to extract and store in a database about 20 characteristic features from the data. The paper gives examples of the background noise from the SGU. The data demonstrates the use of location techniques to identify prominent acoustic source. 8 figs

  3. Fundamentals for remote condition monitoring of offshore wind turbines

    DEFF Research Database (Denmark)

    McGugan, Malcolm; Larsen, Gunner Chr.; Sørensen, Bent F.

    In the future, large wind turbines will be placed offshore in considerable numbers. Since access will be difficult and costly, it is preferable to use monitoring systems to reduce the reliance on manual inspection. The motivation for the effort reported here is to create the fundamental basis...... of the wind turbine blades that can integrate with existing SCADA tools to improve management of large offshore wind farms, and optimise the manual inspection/maintenance effort. Various sensor types, which have previously been identified as technically (and economically) capable of detecting the early...

  4. Condition Monitoring for DC-link Capacitors Based on Artificial Neural Network Algorithm

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Wang, Huai; Gadalla, Brwene Salah Abdelkarim

    2015-01-01

    hardware will reduce the cost, and therefore could be more promising for industry applications. A condition monitoring method based on Artificial Neural Network (ANN) algorithm is therefore proposed in this paper. The implementation of the ANN to the DC-link capacitor condition monitoring in a back......In power electronic systems, capacitor is one of the reliability critical components . Recently, the condition monitoring of capacitors to estimate their health status have been attracted by the academic research. Industry applications require more reliable power electronics products...... with preventive maintenance. However, the existing capacitor condition monitoring methods suffer from either increased hardware cost or low estimation accuracy, being the challenges to be adopted in industry applications. New development in condition monitoring technology with software solutions without extra...

  5. On the use of temperature for online condition monitoring of geared systems - A review

    Science.gov (United States)

    Touret, T.; Changenet, C.; Ville, F.; Lalmi, M.; Becquerelle, S.

    2018-02-01

    Gear unit condition monitoring is a key factor for mechanical system reliability management. When they are subjected to failure, gears and bearings may generate excessive vibration, debris and heat. Vibratory, acoustic or debris analyses are proven approaches to perform condition monitoring. An alternative to those methods is to use temperature as a condition indicator to detect gearbox failure. The review focuses on condition monitoring studies which use this thermal approach. According to the failure type and the measurement method, it exists a distinction whether it is contact (e.g. thermocouple) or non-contact temperature sensor (e.g. thermography). Capabilities and limitations of this approach are discussed. It is shown that the use of temperature for condition monitoring has a clear potential as an alternative to vibratory or acoustic health monitoring.

  6. Monitoring stanja kroz testove analize ulja / Condition monitoring through oil analysis tests

    Directory of Open Access Journals (Sweden)

    Sreten R. Perić

    2010-10-01

    : neutralization number (TAN-total acid number, total base number (TBN, oxidation stabillity, chemical and thermal stabillity, corrodibillity, ash content and carbon residue, water content, compatibility, toxicity, etc. Diagnostics of the tribomechanical system of an internal combustion engine The diagnostics is based on the prediction (recognition of damage and/or failure through characteristic diagnostic parameters. This allows prevention of delays and increases reliability, cost-effectiveness, and usage life. The diagnostics of the tribomechanical system can provide verification of the system condition, working capacity and functionality, and can point out the place, form and cause of a failure. The diagnostics is carried out through the detection of symptoms, determining the value of the characteristic parameters and their comparison with the limit values. If the engine assemblies are considered from the aspect of tribomechanical systems (e. g. piston-piston ring-cylinder, cam-valve lifter, bearing journal bearing defined by tribological processes, it can be shown that the determination of the content of wear products, content of contaminants, state of lubricants and lubrication conditions have a significant influence on the implementation of maintenance of these systems. We should emphasize the importance of monitoring oil for lubrication of tribomechanical engine assemblies, which provides identification of potential causes and phenomena leading to damage and failure in the early stages of the functioning of the system. Prediction, i.e. detection of potential and/or current damage and failures in the system, checking the functionality of oil and determination of usage life are the main factors of the implementation of oil monitoring. Since mobile components of tribomechanical system engines are necessarily exposed to wear and contaminants and wear products deposit in the lubrication oil, it is necessary to monitor changes in fluid properties during exploitation, because the

  7. Development of on the machine process monitoring and control strategy in Robot Assisted Polishing

    DEFF Research Database (Denmark)

    Pilny, Lukas; Bissacco, Giuliano

    2015-01-01

    Robot Assisted Polishing (RAP) can be used to polish rotational symmetric and free form components achieving surface roughness down to Sa 10 nm. With the aim to enable unmanned robust and cost efficient application of RAP, this paper presents the development of a monitoring and control strategy....... The multisensory approach was experimentally validated in polishing with bonded abrasives demonstrating its suitability for process control in RAP....

  8. Significance of Operating Environment in Condition Monitoring of Large Civil Structures

    Directory of Open Access Journals (Sweden)

    Sreenivas Alampalli

    1999-01-01

    Full Text Available Success of remote long-term condition monitoring of large civil structures and developing calibrated analytical models for damage detection, depend significantly on establishing accurate baseline signatures and their sensitivity. Most studies reported in the literature concentrated on the effect of structural damage on modal parameters without emphasis on reliability of modal parameters. Thus, a field bridge structure was studied for the significance of operating conditions in relation to baseline signatures. Results indicate that in practice, civil structures should be monitored for at least one full cycle of in-service environmental changes before establishing baselines for condition monitoring or calibrating finite-element models. Boundary conditions deserve special attention.

  9. A Field Programmable Gate Array-Based Reconfigurable Smart-Sensor Network for Wireless Monitoring of New Generation Computer Numerically Controlled Machines

    Directory of Open Access Journals (Sweden)

    Ion Stiharu

    2010-08-01

    Full Text Available Computer numerically controlled (CNC machines have evolved to adapt to increasing technological and industrial requirements. To cover these needs, new generation machines have to perform monitoring strategies by incorporating multiple sensors. Since in most of applications the online Processing of the variables is essential, the use of smart sensors is necessary. The contribution of this work is the development of a wireless network platform of reconfigurable smart sensors for CNC machine applications complying with the measurement requirements of new generation CNC machines. Four different smart sensors are put under test in the network and their corresponding signal processing techniques are implemented in a Field Programmable Gate Array (FPGA-based sensor node.

  10. A Field Programmable Gate Array-Based Reconfigurable Smart-Sensor Network for Wireless Monitoring of New Generation Computer Numerically Controlled Machines

    Science.gov (United States)

    Moreno-Tapia, Sandra Veronica; Vera-Salas, Luis Alberto; Osornio-Rios, Roque Alfredo; Dominguez-Gonzalez, Aurelio; Stiharu, Ion; de Jesus Romero-Troncoso, Rene

    2010-01-01

    Computer numerically controlled (CNC) machines have evolved to adapt to increasing technological and industrial requirements. To cover these needs, new generation machines have to perform monitoring strategies by incorporating multiple sensors. Since in most of applications the online Processing of the variables is essential, the use of smart sensors is necessary. The contribution of this work is the development of a wireless network platform of reconfigurable smart sensors for CNC machine applications complying with the measurement requirements of new generation CNC machines. Four different smart sensors are put under test in the network and their corresponding signal processing techniques are implemented in a Field Programmable Gate Array (FPGA)-based sensor node. PMID:22163602

  11. Improving the monitoring of quantitative conditions of peacetime fuel stocks at pumping stations

    Directory of Open Access Journals (Sweden)

    Slaviša M. Ilić

    2011-04-01

    human resources. Optimization of quantitative monitoring of peacetime supplies of fuel at gas stations should aim at reducing the impact of the human factor, introducing automated quantitative monitoring of fuel condition with modern equipment for handling as well as applying technology for fast reading and dissemination of information and reports. Civilian pumping stations have been modernized gradually with new digital pump machines, systems for automated production and automated systems for measuring the fuel level in buried tanks. The objectives and criteria of the optimization of model monitoring In order to solve the problem of multi-criteria nature, the methods of operational research have been applied and the formalization of problem solving has been carried out. Models have been identified, criteria and subcriteria have been defined as well as respective criteria values, sub-criteria and weight coefficients for chosen variants in order to rank the alternatives - models. On the basis of the defined objectives and optimization approaches, the task of optimization to be solved is to choose one optimal model of monitoring the quantitative condition of peacetime stocks of fuels at gas stations, out of three variations or alternative models. Application of expert assessment and methods of analytical hierarchy process The problem was solved first 'manually', by using MS Excell, and after that by using the Expert Choice software package. The Expert Choice software package is based on the application of the method of analytical hierarchy process and combines the benefits that this method offers with the speed and visibility of computerized calculations and their result display. The purpose of the AHP method is to rank alternative decisions by their importance and to select the most acceptable alternative on the basis of a defined set of criteria and alternatives. The problem of determining the weight of criteria has been determined by applying the method of expert

  12. Condition Monitoring and Fault Diagnosis for an Antifalling Safety Device

    Directory of Open Access Journals (Sweden)

    Guangxiang Yang

    2015-01-01

    Full Text Available There is a constant need for the safe operation and reliability of antifalling safety device (AFSD of an elevator. This paper reports an experimental study on rotation speed and catching torque monitoring and fault diagnosis of an antifalling safety device in a construction elevator. Denoising the signal using wavelet transform is presented in this paper. Based on the denoising effects for several types of wavelets, the sym8 wavelet basis, which introduces the high order approximation and an adaptive threshold, is employed for denoising the signal. The experimental result shows a maximum data error reduction of 7.5% is obtained and SNRs (signal-to-noise ratio of rotation speed and catching torque are improved for 3.9% and 6.4%, respectively.

  13. A global condition monitoring system for wind turbines

    DEFF Research Database (Denmark)

    Schlechtingen, Meik

    the output signal is entirely reconstructed by using other correlated signals. Benefits in fault visibility and lead-time to failure estimatesare observed. A very important signal to monitor contained in the SCADA data is the wind turbine power output. The power output has a direct influence on the revenue...... proposed method to separate discrete (e.g. originating from gears) from random (e.g. originating from bearings) signal components is applied and validated in this research. This state of the art method named“signal pre-whitening” enhances the fault pattern visibility in the envelope spectra in a very...... developed leading to fully automated fault diagnosis. For this purpose a frequency content identifier is developed extracting the frequency content from the envelope spectrum building the basis for automated diagnosis. A modified parameter, namely the Kurtosis of the Amplitude Envelope Spectrum (KEAS...

  14. Time domain spectroscopy to monitor the condition of cable insulation

    International Nuclear Information System (INIS)

    Mopsik, F.I.; Martzloff, F.D.

    1989-01-01

    The use of Time Domain Spectroscopy, the measurement of dielectric constant and loss using time-domain response, the monitoring the aging of reactor cable insulation is examined. The method is presented, showing its sensitivity, accuracy and wide frequency range. The method's ability to acquire a great deal of information in a short time and its superiority to conventional single frequency data is shown. Different cable samples are examined before and after exposure to radiation and changes with exposure are clearly seen to occur. Also it is shown that a wide range of behavior can be found in different insulation systems. The requirements for performing valid measurements is presented. The need for controlled samples and correlation with other criteria for aging is discussed. 14 refs., 9 figs

  15. Physical working conditions as covered in European monitoring questionnaires

    NARCIS (Netherlands)

    Tynes, T.; Aagestad, C.; Vester Thorsen, S.; Andersen, L.L.; Perkio-Makela, M.; García, F.J.P.; Blanco, L..; Vermeylen, G.; Parent-Thirion, A.; Hooftman, W.; Houtman, I.L.D.; Liebers, F.; Burr, H.; Formazin, M.

    2017-01-01

    Background. The prevalence of workers with demanding physical working conditions in the European work force remains high, and occupational physical exposures are considered important risk factors for musculoskeletal disorders (MSD), a major burden for both workers and society. Exposures to physical

  16. Design of Online Monitoring and Fault Diagnosis System for Belt Conveyors Based on Wavelet Packet Decomposition and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Wei Li

    2013-01-01

    Full Text Available Belt conveyors are the equipment widely used in coal mines and other manufacturing factories, whose main components are a number of idlers. The faults of belt conveyors can directly influence the daily production. In this paper, a fault diagnosis method combining wavelet packet decomposition (WPD and support vector machine (SVM is proposed for monitoring belt conveyors with the focus on the detection of idler faults. Since the number of the idlers could be large, one acceleration sensor is applied to gather the vibration signals of several idlers in order to reduce the number of sensors. The vibration signals are decomposed with WPD, and the energy of each frequency band is extracted as the feature. Then, the features are employed to train an SVM to realize the detection of idler faults. The proposed fault diagnosis method is firstly tested on a testbed, and then an online monitoring and fault diagnosis system is designed for belt conveyors. An experiment is also carried out on a belt conveyor in service, and it is verified that the proposed system can locate the position of the faulty idlers with a limited number of sensors, which is important for operating belt conveyors in practices.

  17. Machine Learning

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

  18. An Enhanced Empirical Wavelet Transform for Features Extraction from Wind Turbine Condition Monitoring Signals

    Directory of Open Access Journals (Sweden)

    Pu Shi

    2017-07-01

    Full Text Available Feature extraction from nonlinear and non-stationary (NNS wind turbine (WT condition monitoring (CM signals is challenging. Previously, much effort has been spent to develop advanced signal processing techniques for dealing with CM signals of this kind. The Empirical Wavelet Transform (EWT is one of the achievements attributed to these efforts. The EWT takes advantage of Empirical Mode Decomposition (EMD in dealing with NNS signals but is superior to the EMD in mode decomposition and robustness against noise. However, the conventional EWT meets difficulty in properly segmenting the frequency spectrum of the signal, especially when lacking pre-knowledge of the signal. The inappropriate segmentation of the signal spectrum will inevitably lower the accuracy of the EWT result and thus raise the difficulty of WT CM. To address this issue, an enhanced EWT is proposed in this paper by developing a feasible and efficient spectrum segmentation method. The effectiveness of the proposed method has been verified by using the bearing and gearbox CM data that are open to the public for the purpose of research. The experiment has shown that, after adopting the proposed method, it becomes much easier and more reliable to segment the frequency spectrum of the signal. Moreover, benefitting from the correct segmentation of the signal spectrum, the fault-related features of the CM signals are presented more explicitly in the time-frequency map of the enhanced EWT, despite the considerable noise contained in the signal and the shortage of pre-knowledge about the machine being investigated.

  19. Non-stationary Condition Monitoring of large diesel engines with the AEWATT toolbox

    DEFF Research Database (Denmark)

    Pontoppidan, Niels Henrik; Larsen, Jan; Sigurdsson, Sigurdur

    2005-01-01

    We are developing a specialized toolbox for non-stationary condition monitoring of large 2-stroke diesel engines based on acoustic emission measurements. The main contribution of this toolbox has so far been the utilization of adaptive linear models such as Principal and Independent Component Ana......, the inversion of those angular timing changes called “event alignment”, has allowed for condition monitoring across operation load settings, successfully enabling a single model to be used with realistic data under varying operational conditions-...

  20. Condition monitoring approach for permanent magnet synchronous motor drives based on the INFORM method

    OpenAIRE

    Arellano-Padilla, J.; Sumner, M.; Gerada, C.

    2016-01-01

    This paper proposes a monitoring scheme based on saliency tracking to assess the health condition of PMSM drives operating under non stationary conditions. The evaluated scheme is based on the INFORM methodology, which is associated to the accurate sensorless control of PM drives without zero speed limitation. The result is a monitoring scheme that is able to detect faults that would be very difficult to evaluate under nonstationary conditions. A relevant aspect of the proposed scheme is that...

  1. Compact polarimetric synthetic aperture radar for monitoring soil moisture condition

    Science.gov (United States)

    Merzouki, A.; McNairn, H.; Powers, J.; Friesen, M.

    2017-12-01

    Coarse resolution soil moisture maps are currently operationally delivered by ESA's SMOS and NASA's SMAP passive microwaves sensors. Despite this evolution, operational soil moisture monitoring at the field scale remains challenging. A number of factors contribute to this challenge including the complexity of the retrieval that requires advanced SAR systems with enhanced temporal revisit capabilities. Since the launch of RADARSAT-2 in 2007, Agriculture and Agri-Food Canada (AAFC) has been evaluating the accuracy of these data for estimating surface soil moisture. Thus, a hybrid (multi-angle/multi-polarization) retrieval approach was found well suited for the planned RADARSAT Constellation Mission (RCM) considering the more frequent relook expected with the three satellite configuration. The purpose of this study is to evaluate the capability of C-band CP data to estimate soil moisture over agricultural fields, in anticipation of the launch of RCM. In this research we introduce a new CP approach based on the IEM and simulated RCM CP mode intensities from RADARSAT-2 images acquired at different dates. The accuracy of soil moisture retrieval from the proposed multi-polarization and hybrid methods will be contrasted with that from a more conventional quad-pol approach, and validated against in situ measurements by pooling data collected over AAFC test sites in Ontario, Manitoba and Saskatchewan, Canada.

  2. Real-time well condition monitoring in extended reach wells

    Energy Technology Data Exchange (ETDEWEB)

    Kucs, R.; Spoerker, H.F. [OMV Austria Exploration and Production GmbH, Gaenserndorf (Austria); Thonhauser, G. [Montanuniversitaet Leoben (Austria)

    2008-10-23

    Ever rising daily operating cost for offshore operations make the risk of running into drilling problems due to torque and drag developments in extended reach applications a growing concern. One option to reduce cost related to torque and drag problems can be to monitor torque and drag trends in real time without additional workload on the platform drilling team. To evaluate observed torque or drag trends it is necessary to automatically recognize operations and to have a 'standard value' to compare the measurements to. The presented systematic approach features both options - fully automated operations recognition and real time analysis. Trends can be discussed between rig- and shore-based teams, and decisions can be based on up to date information. Since the system is focused on visualization of real-time torque and drag trends, instead of highly complex and repeated simulations, calculation time is reduced by comparing the real-time rig data against predictions imported from a commercial drilling engineering application. The system allows reacting to emerging stuck pipe situations or developing cuttings beds long before the situations become severe enough to result in substantial lost time. The ability to compare real-time data with historical data from the same or other wells makes the system a valuable tool in supporting a learning organization. The system has been developed in a joint research initiative for field application on the development of an offshore heavy oil field in New Zealand. (orig.)

  3. Machine learning-based Landsat-MODIS data fusion approach for 8-day 30m evapotranspiration monitoring

    Science.gov (United States)

    Im, J.; Ke, Y.; Park, S.

    2016-12-01

    Continuous monitoring of evapotranspiration (ET) is important for understanding of hydrological cycles and energy flux dynamics. At regional and local scales, routine ET estimation is a critical for efficient water management, drought impact assessment and ecosystem health monitoring, etc. Remote sensing has long been recognized to be able to provide ET monitoring over large areas. However, no single satellite could provide temporally continuous ET at relatively high spatial resolution due to the trade-off between the spatial and temporal resolution of current satellite sensors. Landsat-series satellites provide optical and thermal imagery at 30-100m resolution, whereas the 16-day revisit cycle hinders the observation of ET dynamics; MODIS provides sources of ET estimation at daily basis, but the 500-1000m ground sampling distance is too coarse for field level applications. In this study, we present a machine learning and STARFM based method for Landsat/MODIS ET fusion. The approach first downscales MODIS 8-day 1km ET (MOD16A2) to 30m based on eleven Landsat-derived indicators such as NDVI, EVI, NDWI etc on the cloud-free Landsat-available days using Random Forest approach. For the days when Landsat data are not available, downscaled ET is synthesized by MODIS and Landsat data fusion with STARFM and STI-FM approaches. The models are evaluated using in situ flux tower measurements at US-ARM and US-Twt AmeriFlux sites the United States. Results show that the downscaled 30m ET have good agreement with MODIS ET (RMSE=0.42-3.4mm/8days, rRMSE=3.2%-26%) and the downscaled ET have higher accuracy than MODIS ET when compared to in-situ measurements.

  4. Crown condition assessment at the CONECOFOR Permanent Monitoring Plots

    Directory of Open Access Journals (Sweden)

    Renzo NIBBI

    2002-09-01

    Full Text Available A detailed crown condition assessment is currently being carried out at the CONECOFOR (CONtrollo ECOsistemi FORestali, Control of Forest Ecosystems plots. The assessment began in 1996, and during the first two years (1996 and 1997 an assessment form based on previous regional experience was used; in 1998 the new official EU form was adopted. The resulting loss of comparability means that only a few indices can be used in the temporal series 1996-1999. Much effort was devoted to Quality Assurance (QA procedures. The QA program is structured as follows: (i specific field manuals have been adopted and are continuously updated; (ii a national training and intercalibration course (NT&IC is undertaken yearly before beginning the assessment campaign;( iii field checks are carried out yearly on a large number of plots. The results of the QA program have shown that for several indices the quality objectives were not reached, but the quality of the data is improving with time. To express the change in crown conditions in each area, a complex index (CCI = Crown Condition Index was adopted. This index is the result of the sum of the relativized values of all the common indices used during the four years. The following parameters were used: transparency, ramification type, leaf colour alteration extension, leaf damage extension, alteration of leaf distension extension. The range within which the CCI fluctuates was evaluated taking into account all the observations carried out at a given plot throughout the years. The number of cases over a given threshold (outliers was calculated for each year. The threshold for outliers was calculated as the median value plus 2 times the range of the interquartile value. All individual cases exceeding this value are considered outliers. The results are presented for all the areas in which the data set is complete for the four years. The yearly fluctuations are discussed and related to possible causes.

  5. Power and phase monitoring system for the lower hybrid phased array heating system on ATC machine

    International Nuclear Information System (INIS)

    Reed, B.W.

    1975-01-01

    A four waveguide phased array slow wave structure has been constructed to couple microwave energy into plasma in the ATC Tokamac at Princeton. Theory has indicated that the coupling of power into the plasma column is a strong function of the imposed fourier spectrum at the antenna aperture. To optimize heating, and to verify theoretical results, a precision amplitude and phase monitoring system has been designed and constructed. The system data output is routed to an IBM 1800 computer where the fourier spectrum in n/sub parallel/ space is computed for discrete increments of time during an RF pulse. Computer output data is used to update the adjustment of transmission line parameters in between pulses

  6. The "Haptic Finger"- a new device for monitoring skin condition.

    Science.gov (United States)

    Tanaka, Mami; Lévêque, Jean Luc; Tagami, Hachiro; Kikuchi, Katsuko; Chonan, Seifi

    2003-05-01

    Touching the skin is of great importance for the Clinician for assessing roughness, softness, firmness, etc. This type of clinical assessment is very subjective and therefore non-reproducible from one Clinician to another one or even from time to time for the same Clinician. In order to objectively monitor skin texture, we developed a new sensor, placed directly on the Clinician's finger, which generate some electric signal when slid over the skin surface. The base of this Haptic Finger sensor is a thin stainless steel plate on which sponge rubber, PVDF foil, acetate film and gauze are layered. The signal generated by the sensor was filtered and digitally stored before processing. In a first in vitro experiment, the sensor was moved over different skin models (sponge rubber covered by silicon rubber) of varying hardness and roughness. These experiments allowed the definition of two parameters characterizing textures. The first parameter is variance of the signal processed using wavelet analysis, representing an index of roughness. The second parameter is dispersion of the power spectrum density in the frequency domain, corresponding to hardness. To validate these parameters, the Haptic Finger was used to scan skin surfaces of 30 people, 14 of whom displayed a skin disorder: xerosis (n = 5), atopic dermatitis (n = 7), and psoriasis (n = 2). The results obtained by means of the sensor were compared with subjective, clinical evaluations by a Clinician who scored both roughness and hardness of the skin. Good agreement was observed between clinical assessment of the skin and the two parameters generated using the Haptic Finger. Use of this sensor could prove extremely valuable in cosmetic research where skin surface texture (in terms of tactile properties) is difficult to measure.

  7. Effect of Micro Electrical Discharge Machining Process Conditions on Tool Wear Characteristics: Results of an Analytic Study

    DEFF Research Database (Denmark)

    Puthumana, Govindan; P., Rajeev

    2016-01-01

    Micro electrical discharge machining is one of the established techniques to manufacture high aspect ratio features on electrically conductive materials. This paper presents the results and inferences of an analytical study for estimating theeffect of process conditions on tool electrode wear...... characteristicsin micro-EDM process. A new approach with two novel factors anticipated to directly control the material removal mechanism from the tool electrode are proposed; using discharge energyfactor (DEf) and dielectric flushing factor (DFf). The results showed that the correlation between the tool wear rate...... (TWR) and the factors is poor. Thus, individual effects of each factor on TWR are analyzed. The factors selected for the study of individual effects are pulse on-time, discharge peak current, gap voltage and gap flushing pressure. The tool wear rate decreases linearly with an increase in the pulse on...

  8. Machine-Learning Techniques for the Determination of Attrition of Forces Due to Atmospheric Conditions

    Science.gov (United States)

    2018-02-01

    selected as a proof of concept due to its vast number of data points. While this report does note some trends associated with temperature and dew...separate data sets for helicopters and airplanes, while selectively requesting the event IDs, descriptions of events, light conditions, temperature , dew...weather events) and the error rate for that class . The rows are labeled for the actual occurrence of those events. Thus, for every row–column

  9. Influence of the boundary conditions on the dynamic behavior of large hydraulic machines

    OpenAIRE

    Valentín Ruiz, David

    2018-01-01

    Nowadays, hydropower plays an essential role in the energy market. With the massive entrance of new renewable sources such as wind or solar power, hydropower is the only renewable generating source that can provide fast response and regulation capacity to the electric grid. It can even store the surplus of energy when it is necessary using Reversible Pump-Turbine (RPT) power plants. However, this situation makes that hydraulic turbines are increasingly working at off-design conditions with a ...

  10. AN EVALUATION OF CONDITION MONITORING TECHNIQUES FOR LOW-VOLTAGE ELECTRIC CABLES

    International Nuclear Information System (INIS)

    LOFARO, R.J.; GROVE, E.; SOO, P.

    2000-01-01

    Aging of systems and components in nuclear power plants is a well known occurrence that must be managed to ensure the continued safe operation of these plants. Much of the degradation due to aging is controlled through periodic maintenance and/or component replacement. However, there are components that do not receive periodic maintenance or monitoring once they are installed; electric cables are such a component. To provide a means of monitoring the condition of electric cables, research is ongoing to evaluate promising condition monitoring (CM) techniques that can be used in situ to monitor cable condition and predict remaining life. While several techniques are promising, each has limitations that must be considered in its application. This paper discusses the theory behind several of the promising cable CM techniques being studied, along with their effectiveness for monitoring aging degradation in typical cable insulation materials, such as cross-linked polyethylene and ethylene propylene rubber. Successes and limitations of each technique are also presented

  11. Experimental FSO network availability estimation using interactive fog condition monitoring

    Science.gov (United States)

    Turán, Ján.; Ovseník, Łuboš

    2016-12-01

    Free Space Optics (FSO) is a license free Line of Sight (LOS) telecommunication technology which offers full duplex connectivity. FSO uses infrared beams of light to provide optical broadband connection and it can be installed literally in a few hours. Data rates go through from several hundreds of Mb/s to several Gb/s and range is from several 100 m up to several km. FSO link advantages: Easy connection establishment, License free communication, No excavation are needed, Highly secure and safe, Allows through window connectivity and single customer service and Compliments fiber by accelerating the first and last mile. FSO link disadvantages: Transmission media is air, Weather and climate dependence, Attenuation due to rain, snow and fog, Scattering of laser beam, Absorption of laser beam, Building motion and Air pollution. In this paper FSO availability evaluation is based on long term measured data from Fog sensor developed and installed at TUKE experimental FSO network in TUKE campus, Košice, Slovakia. Our FSO experimental network has three links with different physical distances between each FSO heads. Weather conditions have a tremendous impact on FSO operation in terms of FSO availability. FSO link availability is the percentage of time over a year that the FSO link will be operational. It is necessary to evaluate the climate and weather at the actual geographical location where FSO link is going to be mounted. It is important to determine the impact of a light scattering, absorption, turbulence and receiving optical power at the particular FSO link. Visibility has one of the most critical influences on the quality of an FSO optical transmission channel. FSO link availability is usually estimated using visibility information collected from nearby airport weather stations. Raw data from fog sensor (Fog Density, Relative Humidity, Temperature measured at each ms) are collected and processed by FSO Simulator software package developed at our Department. Based

  12. Complex data management for landslide monitoring in emergency conditions

    Science.gov (United States)

    Intrieri, Emanuele; Bardi, Federica; Fanti, Riccardo; Gigli, Giovanni; Fidolini, Francesco; Casagli, Nicola; Costanzo, Sandra; Raffo, Antonio; Di Massa, Giuseppe; Versace, Pasquale

    2017-04-01

    Urbanization, especially in mountain areas, can be considered a major cause for high landslide risk because of the increased exposure of elements at risk. Among the elements at risk, important communication routes such as highways, can be classified as critical infrastructures, since their rupture can cause deaths and chain effects with catastrophic damages on society. The resiliency policy involves prevention activities but also, and more importantly, those activities needed to maintain functionality after disruption and promptly alert incoming catastrophes. To tackle these issues, early warning systems are increasingly employed. However, a gap exists between the ever more technologically advanced instruments and the actual capability of exploiting their full potential. This is due to several factors such as the limited internet connectivity with respect to big data transfers, or the impossibility for operators to check a continuous flow of real time information. A ground-based interferometric synthetic aperture radar was installed along the A16 highway (Campania Region, Southern Italy) to monitor an unstable slope threatening this infrastructure. The installation was in an area where the only internet connection available was 3G, with a limit of 2 gigabyte data transfer per month. On the other hand interferometric data are complex numbers organized in a matrix where each pixel contains both phase and amplitude information of the backscattered signal. The radar employed produced a 1001x1001 complex matrix (corresponding to 7 megabytes) every 5 minutes. Therefore there was the need to reduce the massive data flow produced by the radar. For this reason data were locally and automatically elaborated in order to produce, from a complex matrix, a simple ASCII grid containing only the pixel by pixel displacement value, which is derived from the phase information. Then, since interferometry only measures the displacement component projected along the radar line of sight

  13. Condition monitoring: a study on ageing in Inconel 718

    International Nuclear Information System (INIS)

    Acharya, Vidhi; Murthy, G.V.S.

    2015-01-01

    The development of contemporary high temperature materials is needed to enable the successful introduction of cleaner and more efficient next generation power plants. Due to inherent limitations in steels, new high temperature materials must be selected for a change in operating parameters. Inconel-718 is currently considered to be one of the leading materials for use in high temperature applications. Due to its excellent high-temperature mechanical properties, Inconel-718 is believed to be a contender for forged components of Advanced Ultra-Supercritical (A-USC) power plants. The A-USC power plant with steam conditions of 700°C/35 MPa, is expected to have greater efficiency. Thus the microstructural stability and its impact on the mechanical properties of this alloy at elevated temperatures will certainly be a crucial factor that influences the reliability of the power plants. Therefore it is of imminent importance to study the microstructural evolution of components made out of Inconel-718 preferably by Non-destructive methods

  14. Sparse filtering with the generalized lp/lq norm and its applications to the condition monitoring of rotating machinery

    Science.gov (United States)

    Jia, Xiaodong; Zhao, Ming; Di, Yuan; Li, Pin; Lee, Jay

    2018-03-01

    Sparsity is becoming a more and more important topic in the area of machine learning and signal processing recently. One big family of sparse measures in current literature is the generalized lp /lq norm, which is scale invariant and is widely regarded as normalized lp norm. However, the characteristics of the generalized lp /lq norm are still less discussed and its application to the condition monitoring of rotating devices has been still unexplored. In this study, we firstly discuss the characteristics of the generalized lp /lq norm for sparse optimization and then propose a method of sparse filtering with the generalized lp /lq norm for the purpose of impulsive signature enhancement. Further driven by the trend of industrial big data and the need of reducing maintenance cost for industrial equipment, the proposed sparse filter is customized for vibration signal processing and also implemented on bearing and gearbox for the purpose of condition monitoring. Based on the results from the industrial implementations in this paper, the proposed method has been found to be a promising tool for impulsive feature enhancement, and the superiority of the proposed method over previous methods is also demonstrated.

  15. 10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

    Science.gov (United States)

    2010-01-01

    ... external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive... 10 Energy 1 2010-01-01 2010-01-01 false Conditions requiring individual monitoring of external and internal occupational dose. 20.1502 Section 20.1502 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR...

  16. 4th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations

    CERN Document Server

    Zimroz, Radoslaw; Bartelmus, Walter; Haddar, Mohamed

    2016-01-01

    The book provides readers with a snapshot of recent research and technological trends in the field of condition monitoring of machinery working under a broad range of operating conditions. Each chapter, accepted after a rigorous peer-review process, reports on an original piece of work presented and discussed at the 4th International Conference on Condition Monitoring of Machinery in Non-stationary Operations, CMMNO 2014, held on December 15-16, 2014, in Lyon, France. The contributions have been grouped into three different sections according to the main subfield (signal processing, data mining, or condition monitoring techniques) they are related to. The book includes both theoretical developments as well as a number of industrial case studies, in different areas including, but not limited to: noise and vibration; vibro-acoustic diagnosis; signal processing techniques; diagnostic data analysis; instantaneous speed identification; monitoring and diagnostic systems; and dynamic and fault modeling. This book no...

  17. Performance and perspectives of the diamond based Beam Condition Monitor for beam loss monitoring at CMS

    CERN Document Server

    AUTHOR|(CDS)2080862

    2015-01-01

    At CMS, a beam loss monitoring system is operated to protect the silicon detectors from high particle rates, arising from intense beam loss events. As detectors, poly-crystalline CVD diamond sensors are placed around the beam pipe at several locations inside CMS. In case of extremely high detector currents, the LHC beams are automatically extracted from the LHC rings.Diamond is the detector material of choice due to its radiation hardness. Predictions of the detector lifetime were made based on FLUKA monte-carlo simulations and irradiation test results from the RD42 collaboration, which attested no significant radiation damage over several years.During the LHC operational Run1 (2010 â?? 2013), the detector efficiencies were monitored. A signal decrease of about 50 times stronger than expectations was observed in the in-situ radiation environment. Electric field deformations due to charge carriers, trapped in radiation induced lattice defects, are responsible for this signal decrease. This so-called polarizat...

  18. Condition based monitoring, diagnosis and maintenance on operating equipments of a hydraulic generator unit

    International Nuclear Information System (INIS)

    Liu, X T; Feng, F Z; Si, A W

    2012-01-01

    According to performance characteristics of operating equipments in a hydraulic generator unit (HGU), the relative techniques on condition monitoring and fault diagnosis (CMFD) are introduced in this paper, especially the key technologies are emphasized, such as equipment monitoring, expert system (ES), intelligent diagnosis and condition based maintenance (CBM). Meanwhile, according to the instructor on CBM proposed by State electric power corporation, based on integrated mode, the main steps on implementation of CBM are discussed in this paper.

  19. Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods

    OpenAIRE

    Peng Guo; Nan Bai

    2011-01-01

    Condition Monitoring (CM) of wind turbines can greatly reduce the maintenance costs for wind farms, especially for offshore wind farms. A new condition monitoring method for a wind turbine gearbox using temperature trend analysis is proposed. Autoassociative Kernel Regression (AAKR) is used to construct the normal behavior model of the gearbox temperature. With a proper construction of the memory matrix, the AAKR model can cover the normal working space for the gearbox. When the gearbox has a...

  20. Tribological and Wear Performance of Carbide Tools with TiB2 PVD Coating under Varying Machining Conditions of TiAl6V4 Aerospace Alloy

    Directory of Open Access Journals (Sweden)

    Jose Mario Paiva

    2017-11-01

    Full Text Available Tribological phenomena and tool wear mechanisms during machining of hard-to-cut TiAl6V4 aerospace alloy have been investigated in detail. Since cutting tool wear is directly affected by tribological phenomena occurring between the surfaces of the workpiece and the cutting tool, the performance of the cutting tool is strongly associated with the conditions of the machining process. The present work shows the effect of different machining conditions on the tribological and wear performance of TiB2-coated cutting tools compared to uncoated carbide tools. FEM modeling of the temperature profile on the friction surface was performed for wet machining conditions under varying cutting parameters. Comprehensive characterization of the TiB2 coated vs. uncoated cutting tool wear performance was made using optical 3D imaging, SEM/EDX and XPS methods respectively. The results obtained were linked to the FEM modeling. The studies carried out show that during machining of the TiAl6V4 alloy, the efficiency of the TiB2 coating application for carbide cutting tools strongly depends on cutting conditions. The TiB2 coating is very efficient under roughing at low speeds (with strong buildup edge formation. In contrast, it shows similar wear performance to the uncoated tool under finishing operations at higher cutting speeds when cratering wear predominates.

  1. Environment monitoring and residents health condition monitoring of nuclear power plant Bohunice region

    International Nuclear Information System (INIS)

    Letkovicova, M.; Rehak, R.; Stehlikova, B.; Celko, M.; Hraska, S.; Klocok, L.; Kostial, J.; Prikazsky, V.; Vidovic, J.; Zirko, M.; Beno, T.; Mitosinka, J.

    1998-01-01

    The report contents final environment evaluation and selected characteristic of residents health physics of nuclear power plant Bohunice region. Evaluated data were elaborated during analytical period 1993-1997.Task solving which results are documented in this final report was going on between 1996- 1998. The report deals in individual stages with the following: Information obtaining and completing which characterize demographic situation of the area for the 1993-1997 period; Datum obtaining and completing which contain selected health physics characteristics of the area residents; Database structures for individual data archiving from monitoring and collection; Brief description of geographic information system for graphic presentation of evaluation results based on topographic base; Digital mapping structure description; Results and evaluation of radionuclide monitoring in environment performed by Environmental radiation measurements laboratory by the nuclear power plant Bohunice for the 1993-1997 period. Demographic situation evaluation and selected health physics characteristics of the area of nuclear power plant residents for the 1993-1997 period are summarized in the final part of the document. Monitoring results and their evaluation is processed in graph, table, text description and map output forms. Map outputs are processed in the geographic information system Arc View GIS 3.0a environment

  2. Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions.

    Science.gov (United States)

    Zdravevski, Eftim; Risteska Stojkoska, Biljana; Standl, Marie; Schulz, Holger

    2017-01-01

    Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position. The study used diarized jogging periods and the corresponding accelerometer data from thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine learning algorithms were used for classification: Logistic regression, Support Vector Machines, Random Forest and Extremely Randomized Trees. Classification was performed using only data from the hip accelerometer, using only data from ankle accelerometer and using data from both accelerometers. The reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of correctly identified jogging periods related to the total time including the missed ones, was up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious benefit of using two accelerometers, rather almost the same performance could be achieved from

  3. Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions.

    Directory of Open Access Journals (Sweden)

    Eftim Zdravevski

    Full Text Available Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position.The study used diarized jogging periods and the corresponding accelerometer data from thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine learning algorithms were used for classification: Logistic regression, Support Vector Machines, Random Forest and Extremely Randomized Trees. Classification was performed using only data from the hip accelerometer, using only data from ankle accelerometer and using data from both accelerometers.The reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of correctly identified jogging periods related to the total time including the missed ones, was up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious benefit of using two accelerometers, rather almost the same performance could be

  4. A radioactive waste transportation package monitoring system for normal transport and accident emergency response conditions

    International Nuclear Information System (INIS)

    Brown, G.S.; Cashwell, J.W.; Apple, M.L.

    1993-01-01

    This paper addresses spent fuel and high level waste transportation history and prospects, discusses accident histories of radioactive material transport, discusses emergency responder needs and provides a general description of the Transportation Intelligent Monitoring System (TRANSIMS) design. The key objectives of the monitoring system are twofold: (1) to facilitate effective emergency response to accidents involving a radioactive waste transportation package, while minimizing risk to the public and emergency first-response personnel, and (2) to allow remote monitoring of transportation vehicle and payload conditions to enable research into radioactive material transportation for normal and accident conditions. (J.P.N.)

  5. Operant conditioning of a multiple degree-of-freedom brain-machine interface in a primate model of amputation.

    Science.gov (United States)

    Balasubramanian, Karthikeyan; Southerland, Joshua; Vaidya, Mukta; Qian, Kai; Eleryan, Ahmed; Fagg, Andrew H; Sluzky, Marc; Oweiss, Karim; Hatsopoulos, Nicholas

    2013-01-01

    Operant conditioning with biofeedback has been shown to be an effective method to modify neural activity to generate goal-directed actions in a brain-machine interface. It is particularly useful when neural activity cannot be mathematically mapped to motor actions of the actual body such as in the case of amputation. Here, we implement an operant conditioning approach with visual feedback in which an amputated monkey is trained to control a multiple degree-of-freedom robot to perform a reach-to-grasp behavior. A key innovation is that each controlled dimension represents a behaviorally relevant synergy among a set of joint degrees-of-freedom. We present a number of behavioral metrics by which to assess improvements in BMI control with exposure to the system. The use of non-human primates with chronic amputation is arguably the most clinically-relevant model of human amputation that could have direct implications for developing a neural prosthesis to treat humans with missing upper limbs.

  6. Forecasting of flowrate under rolling motion flow instability condition based on on-line sequential extreme learning machine

    International Nuclear Information System (INIS)

    Chen Hanying; Gao Puzhen; Tan Sichao; Tang Jiguo; Hou Xiaofan; Xu Huiqiang; Wu Xiangcheng

    2015-01-01

    The coupling of multiple thermal-hydraulic parameters can result in complex flow instability in natural circulation system under rolling motion. A real-time thermal-hydraulic condition prediction is helpful to the operation of systems in such condition. A single hidden layer feedforward neural networks algorithm named extreme learning machine (ELM) is considered as suitable method for this application because of its extremely fast training time, good accuracy and simplicity. However, traditional ELM assumes that all the training data are ready before the training process, while the training data is received sequentially in practical forecasting of flowrate. Therefore, this paper proposes a forecasting method for flowrate under rolling motion based on on-line sequential ELM (OS-ELM), which can learn the data one by one or chunk-by-chunk. The experiment results show that the OS-ELM method can achieve a better forecasting performance than basic ELM method and still keep the advantage of fast training and simplicity. (author)

  7. Influence factor analysis of atmospheric electric field monitoring near ground under different weather conditions

    International Nuclear Information System (INIS)

    Wan, Haojiang; Wei, Guanghui; Cui, Yaozhong; Chen, Yazhou

    2013-01-01

    Monitoring of atmospheric electric field near ground plays a critical role in atmospheric environment detecting and lightning warning. Different environmental conditions (e.g. buildings, plants, weather, etc.) have different influences on the data's coherence in an atmospheric electric field detection network. In order to study the main influence factors of atmospheric electric field monitoring under different weather conditions, with the combination of theoretical analysis and experiments, the electric field monitoring data on the ground and on the top of a building are compared in fair weather and thunderstorm weather respectively in this paper. The results show that: In fair weather, the field distortion due to the buildings is the main influence factor on the electric field monitoring. In thunderstorm weather, the corona ions produced from the ground, besides the field distortion due to the buildings, can also influence the electric field monitoring results.

  8. Implementation strategies and tools for condition based monitoring at nuclear power plants

    International Nuclear Information System (INIS)

    2007-05-01

    There is now an acute need to optimize maintenance to improve both reliability and competitiveness of nuclear power plant operation. There is an increasing tendency to move from the preventive (time based) maintenance concept to one dependent on plant and component conditions. In this context, various on-line and off-line condition monitoring and diagnostics, nondestructive inspection techniques and surveillance are used. Component selection for condition based maintenance, parameter selection for monitoring condition, evaluation of condition monitoring results are issues influencing the effectiveness of condition based maintenance. All these selections of components and parameters to be monitored, monitoring and diagnostics techniques to be used, acceptance criteria and trending for condition evaluation, and the economic aspect of predictive maintenance and condition monitoring should be incorporated into an integrated, effective condition based maintenance programme, which is part of the plant's overall maintenance optimization programme. This publication collects and analyses proven condition based maintenance strategies and techniques (engineering and organizational) in Member States. It includes selected papers on maintenance optimization presented during its preparation. This report was prepared under IAEA project on integrated NPP life cycle management including decommissioning. The main objective of an integrated life cycle management programme is to enable NPP's to compete, without compromising safety, successfully in the changing energy markets throughout their service life and to facilitate life extension and eventual decommissioning through improved engineering, technological, economic and managerial actions. The technical working group on NPP life management and other advisory groups nominated by the Member States provide recommendations on high priority needs of Member States in this area

  9. Life cycle management. Condition monitoring of wind power plants; Life-cycle-management. Zustandsueberwachung von Windenergieanlagen

    Energy Technology Data Exchange (ETDEWEB)

    Wolff, R. [cmc GmbH, Kiel (Germany)

    2013-06-01

    The author of the contribution under consideration reports on maintenance strategies and condition monitoring in the field of wind energy. Beside the components in the drive train of wind turbines under consideration, the condition monitoring of the hardware systems and their software is explained. A brief overview of the field of machinery diagnosis and an explanation of the transmission of the measured data follow. Additional sensors such as sensors for the rotor blade monitoring, oil particles counter or oil quality sensors are described. In the field of diagnostic certainty, special follow-up studies such as video endoscopy, analysis of oil or grease, filter testing and material testing are discussed. The information from these thematic fields is used in the life-cycle management database for operationally relevant evaluations and considerations of economy of condition monitoring systems.

  10. Economic analysis of condition monitoring systems for offshore wind turbine sub-systems

    DEFF Research Database (Denmark)

    May, Allan; MacMillan, David; Thöns, Sebastian

    2015-01-01

    The use of condition monitoring systems on offshore wind turbines has increased dramatically in recent times. However, their use is mostly restricted to vibration based monitoring systems for the gearbox, generator and drive train. A survey of commercially available condition monitoring systems...... year life cycle. The model uses Hidden Markov Models to represent both the actual system state and the observed condition monitoring state. The CM systems are modelled to include reduced failure types, false alarms, detection rates and 6 month failure warnings. The costs for system failures are derived...... and their associated costs has been completed for the blades, drive train, tower and foundation. This paper considers what value can be obtained from integrating these additional systems into the maintenance plan. This is achieved by running simulations on an operations and maintenance model for a wind farm over a 20...

  11. Monitoring and Analysis of Ground Settlement Induced by Tunnelling with Slurry Pressure-Balanced Tunnel Boring Machine

    Directory of Open Access Journals (Sweden)

    Hyunku Park

    2018-01-01

    Full Text Available A case study of monitoring and analysis of ground settlement caused by tunnelling of stacked twin tunnels for underground metro line construction through the densely populated area using the slurry pressure-balanced TBM is presented. Detailed ground settlement monitoring was carried out for the initial stage of down-track tunnelling in order to estimate trough width factor and volume losses including face, shield, and tail losses. In addition, using the gap model, prediction of volume loss and ground settlement was carried out with consideration of the ground condition, TBM configurations, and actual operation data. The predictions of the gap model were compared with the observed results, and adjustment factors were determined for volume loss estimation. The adjusted factors were applied to predict ground settlement of the up-track tunnel, and its results were compared with the field measurements.

  12. Condition Monitoring

    DEFF Research Database (Denmark)

    Avenas, Yvan; Dupont, Laurent; Baker, Nick

    2015-01-01

    Power conversion systems are dependent on the performance and reliability of static converters. However, they are subject to frequent functional and environmental strains, which can induce failures. The anticipation of these failures is difficult but important so the operation of a system can be ...

  13. The use of condition monitoring information for maintenance planning and decision-making

    Energy Technology Data Exchange (ETDEWEB)

    Laakso, K.; Rosqvist, T. [VTT Industrial Systems (Finland); Paulsen, J.L. [Risoe National Lab., Roskilde (Denmark)

    2002-12-01

    A survey is presented outlining the use of condition monitoring information in three Nordic nuclear power plants. The questions of the survey relate to the role of condition monitoring in strategic, as well as operative, maintenance planning and decision-making. The survey indicates that condition monitoring is increasingly implemented at nuclear power plants, but very selectively and in a rather slow pace for predictive maintenance. A combined strategy of condition based maintenance and predetermined preventive maintenance is applied for important equipment such as main circulation pumps and steam turbines. A realistic aim is to reduce the number of costly or error prone maintenance and disassembling inspection activities by condition monitoring given that the approach enables a good diagnosis and prediction. Systematic follow-up and analysis of such condition monitoring information followed by a case-specific planning and decision making of timely and rightly directed maintenance actions can justify an extension of the intervals of a number of predetermined inspection, maintenance or periodic testing tasks. (au)

  14. Catalogue of systems for the monitoring of working conditions relating to health and safety

    NARCIS (Netherlands)

    Prins, R.; Verboon, F.

    1991-01-01

    In this Catalogue a number of systems or instruments for Monitoring Working Conditions and workers Health and Safety have been described. The general aim of the project was three-fold: - to obtain an overall assessment of the existing instruments for identifying risk factors and working conditions

  15. Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features

    Science.gov (United States)

    Ahmed, H. O. A.; Wong, M. L. D.; Nandi, A. K.

    2018-01-01

    Condition classification of rolling element bearings in rotating machines is important to prevent the breakdown of industrial machinery. A considerable amount of literature has been published on bearing faults classification. These studies aim to determine automatically the current status of a roller element bearing. Of these studies, methods based on compressed sensing (CS) have received some attention recently due to their ability to allow one to sample below the Nyquist sampling rate. This technology has many possible uses in machine condition monitoring and has been investigated as a possible approach for fault detection and classification in the compressed domain, i.e., without reconstructing the original signal. However, previous CS based methods have been found to be too weak for highly compressed data. The present paper explores computationally, for the first time, the effects of sparse autoencoder based over-complete sparse representations on the classification performance of highly compressed measurements of bearing vibration signals. For this study, the CS method was used to produce highly compressed measurements of the original bearing dataset. Then, an effective deep neural network (DNN) with unsupervised feature learning algorithm based on sparse autoencoder is used for learning over-complete sparse representations of these compressed datasets. Finally, the fault classification is achieved using two stages, namely, pre-training classification based on stacked autoencoder and softmax regression layer form the deep net stage (the first stage), and re-training classification based on backpropagation (BP) algorithm forms the fine-tuning stage (the second stage). The experimental results show that the proposed method is able to achieve high levels of accuracy even with extremely compressed measurements compared with the existing techniques.

  16. Development of a Data Acquisition Program for the Purpose of Monitoring Processing Statistics Throughout the BaBar Online Computing Infrastructure's Farm Machines

    Energy Technology Data Exchange (ETDEWEB)

    Stonaha, P.

    2004-09-03

    A current shortcoming of the BaBar monitoring system is the lack of systematic gathering, archiving, and access to the running statistics of the BaBar Online Computing Infrastructure's farm machines. Using C, a program has been written to gather the raw data of each machine's running statistics and compute various rates and percentages that can be used for system monitoring. These rates and percentages then can be stored in an EPICS database for graphing, archiving, and future access. Graphical outputs show the reception of the data into the EPICS database. The C program can read if the data are 32- or 64-bit and correct for overflows. This program is not exclusive to BaBar and can be easily modified for any system.

  17. Monitoring of the state of the paper machine circulation water with a wide-band impedance measurement; Paperikoneen kiertoveden tilan seuranta laajakaistaisella impedanssimittauksella - MPKT 02

    Energy Technology Data Exchange (ETDEWEB)

    Varpula, T [VTT Automation, Espoo (Finland). Measurement Technology

    1999-12-31

    A new measurement method for monitoring the chemical state of the circulation water in the paper machine is proposed and studied. In the method, the electrical properties - conductivity and permittivity - of the water are measured in a wide frequency band: 20 Hz - 10 mhz. Large-molecule organic compounds in the water are expected cause characteristic changes in the dielectric properties of the water. Continuous monitoring of the permittivity in the wide frequency band thus reveals their presence. Various electronic measurement setups for the measurement are constructed and studied by using test samples. If the method turns out to be promising, a prototype device will be made. (orig.)

  18. Monitoring of the state of the paper machine circulation water with a wide-band impedance measurement; Paperikoneen kiertoveden tilan seuranta laajakaistaisella impedanssimittauksella - MPKT 02

    Energy Technology Data Exchange (ETDEWEB)

    Varpula, T. [VTT Automation, Espoo (Finland). Measurement Technology

    1998-12-31

    A new measurement method for monitoring the chemical state of the circulation water in the paper machine is proposed and studied. In the method, the electrical properties - conductivity and permittivity - of the water are measured in a wide frequency band: 20 Hz - 10 mhz. Large-molecule organic compounds in the water are expected cause characteristic changes in the dielectric properties of the water. Continuous monitoring of the permittivity in the wide frequency band thus reveals their presence. Various electronic measurement setups for the measurement are constructed and studied by using test samples. If the method turns out to be promising, a prototype device will be made. (orig.)

  19. Improvements in valve reliability due to implementation of effective condition monitoring programs

    International Nuclear Information System (INIS)

    Hale, Stan

    2003-01-01

    Modern diagnostic systems for motor-operated valves, pneumatic control valves and checkvalves have facilitated a shift in the maintenance philosophy for valves and actuators in nuclear power plants from schedule based to condition-based maintenance (CBM). This shift enables plant management to focus resources and schedule priority on the plant equipment that warrants attention thereby not wasting resources or increasing the human factors risk on equipment that has not degraded. The most recent initiatives combine condition monitoring with risk/safety insights to focus attention and resonances on the right equipment at the right time consistent with each component's safety-significance. The activities of the ASME working groups responsible for nuclear O and M codes have kept pace with the technology and process improvements necessary to maximize the technical and economic benefits of condition based and risk informed maintenance. This paper discusses adoption of valve condition monitoring in the nuclear power industry, changes to ASME codes and standards during the 90's to facilitate adoption of condition monitoring technology for in-service testing and recent efforts to combine risk insights with condition monitoring strategies to achieve the highest level of valve reliability and nuclear safety without over inflating maintenance cost. (author)

  20. Priority target conditions for algorithms for monitoring children's growth: Interdisciplinary consensus.

    Directory of Open Access Journals (Sweden)

    Pauline Scherdel

    Full Text Available Growth monitoring of apparently healthy children aims at early detection of serious conditions through the use of both clinical expertise and algorithms that define abnormal growth. Optimization of growth monitoring requires standardization of the definition of abnormal growth, and the selection of the priority target conditions is a prerequisite of such standardization.To obtain a consensus about the priority target conditions for algorithms monitoring children's growth.We applied a formal consensus method with a modified version of the RAND/UCLA method, based on three phases (preparatory, literature review, and rating, with the participation of expert advisory groups from the relevant professional medical societies (ranging from primary care providers to hospital subspecialists as well as parent associations. We asked experts in the pilot (n = 11, reading (n = 8 and rating (n = 60 groups to complete the list of diagnostic classification of the European Society for Paediatric Endocrinology and then to select the conditions meeting the four predefined criteria of an ideal type of priority target condition.Strong agreement was obtained for the 8 conditions selected by the experts among the 133 possible: celiac disease, Crohn disease, craniopharyngioma, juvenile nephronophthisis, Turner syndrome, growth hormone deficiency with pituitary stalk interruption syndrome, infantile cystinosis, and hypothalamic-optochiasmatic astrocytoma (in decreasing order of agreement.This national consensus can be used to evaluate the algorithms currently suggested for growth monitoring. The method used for this national consensus could be re-used to obtain an international consensus.

  1. Fast beam conditions monitor BCM1F for the CMS experiment

    International Nuclear Information System (INIS)

    Bell, A.; Castro, E.; Hall-Wilton, R.

    2009-10-01

    The CMS Beam Conditions and Radiation Monitoring System, BRM, will support beam tuning, protect the CMS detector from adverse beam conditions, and measure the accumulated dose close to or inside all sub-detectors. It is composed of different sub-systems measuring either the particle flux near the beam pipe with time resolution between nano- and microseconds or the integrated dose over longer time intervals. This paper presents the Fast Beam Conditions Monitor, BCM1F, which is designed for fast flux monitoring measuring both beam halo and collision products. BCM1F is located inside the CMS pixel detector volume close to the beam-pipe. It uses sCVD diamond sensors and radiation hard front-end electronics, along with an analog optical readout of the signals. The commissioning of the system and its successful operation during the first beams of the LHC are described. (orig.)

  2. Image edge detection based tool condition monitoring with morphological component analysis.

    Science.gov (United States)

    Yu, Xiaolong; Lin, Xin; Dai, Yiquan; Zhu, Kunpeng

    2017-07-01

    The measurement and monitoring of tool condition are keys to the product precision in the automated manufacturing. To meet the need, this study proposes a novel tool wear monitoring approach based on the monitored image edge detection. Image edge detection has been a fundamental tool to obtain features of images. This approach extracts the tool edge with morphological component analysis. Through the decomposition of original tool wear image, the approach reduces the influence of texture and noise for edge measurement. Based on the target image sparse representation and edge detection, the approach could accurately extract the tool wear edge with continuous and complete contour, and is convenient in charactering tool conditions. Compared to the celebrated algorithms developed in the literature, this approach improves the integrity and connectivity of edges, and the results have shown that it achieves better geometry accuracy and lower error rate in the estimation of tool conditions. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  3. Fast beam conditions monitor BCM1F for the CMS experiment

    Energy Technology Data Exchange (ETDEWEB)

    Bell, A. [CERN, Geneva (Switzerland); Geneva Univ. (Switzerland); Castro, E. [DESY Zeuthen (Germany); Hall-Wilton, R. [CERN, Geneva (Switzerland); Wisconsin Univ., Madison, WI (US)] (and others)

    2009-10-15

    The CMS Beam Conditions and Radiation Monitoring System, BRM, will support beam tuning, protect the CMS detector from adverse beam conditions, and measure the accumulated dose close to or inside all sub-detectors. It is composed of different sub-systems measuring either the particle flux near the beam pipe with time resolution between nano- and microseconds or the integrated dose over longer time intervals. This paper presents the Fast Beam Conditions Monitor, BCM1F, which is designed for fast flux monitoring measuring both beam halo and collision products. BCM1F is located inside the CMS pixel detector volume close to the beam-pipe. It uses sCVD diamond sensors and radiation hard front-end electronics, along with an analog optical readout of the signals. The commissioning of the system and its successful operation during the first beams of the LHC are described. (orig.)

  4. Data support system for controlling decentralised nuclear power industry facilities through uninterruptible condition monitoring

    Directory of Open Access Journals (Sweden)

    Povarov Vladimir

    2018-01-01

    Full Text Available The article describes the automated uninterruptible multi-parameter system for monitoring operational vulnerability of critical NPP components, which differs from existing ones by being universally applicable for analysing mechanical damage of nuclear power unit components. The system allows for performing routine assessment of metal structures. The assessment of strained condition of a deteriorating component is based on three-dimensional finite element simulation with calculations adjusted with reference to in-situ measurements. A program for calculation and experimental analysis of maximum load and durability of critical area forms the core of uninterruptible monitoring system. The knowledge base on performance of the monitored components in different operating conditions and the corresponding comprehensive analysis of strained condition and deterioration rates compose the basis of control system data support, both for operating nuclear power units and robotic maintenance and repair systems.

  5. A Review of the Condition Monitoring of Capacitors in Power Electronic Converters

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Wang, Huai; Blaabjerg, Frede

    2015-01-01

    Capacitor is one of the reliability critical components in power electronic systems. In the last two decades, many efforts in the academic research have been devoted to the condition monitoring of capacitors to estimate their health status. Industry applications demand more reliable power...... electronics products with preventive maintenance. Nevertheless, most of the developed capacitor condition monitoring technologies are rarely adopted by industry due to the complexity, increased cost and other relevant issues. An overview of the prior-art research in this area is therefore needed to justify....... Therefore, this paper firstly classifies the capacitor condition monitoring methods into three categories, then the respective technology evolution from 1993 to 2015 is summarized. Remarks on the state-of-the-art research and the future opportunities targeting for practical industry applications are given....

  6. A Review of the Condition Monitoring of Capacitors in Power Electronic Converters

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Wang, Huai; Blaabjerg, Frede

    2016-01-01

    Capacitors are one type of reliability-critical components in power electronic systems. In the last two decades, many efforts in academic research have been devoted to the condition monitoring of capacitors to estimate their health status. Industry applications are demanding more reliable power...... electronics products with preventive maintenance. Nevertheless, most of the developed capacitor condition monitoring technologies are rarely adopted by industry due to the complexity, increased cost, and other relevant issues. An overview of the prior-art research in this area is therefore needed to justify......, this paper first classifies the capacitor condition monitoring methods into three categories, then the respective technology evolution in the last two decades is summarized. Finally, the state-of-the-art research and the future opportunities targeting for industry applications are given....

  7. A real time study on condition monitoring of distribution transformer using thermal imager

    Science.gov (United States)

    Mariprasath, T.; Kirubakaran, V.

    2018-05-01

    The transformer is one of the critical apparatus in the power system. At any cost, a few minutes of outages harshly influence the power system. Hence, prevention-based maintenance technique is very essential. The continuous conditioning and monitoring technology significantly increases the life span of the transformer, as well as reduces the maintenance cost. Hence, conditioning and monitoring of transformer's temperature are very essential. In this paper, a critical review has been made on various conditioning and monitoring techniques. Furthermore, a new method, hot spot indication technique, is discussed. Also, transformer's operating condition is monitored by using thermal imager. From the thermal analysis, it is inferred that major hotspot locations are appearing at connection lead out; also, the bushing of the transformer is the very hottest spot in transformer, so monitoring the level of oil is essential. Alongside, real time power quality analysis has been carried out using the power analyzer. It shows that industrial drives are injecting current harmonics to the distribution network, which causes the power quality problem on the grid. Moreover, the current harmonic limit has exceeded the IEEE standard limit. Hence, the adequate harmonics suppression technique is need an hour.

  8. Identification of material constitutive laws representative of machining conditions for two titanium alloys: Ti6Al4V and Ti555-3

    OpenAIRE

    GERMAIN, Guénaël; MOREL, Anne; BRAHAM-BOUCHNAK, Tarek

    2013-01-01

    Determining a material constitutive law that is representative of the extreme conditions found in the cutting zone during machining operations is a very challenging problem. In this study, dynamic shear tests, which reproduce, as faithfully as possible, these conditions in terms of strain, strain rate, and temperature, have been developed using hat-shaped specimens. The objective was to identify the parameters of a Johnson–Cook material behavior model by an inverse method for two titanium all...

  9. New Fast Beam Conditions Monitoring (BCM1F) system for CMS

    Science.gov (United States)

    Zagozdzinska, A. A.; Bell, A. J.; Dabrowski, A. E.; Hempel, M.; Henschel, H. M.; Karacheban, O.; Przyborowski, D.; Leonard, J. L.; Penno, M.; Pozniak, K. T.; Miraglia, M.; Lange, W.; Lohmann, W.; Ryjov, V.; Lokhovitskiy, A.; Stickland, D.; Walsh, R.

    2016-01-01

    The CMS Beam Radiation Instrumentation and Luminosity (BRIL) project is composed of several systems providing the experiment protection from adverse beam conditions while also measuring the online luminosity and beam background. Although the readout bandwidth of the Fast Beam Conditions Monitoring system (BCM1F—one of the faster monitoring systems of the CMS BRIL), was sufficient for the initial LHC conditions, the foreseen enhancement of the beams parameters after the LHC Long Shutdown-1 (LS1) imposed the upgrade of the system. This paper presents the new BCM1F, which is designed to provide real-time fast diagnosis of beam conditions and instantaneous luminosity with readout able to resolve the 25 ns bunch structure.

  10. Development of wall conditioning and impurity monitoring systems in Versatile Experiment Spherical Torus (VEST)

    Energy Technology Data Exchange (ETDEWEB)

    Lee, H.Y., E-mail: brbbebbero@snu.ac.kr [Seoul National University, Seoul (Korea, Republic of); Yang, J.; Kim, Y.G.; Yang, S.M.; Kim, Y.S.; Lee, K.H. [Seoul National University, Seoul (Korea, Republic of); An, Y.H. [National Fusion Research Institute, Daejon (Korea, Republic of); Chung, K.J.; Na, Y.S. [Seoul National University, Seoul (Korea, Republic of); Hwang, Y.S., E-mail: yhwang@snu.ac.kr [Seoul National University, Seoul (Korea, Republic of)

    2016-11-01

    Highlights: • The baking for partial wall heating and H{sub 2}/He GDC systems are developed in VEST. • The RGA and OES systems for monitoring impurities are constructed in VEST. • The partial baking and He GDC show limited effects on plasma characteristics. • H{sub 2} GDC above 4 h enables the longer plasma current duration up to ∼15 ms. • After H{sub 2} GDC, the discharge should be conducted within 3 h from treatment. - Abstract: Wall conditioning and impurity monitoring systems are developed in Versatile Experiment Spherical Torus (VEST). As a wall conditioning system, a baking system covering the vacuum vessel wall partially and a glow discharge cleaning (GDC) system using two electrodes with dc and 50 kHz power supplies are installed. The GDC system operates with hydrogen and helium gases for both chemical and physical desorption. The impurity monitoring system with residual gas analyzer (RGA), operating at <10{sup −5} Torr with a differential pumping system, is installed along with the optical emission spectroscopy (OES) system to monitor the hydrogen and impurity radiation lines. Effects of these wall conditioning techniques are investigated with the impurity monitoring system for ohmic discharges of VEST. The partial baking and He GDC show limited effects on plasma characteristics but sufficient H{sub 2} GDC above 4 h enables the longer plasma current duration up to ∼15 ms within 3 h from the end of treatment.

  11. Development of wall conditioning and impurity monitoring systems in Versatile Experiment Spherical Torus (VEST)

    International Nuclear Information System (INIS)

    Lee, H.Y.; Yang, J.; Kim, Y.G.; Yang, S.M.; Kim, Y.S.; Lee, K.H.; An, Y.H.; Chung, K.J.; Na, Y.S.; Hwang, Y.S.

    2016-01-01

    Highlights: • The baking for partial wall heating and H_2/He GDC systems are developed in VEST. • The RGA and OES systems for monitoring impurities are constructed in VEST. • The partial baking and He GDC show limited effects on plasma characteristics. • H_2 GDC above 4 h enables the longer plasma current duration up to ∼15 ms. • After H_2 GDC, the discharge should be conducted within 3 h from treatment. - Abstract: Wall conditioning and impurity monitoring systems are developed in Versatile Experiment Spherical Torus (VEST). As a wall conditioning system, a baking system covering the vacuum vessel wall partially and a glow discharge cleaning (GDC) system using two electrodes with dc and 50 kHz power supplies are installed. The GDC system operates with hydrogen and helium gases for both chemical and physical desorption. The impurity monitoring system with residual gas analyzer (RGA), operating at <10"−"5 Torr with a differential pumping system, is installed along with the optical emission spectroscopy (OES) system to monitor the hydrogen and impurity radiation lines. Effects of these wall conditioning techniques are investigated with the impurity monitoring system for ohmic discharges of VEST. The partial baking and He GDC show limited effects on plasma characteristics but sufficient H_2 GDC above 4 h enables the longer plasma current duration up to ∼15 ms within 3 h from the end of treatment.

  12. Recommendations for strengthening the infrared technology component of any condition monitoring program

    Science.gov (United States)

    Nicholas, Jack R., Jr.; Young, R. K.

    1999-03-01

    This presentation provides insights of a long term 'champion' of many condition monitoring technologies and a Level III infra red thermographer. The co-authors present recommendations based on their observations of infra red and other components of predictive, condition monitoring programs in manufacturing, utility and government defense and energy activities. As predictive maintenance service providers, trainers, informal observers and formal auditors of such programs, the co-authors provide a unique perspective that can be useful to practitioners, managers and customers of advanced programs. Each has over 30 years experience in the field of machinery operation, maintenance, and support the origins of which can be traced to and through the demanding requirements of the U.S. Navy nuclear submarine forces. They have over 10 years each of experience with programs in many different countries on 3 continents. Recommendations are provided on the following: (1) Leadership and Management Support (For survival); (2) Life Cycle View (For establishment of a firm and stable foundation for a program); (3) Training and Orientation (For thermographers as well as operators, managers and others); (4) Analyst Flexibility (To innovate, explore and develop their understanding of machinery condition); (5) Reports and Program Justification (For program visibility and continued expansion); (6) Commitment to Continuous Improvement of Capability and Productivity (Through application of updated hardware and software); (7) Mutual Support by Analysts (By those inside and outside of the immediate organization); (8) Use of Multiple Technologies and System Experts to Help Define Problems (Through the use of correlation analysis of data from up to 15 technologies. An example correlation analysis table for AC and DC motors is provided.); (9) Root Cause Analysis (Allows a shift from reactive to proactive stance for a program); (10) Master Equipment Identification and Technology Application (To

  13. Significance of Operating Environment in Condition Monitoring of Large Civil Structures

    OpenAIRE

    Alampalli, Sreenivas

    1999-01-01

    Success of remote long-term condition monitoring of large civil structures and developing calibrated analytical models for damage detection, depend significantly on establishing accurate baseline signatures and their sensitivity. Most studies reported in the literature concentrated on the effect of structural damage on modal parameters without emphasis on reliability of modal parameters. Thus, a field bridge structure was studied for the significance of operating conditions in relation to bas...

  14. ANN based Performance Evaluation of BDI for Condition Monitoring of Induction Motor Bearings

    Science.gov (United States)

    Patel, Raj Kumar; Giri, V. K.

    2017-06-01

    One of the critical parts in rotating machines is bearings and most of the failure arises from the defective bearings. Bearing failure leads to failure of a machine and the unpredicted productivity loss in the performance. Therefore, bearing fault detection and prognosis is an integral part of the preventive maintenance procedures. In this paper vibration signal for four conditions of a deep groove ball bearing; normal (N), inner race defect (IRD), ball defect (BD) and outer race defect (ORD) were acquired from a customized bearing test rig, under four different conditions and three different fault sizes. Two approaches have been opted for statistical feature extraction from the vibration signal. In the first approach, raw signal is used for statistical feature extraction and in the second approach statistical features extracted are based on bearing damage index (BDI). The proposed BDI technique uses wavelet packet node energy coefficients analysis method. Both the features are used as inputs to an ANN classifier to evaluate its performance. A comparison of ANN performance is made based on raw vibration data and data chosen by using BDI. The ANN performance has been found to be fairly higher when BDI based signals were used as inputs to the classifier.

  15. Welding station condition monitoring using bluetooth enabled sensors and intelligent data management

    Energy Technology Data Exchange (ETDEWEB)

    Eyers, D R; Grosvenor, R I; Prickett, P W [Intelligent Process Monitoring and Management (IPMM) Group, Cardiff School of Engineering, Cardiff University, Wales (United Kingdom)

    2005-01-01

    This paper reports on the first phase deployment of bluetooth enabled condition monitoring systems at a large multinational engineering company. The radio networking of sensor signals is a fast developing area and the facilities afforded by the Wisnet device were used in the monitoring of a welding station. This and any of the planned further monitoring systems had to comply to a carefully managed IT information plan at the company. For the example application, the development and testing of microcontoller-based pre-processing of data is reported. This includes further development of the Petri Net approach to provide event-based monitoring as a sensible alternative to the continuous transmission of the sensory data.

  16. Tools and techniques for ageing predictions in nuclear reactors through condition monitoring

    International Nuclear Information System (INIS)

    Verma, R.M.P.

    1994-01-01

    To operate the nuclear reactors beyond their design predicted life is gaining importance because of huge replacement and decommissioning costs. But experience shows that nuclear plant safety and reliability may decline in the later years of plant life due to ageing degradation. Ageing of nuclear plant components, structures and systems, if unmitigated reduces their safety margins provided in the design and thus increases risks to public health and safety. These safety margins must be monitored throughout plant service life including any extended life. Condition monitoring of nuclear reactor components/equipment and systems can be done to study the effect of ageing, status of safety margins and effect of corrective and mitigating actions taken. The tools and techniques of condition monitoring are also important in failure trending, predictive maintenance, evaluation of scheduled maintenance, in mitigation of ageing, life extension and reliability studies. (author). 1 fig., 1 annexure

  17. A contemporary method for monitoring indoor radon and environmental conditions at a remote test site

    International Nuclear Information System (INIS)

    Renken, K.J.; Coursin, S.

    1996-01-01

    A state-of-the-art method for automatically monitoring indoor radon and environmental conditions at a remote test site is described. A Wisconsin home that exhibited elevated radon levels has been installed with automated PC-data acquisition system (PC-DAS) that includes: a laptop PC, a data acquisition cardcage, a commercial data acquisition software program plus sensors to measure radon gas concentrations, differential pressures, indoor air quality and meteorological conditions. The isolated PC-DAS is connected to a PC in a university laboratory via a modem and a communications software package. Experimental data is monitored and saved by the remote PC in real time and then automatically downloaded to the lab computer at selected intervals. An example of the formatted field results is presented and analysed. This documentation of the set-up, the off-the-shelf computer hardware and software, and the procedures should assist investigations requiring flexible remote long-term radon and environmental monitoring. (Author)

  18. Online calibration method for condition monitoring of nuclear reactor instrumentations based on electrical signature analysis

    International Nuclear Information System (INIS)

    Syaiful Bakhri

    2013-01-01

    Electrical signature analysis currently becomes an alternative in condition monitoring in nuclear power plants not only for stationary components such as sensors, measurement and instrumentation channels, and other components but also for dynamic components such as electric motors, pumps, generator or actuators. In order to guarantee the accuracy, the calibration of monitoring system is a necessary which practically is performed offline, under limited schedules and certain tight procedures. This research aims to introduce online calibration technique for electrical signature condition monitoring in order that the accuracy can be maintained continuously which in turn increases the reactor safety as a whole. The research was performed step by stepin detail from the conventional technique, online calibration using baseline information and online calibration using differential gain adjustment. Online calibration based on differential gain adjustment provides better results than other techniques even tough under extreme gain insertion as well as external disturbances such as supply voltages. (author)

  19. Condition Monitoring for Roller Bearings of Wind Turbines Based on Health Evaluation under Variable Operating States

    Directory of Open Access Journals (Sweden)

    Lei Fu

    2017-10-01

    Full Text Available Condition monitoring (CM is used to assess the health status of wind turbines (WT by detecting turbine failure and predicting maintenance needs. However, fluctuating operating conditions cause variations in monitored features, therefore increasing the difficulty of CM, for example, the frequency-domain analysis may lead to an inaccurate or even incorrect prediction when evaluating the health of the WT components. In light of this challenge, this paper proposed a method for the health evaluation of WT components based on vibration signals. The proposed approach aimed to reduce the evaluation error caused by the impact of the variable operating condition. First, the vibration signal was decomposed into a set of sub-signals using variational mode decomposition (VMD. Next, the sub-signal energy and the probability distribution were obtained and normalized. Finally, the concept of entropy was introduced to evaluate the health condition of a monitored object to provide an effective guide for maintenance. In particular, the health evaluation for CM was based on a performance review over a range of operating conditions, rather than at a certain single operating condition. Experimental investigations were performed which verified the efficiency of the evaluation method, as well as a comparison with the previous method.

  20. Assessment of Augmented Electronic Fuel Controls for Modular Engine Diagnostics and Condition Monitoring

    Science.gov (United States)

    1978-12-01

    removal of the horoscope . Diagnostic Conoctor - E4 Th10 E4 23-pin connoctor on the electrical control unit Is provided for ground- checking electrical...confidenou in engine condition monitoring * 1min general. Thi9 has boon especially true in~ eases where fUse signal s have c~aused engine shutdowns. Where ECWI

  1. Wind Turbine Drivetrain Condition Monitoring During GRC Phase 1 and Phase 2 Testing

    Energy Technology Data Exchange (ETDEWEB)

    Sheng, S.; Link, H.; LaCava, W.; van Dam, J.; McNiff, B.; Veers, P.; Keller, J.; Butterfield, S.; Oyague, F.

    2011-10-01

    This report will present the wind turbine drivetrain condition monitoring (CM) research conducted under the phase 1 and phase 2 Gearbox Reliability Collaborative (GRC) tests. The rationale and approach for this drivetrain CM research, investigated CM systems, test configuration and results, and a discussion on challenges in wind turbine drivetrain CM and future research and development areas, will be presented.

  2. A new oil debris sensor for online condition monitoring of wind turbine gearboxes

    DEFF Research Database (Denmark)

    Wang, Chao; Liu, Hui; Liu, Xiao

    2015-01-01

    Online Condition Monitoring (CM) is a key technology for the Operation and Maintenance (O&M) of wind turbines. Lubricating oil is the blood of the wind turbine gearbox. Metal debris in lubricating oil contains abundant information regarding the ageing and wear/damage of mechanical transmission sy...

  3. Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions.

    Science.gov (United States)

    Torous, John; Friedman, Rohn; Keshavan, Matcheri

    2014-01-21

    Patient retrospective recollection is a mainstay of assessing symptoms in mental health and psychiatry. However, evidence suggests that these retrospective recollections may not be as accurate as data collection though the experience sampling method (ESM), which captures patient data in "real time" and "real life." However, the difficulties in practical implementation of ESM data collection have limited its impact in psychiatry and mental health. Smartphones with the capability to run mobile applications may offer a novel method of collecting ESM data that may represent a practical and feasible tool for mental health and psychiatry. This paper aims to provide data on psychiatric patients' prevalence of smartphone ownership, patterns of use, and interest in utilizing mobile applications to monitor their mental health conditions. One hundred psychiatric outpatients at a large urban teaching hospital completed a paper-and-pencil survey regarding smartphone ownership, use, and interest in utilizing mobile applications to monitor their mental health condition. Ninety-seven percent of patients reported owning a phone and 72% reported that their phone was a smartphone. Patients in all age groups indicated greater than 50% interest in using a mobile application on a daily basis to monitor their mental health condition. Smartphone and mobile applications represent a practical opportunity to explore new modalities of monitoring, treatment, and research of psychiatric and mental health conditions.

  4. Dimensional comparability of psychosocial working conditions as covered in European monitoring questionnaires

    NARCIS (Netherlands)

    Formazin, M.; Burr, H.; Aagestad, C.; Tynes, T.; Thorsen, S.V.; Perkio-Makela, M.; Díaz Aramburu, C.I.; Pinilla García, F.J.; Galiana Blanco, L.; Vermeylen, G.; Parent-Thirion, A.; Hooftman, W.; Houtman, I.L.D.

    2014-01-01

    Background.In most countries in the EU, national surveys are used to monitor working conditions and health. Since the development processes behind the various surveys are not necessarily theoretical, but certainly practical and political, the extent of similarity among the dimensions covered in

  5. Long-term monitoring of sea ice conditions in the Kerch Strait by remote sensing data

    Science.gov (United States)

    Lavrova, Olga Yu.; Mityagina, Marina I.; Bocharova, Tatiana Yu.; Kostianoy, Andrey G.

    2017-10-01

    The results of multi-year satellite monitoring of ice conditions in the Kerch Strait connecting the Black and Azov Seas are discussed. The issue gained importance in view of the ongoing construction of the Crimean Bridge across the strait. Our monitoring has been based on the whole variety of available satellite data including visible and radar data over the past 17 years. Every year the Azov Sea becomes fully or partially covered by ice during the cold season. In severe winters, ice often is carried to the Kerch Strait and even the Black Sea. An analysis of ice drift hydrometeorological conditions is presented. The ice conditions of 2017 are under special consideration. Everyday satellite monitoring of the Kerch Strait, including the construction area of the Crimean Bridge, revealed ice formation and drift features on the way from the Azov Sea through the Kerch Strait as well as ice interaction with the piers of the main and technological bridges under construction. It was found that, even under strong northeast winds, ice can pass neither through the piers, nor via the widest shipway. At present, it is hard to discern the impacts of the two bridges on floating ice, nevertheless when the construction is over and the technological bridge is gone, by all appearances the main bridge will strongly affect ice conditions in the Kerch Strait. This perspective calls for continuous satellite monitoring of the area that is enabled by cutting-edge systems and technologies.

  6. Building and application of the plant condition monitoring system for nuclear power plants

    International Nuclear Information System (INIS)

    Ono, S.

    2013-01-01

    To achieve the stable operation of nuclear power plants, we developed the plant condition monitoring system based on the heat and mass balance calculation. This system has adopted the heat balance model based on the actual plant data to find the symptoms of the disorder of the equipment by heat balance changes in the turbine system. (author)

  7. Development and application of the plant condition monitoring system for nuclear power plants

    International Nuclear Information System (INIS)

    Ono, S.

    2014-01-01

    To achieve the stable operation of nuclear power plants, we developed the plant condition monitoring system based on the heat and mass balance calculation. In this system, it is a significant feature to adopt the sophisticated heat balance model based on the actual plant data to find the symptoms of anomalies in the turbine system from heat balance changes. (author)

  8. Photovoltaic Array Condition Monitoring Based on Online Regression of Performance Model

    DEFF Research Database (Denmark)

    Spataru, Sergiu; Sera, Dezso; Kerekes, Tamas

    2013-01-01

    regression modeling, from PV array production, plane-of-array irradiance, and module temperature measurements, acquired during an initial learning phase of the system. After the model has been parameterized automatically, the condition monitoring system enters the normal operation phase, where...

  9. Implementation of an Integrated, Portable Transformer Condition Monitoring Instrument in the Classroom and On-Site

    Science.gov (United States)

    Chatterjee, B.; Dey, D.; Chakravorti, S.

    2010-01-01

    The development of integrated, portable, transformer condition monitoring (TCM) equipment for classroom demonstrations as well as for student exercises conducted in the field is discussed. Demonstrations include experimentation with real-world transformers to illustrate concepts such as polarization and depolarization current through oil-paper…

  10. 77 FR 24228 - Condition Monitoring Techniques for Electric Cables Used in Nuclear Power Plants

    Science.gov (United States)

    2012-04-23

    ... Used in Nuclear Power Plants AGENCY: Nuclear Regulatory Commission. ACTION: Regulatory guide; issuance... guide, (RG) 1.218, ``Condition Monitoring Techniques for Electric Cables Used in Nuclear Power Plants... of electric cables for nuclear power plants. RG 1.218 is not intended to be prescriptive, instead it...

  11. An approach to effectiveness monitoring of floodplain channel aquatic habitat: channel condition assessment.

    Science.gov (United States)

    Richard D. Woodsmith; James R. Noel; Michael L. Dilger

    2005-01-01

    The condition of aquatic habitat and the health of species dependent on that habitat are issues of significant concern to land management agencies, other organizations, and the public at large in southeastern Alaska, as well as along much of the Pacific coastal region of North America. We develop and test a set of effectiveness monitoring procedures for measuring...

  12. Online condition monitoring to enable extended operation of nuclear power plants

    International Nuclear Information System (INIS)

    Meyer, Ryan Michael; Bond, Leonard John; Ramuhalli, Pradeep

    2012-01-01

    Safe, secure, and economic operation of nuclear power plants will remain of strategic significance. New and improved monitoring will likely have increased significance in the post-Fukushima world. Prior to Fukushima, many activities were already underway globally to facilitate operation of nuclear power plants beyond their initial licensing periods. Decisions to shut down a nuclear power plant are mostly driven by economic considerations. Online condition monitoring is a means to improve both the safety and economics of extending the operating lifetimes of nuclear power plants, enabling adoption of proactive aging management. With regard to active components (e.g., pumps, valves, motors, etc.), significant experience in other industries has been leveraged to build the science base to support adoption of online condition-based maintenance and proactive aging management in the nuclear industry. Many of the research needs are associated with enabling proactive management of aging in passive components (e.g., pipes, vessels, cables, containment structures, etc.). This paper provides an overview of online condition monitoring for the nuclear power industry with an emphasis on passive components. Following the overview, several technology/knowledge gaps are identified, which require addressing to facilitate widespread online condition monitoring of passive components. (author)

  13. Monitoring Conditions Leading to SCC/Corrosion of Carbon Steel in Fuel Grade Ethanol

    Science.gov (United States)

    2011-02-11

    This is the draft final report of the project on field monitoring of conditions that lead to SCC in ethanol tanks and piping. The other two aspects of the consolidated program, ethanol batching and blending effects (WP#325) and source effects (WP#323...

  14. Validation of a Numerical Model for the Prediction of the Annoyance Condition at the Operator Station of Construction Machines

    Directory of Open Access Journals (Sweden)

    Eleonora Carletti

    2016-11-01

    Full Text Available It is well-known that the reduction of noise levels is not strictly linked to the reduction of noise annoyance. Even earthmoving machine manufacturers are facing the problem of customer complaints concerning the noise quality of their machines with increasing frequency. Unfortunately, all the studies geared to the understanding of the relationship between multidimensional characteristics of noise signals and the auditory perception of annoyance require repeated sessions of jury listening tests, which are time-consuming. In this respect, an annoyance prediction model was developed for compact loaders to assess the annoyance sensation perceived by operators at their workplaces without repeating the full sound quality assessment but using objective parameters only. This paper aims at verifying the feasibility of the developed annoyance prediction model when applied to other kinds of earthmoving machines. For this purpose, an experimental investigation was performed on five earthmoving machines, different in type, dimension, and engine mechanical power, and the annoyance predicted by the numerical model was compared to the annoyance given by subjective listening tests. The results were evaluated by means of the squared value of the correlation coefficient, R2, and they confirm the possible applicability of the model to other kinds of machines.

  15. Model Based Optimal Sensor Network Design for Condition Monitoring in an IGCC Plant

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Rajeeva; Kumar, Aditya; Dai, Dan; Seenumani, Gayathri; Down, John; Lopez, Rodrigo

    2012-12-31

    This report summarizes the achievements and final results of this program. The objective of this program is to develop a general model-based sensor network design methodology and tools to address key issues in the design of an optimal sensor network configuration: the type, location and number of sensors used in a network, for online condition monitoring. In particular, the focus in this work is to develop software tools for optimal sensor placement (OSP) and use these tools to design optimal sensor network configuration for online condition monitoring of gasifier refractory wear and radiant syngas cooler (RSC) fouling. The methodology developed will be applicable to sensing system design for online condition monitoring for broad range of applications. The overall approach consists of (i) defining condition monitoring requirement in terms of OSP and mapping these requirements in mathematical terms for OSP algorithm, (ii) analyzing trade-off of alternate OSP algorithms, down selecting the most relevant ones and developing them for IGCC applications (iii) enhancing the gasifier and RSC models as required by OSP algorithms, (iv) applying the developed OSP algorithm to design the optimal sensor network required for the condition monitoring of an IGCC gasifier refractory and RSC fouling. Two key requirements for OSP for condition monitoring are desired precision for the monitoring variables (e.g. refractory wear) and reliability of the proposed sensor network in the presence of expected sensor failures. The OSP problem is naturally posed within a Kalman filtering approach as an integer programming problem where the key requirements of precision and reliability are imposed as constraints. The optimization is performed over the overall network cost. Based on extensive literature survey two formulations were identified as being relevant to OSP for condition monitoring; one based on LMI formulation and the other being standard INLP formulation. Various algorithms to solve

  16. Impact Analysis of Temperature and Humidity Conditions on Electrochemical Sensor Response in Ambient Air Quality Monitoring.

    Science.gov (United States)

    Wei, Peng; Ning, Zhi; Ye, Sheng; Sun, Li; Yang, Fenhuan; Wong, Ka Chun; Westerdahl, Dane; Louie, Peter K K

    2018-01-23

    The increasing applications of low-cost air sensors promises more convenient and cost-effective systems for air monitoring in many places and under many conditions. However, the data quality from such systems has not been fully characterized and may not meet user expectations in research and regulatory uses, or for use in citizen science. In our study, electrochemical sensors (Alphasense B4 series) for carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO₂), and oxidants (O x ) were evaluated under controlled laboratory conditions to identify the influencing factors and quantify their relation with sensor outputs. Based on the laboratory tests, we developed different correction methods to compensate for the impact of ambient conditions. Further, the sensors were assembled into a monitoring system and tested in ambient conditions in Hong Kong side-by-side with regulatory reference monitors, and data from these tests were used to evaluate the performance of the models, to refine them, and validate their applicability in variable ambient conditions in the field. The more comprehensive correction models demonstrated enhanced performance when compared with uncorrected data. One over-arching observation of this study is that the low-cost sensors may promise excellent sensitivity and performance, but it is essential for users to understand and account for several key factors that may strongly affect the nature of sensor data. In this paper, we also evaluated factors of multi-month stability, temperature, and humidity, and considered the interaction of oxidant gases NO₂ and ozone on a newly introduced oxidant sensor.

  17. The Piston Compressor: The Methodology of the Real-Time Condition Monitoring

    International Nuclear Information System (INIS)

    Naumenko, A P; Kostyukov, V N

    2012-01-01

    The methodology of a diagnostic signal processing, a function chart of the monitoring system are considered in the article. The methodology of monitoring and diagnosing is based on measurement of indirect processes' parameters (vibroacoustic oscillations) therefore no more than five sensors is established on the cylinder, measurement of direct structural and thermodynamic parameters is envisioned as well. The structure and principle of expert system's functioning of decision-making is given. Algorithm of automatic expert system includes the calculation diagnostic attributes values based on their normative values, formation sets of diagnostic attributes that correspond to individual classes to malfunction, formation of expert system messages. The scheme of a real-time condition monitoring system for piston compressors is considered. The system have consistently-parallel structure of information-measuring equipment, which allows to measure the vibroacoustic signal for condition monitoring of reciprocating compressors and modes of its work. Besides, the system allows to measure parameters of other physical processes, for example, system can measure and use for monitoring and statements of the diagnosis the pressure in decreasing spaces (the indicator diagram), the inlet pressure and flowing pressure of each cylinder, inlet and delivery temperature of gas, valves temperature, position of a rod, leakage through compression packing and others.

  18. Cost-Effective Shaft Torque Observer for Condition Monitoring of Wind Turbines

    DEFF Research Database (Denmark)

    Perisic, Nevena; Kirkegaard, Poul Henning; Pedersen, Bo Juul

    2015-01-01

    Improvement of condition monitoring (CM) systems for wind turbines (WTs) and reduction of the cost of wind energy are possible if knowledge about the condition of different WT components is available. CM based on the WT drive train shaft torque signal can give a better understanding of the gearbox...... of the augmented Kalman filter with fading memory (AKFF) is compared with the augmented Kalman filter (AKF) using simulated data of theWT for different load conditions, measurement noise levels andWT fault scenarios. A multiple-model algorithm, based on a set of different Kalman filters, is designed for practical...

  19. Condition monitoring of a motor-operated valve using estimated motor torque

    International Nuclear Information System (INIS)

    Chai, Jangbom; Kang, Shinchul; Park, Sungkeun; Hong, Sungyull; Lim, Chanwoo

    2004-01-01

    This paper is concerned with the development of data analysis methods to be used in on-line monitoring and diagnosis of Motor-Operated Valves (MOVs) effectively and accurately. The technique to be utilized includes the electrical measurements and signal processing to estimate electric torque of induction motors, which are attached to most of MOV systems. The estimated torque of an induction motor is compared with the directly measured torque using a torque cell in various loading conditions including the degraded voltage conditions to validate the estimating scheme. The accuracy of the estimating scheme is presented. The advantages of the estimated torque signatures are reviewed over the currently used ones such as the current signature and the power signature in several respects: accuracy, sensitivity, resolution and so on. Additionally, the estimated torque methods are suggested as a good way to monitor the conditions of MOVs with higher accuracy. (author)

  20. Measurements of the performance of a beam condition monitor prototype in a 5 GeV electron beam

    Energy Technology Data Exchange (ETDEWEB)

    Hempel, M., E-mail: maria.hempel@desy.de [Brandenburg University of Technology Cottbus-Senftenberg, Cottbus 03013 (Germany); DESY, Zeuthen 15738 (Germany); Afanaciev, K. [NCPHEP, Minsk 220040 (Belarus); Burtowy, P.; Dabrowski, A. [CERN, Geneva 1211 (Switzerland); Henschel, H. [DESY, Zeuthen 15738 (Germany); Idzik, M. [AGH University of Science and Technology, Cracow 30-059 (Poland); Karacheban, O. [Brandenburg University of Technology Cottbus-Senftenberg, Cottbus 03013 (Germany); Lange, W.; Leonard, J. [DESY, Zeuthen 15738 (Germany); Levy, I. [Tel Aviv University, Tel Aviv 6997801 (Israel); Lohmann, W. [Brandenburg University of Technology Cottbus-Senftenberg, Cottbus 03013 (Germany); DESY, Zeuthen 15738 (Germany); Pollak, B. [Northwestern University, Evanston, IL 60208 (United States); Przyborowski, D. [AGH University of Science and Technology, Cracow 30-059 (Poland); Ryjov, V. [CERN, Geneva 1211 (Switzerland); Schuwalow, S. [DESY, Zeuthen 15738 (Germany); Stickland, D. [Princeton University, Princeton, NJ 08544 (United States); Walsh, R. [DESY, Zeuthen 15738 (Germany); Zagozdzinska, A. [CERN, Geneva 1211 (Switzerland)

    2016-08-01

    The Fast Beam Conditions Monitor, BCM1F, in the Compact Muon Solenoid, CMS, experiment was operated since 2008 and delivered invaluable information on the machine induced background in the inner part of the CMS detector supporting a safe operation of the inner tracker and high quality data. Due to the shortening of the time between two bunch crossings from 50 ns to 25 ns and higher expected luminosity at the Large Hadron Collider, LHC, in 2015, BCM1F needed an upgrade to higher bandwidth. In addition, BCM1F is used as an on-line luminometer operated independently of CMS. To match these requirements, the number of single crystal diamond sensors was enhanced from 8 to 24. Each sensor is subdivided into two pads, leading to 48 readout channels. Dedicated fast front-end ASICs were developed in 130 nm technology, and the back-end electronics is completely upgraded. An assembled prototype BCM1F detector comprising sensors, a fast front-end ASIC and optical analog readout was studied in a 5 GeV electron beam at the DESY-II accelerator. Results on the performance are given.

  1. An online condition monitoring system implemented an internet connectivity and FTP for low speed slew bearing

    Science.gov (United States)

    Caesarendra, W.; Kosasih, B.; Tjahjowidodo, T.; Ariyanto, M.; Daryl, LWQ; Pamungkas, D.

    2018-04-01

    Rapid and reliable information in slew bearing maintenance is not trivial issue. This paper presents the online monitoring system to assist maintenance engineer in order to monitor the bearing condition of low speed slew bearing in sheet metal company. The system is able to pass the vibration information from the place where the bearing and accelerometer sensors are attached to the data center; and from the data center it can be access by opening the online monitoring website from any place and by any person. The online monitoring system is built using some programming languages such as C language, MATLAB, PHP, HTML and CSS. Generally, the flow process is start with the automatic vibration data acquisition; then features are calculated from the acquired vibration data. These features are then sent to the data center; and form the data center, the vibration features can be seen through the online monitoring website. This online monitoring system has been successfully applied in School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong.

  2. Ultrasensitive and Highly Stable Resistive Pressure Sensors with Biomaterial-Incorporated Interfacial Layers for Wearable Health-Monitoring and Human-Machine Interfaces.

    Science.gov (United States)

    Chang, Hochan; Kim, Sungwoong; Jin, Sumin; Lee, Seung-Woo; Yang, Gil-Tae; Lee, Ki-Young; Yi, Hyunjung

    2018-01-10

    Flexible piezoresistive sensors have huge potential for health monitoring, human-machine interfaces, prosthetic limbs, and intelligent robotics. A variety of nanomaterials and structural schemes have been proposed for realizing ultrasensitive flexible piezoresistive sensors. However, despite the success of recent efforts, high sensitivity within narrower pressure ranges and/or the challenging adhesion and stability issues still potentially limit their broad applications. Herein, we introduce a biomaterial-based scheme for the development of flexible pressure sensors that are ultrasensitive (resistance change by 5 orders) over a broad pressure range of 0.1-100 kPa, promptly responsive (20 ms), and yet highly stable. We show that employing biomaterial-incorporated conductive networks of single-walled carbon nanotubes as interfacial layers of contact-based resistive pressure sensors significantly enhances piezoresistive response via effective modulation of the interlayer resistance and provides stable interfaces for the pressure sensors. The developed flexible sensor is capable of real-time monitoring of wrist pulse waves under external medium pressure levels and providing pressure profiles applied by a thumb and a forefinger during object manipulation at a low voltage (1 V) and power consumption (<12 μW). This work provides a new insight into the material candidates and approaches for the development of wearable health-monitoring and human-machine interfaces.

  3. Wind turbine condition monitoring based on SCADA data using normal behavior models

    DEFF Research Database (Denmark)

    Schlechtingen, Meik; Santos, Ilmar; Achiche, Sofiane

    2013-01-01

    This paper proposes a system for wind turbine condition monitoring using Adaptive Neuro-Fuzzy Interference Systems (ANFIS). For this purpose: (1) ANFIS normal behavior models for common Supervisory Control And Data Acquisition (SCADA) data are developed in order to detect abnormal behavior...... the applicability of ANFIS models for monitoring wind turbine SCADA signals. The computational time needed for model training is compared to Neural Network (NN) models showing the strength of ANFIS in training speed. (2) For automation of fault diagnosis Fuzzy Interference Systems (FIS) are used to analyze...

  4. Information support of monitoring of technical condition of buildings in construction risk area

    Science.gov (United States)

    Skachkova, M. E.; Lepihina, O. Y.; Ignatova, V. V.

    2018-05-01

    The paper presents the results of the research devoted to the development of a model of information support of monitoring buildings technical condition; these buildings are located in the construction risk area. As a result of the visual and instrumental survey, as well as the analysis of existing approaches and techniques, attributive and cartographic databases have been created. These databases allow monitoring defects and damages of buildings located in a 30-meter risk area from the object under construction. The classification of structures and defects of these buildings under survey is presented. The functional capabilities of the developed model and the field of it practical applications are determined.

  5. Model-Based Sensor Placement for Component Condition Monitoring and Fault Diagnosis in Fossil Energy Systems

    Energy Technology Data Exchange (ETDEWEB)

    Mobed, Parham [Texas Tech Univ., Lubbock, TX (United States); Pednekar, Pratik [West Virginia Univ., Morgantown, WV (United States); Bhattacharyya, Debangsu [West Virginia Univ., Morgantown, WV (United States); Turton, Richard [West Virginia Univ., Morgantown, WV (United States); Rengaswamy, Raghunathan [Texas Tech Univ., Lubbock, TX (United States)

    2016-01-29

    Design and operation of energy producing, near “zero-emission” coal plants has become a national imperative. This report on model-based sensor placement describes a transformative two-tier approach to identify the optimum placement, number, and type of sensors for condition monitoring and fault diagnosis in fossil energy system operations. The algorithms are tested on a high fidelity model of the integrated gasification combined cycle (IGCC) plant. For a condition monitoring network, whether equipment should be considered at a unit level or a systems level depends upon the criticality of the process equipment, its likeliness to fail, and the level of resolution desired for any specific failure. Because of the presence of a high fidelity model at the unit level, a sensor network can be designed to monitor the spatial profile of the states and estimate fault severity levels. In an IGCC plant, besides the gasifier, the sour water gas shift (WGS) reactor plays an important role. In view of this, condition monitoring of the sour WGS reactor is considered at the unit level, while a detailed plant-wide model of gasification island, including sour WGS reactor and the Selexol process, is considered for fault diagnosis at the system-level. Finally, the developed algorithms unify the two levels and identifies an optimal sensor network that maximizes the effectiveness of the overall system-level fault diagnosis and component-level condition monitoring. This work could have a major impact on the design and operation of future fossil energy plants, particularly at the grassroots level where the sensor network is yet to be identified. In addition, the same algorithms developed in this report can be further enhanced to be used in retrofits, where the objectives could be upgrade (addition of more sensors) and relocation of existing sensors.

  6. Harmonic wave model of a permanent magnet synchronous machine for modeling partial demagnetization under short circuit conditions

    NARCIS (Netherlands)

    Kral, C.; Haumer, A.; Bogomolov, M.D.; Lomonova, E.

    2012-01-01

    This paper proposes a multi domain physical model of permanent magnet synchronous machines, considering electrical, magnetic, thermal and mechanical effects. For each component of the model, the main wave as well as lower and higher harmonic wave components of the magnetic flux and the magnetic

  7. Laboratory versus industrial cutting force sensor in tool condition monitoring system

    International Nuclear Information System (INIS)

    Szwajka, K

    2005-01-01

    Research works concerning the utilisation of cutting force measures in tool condition monitoring usually present results and deliberations based on laboratory sensors. These sensors are too fragile to be used in industrial practice. Industrial sensors employed on the factory floor are less accurate, and this must be taken into account when creating a tool condition monitoring strategy. Another drawback of most of these works is that constant cutting parameters are used for the entire tool life. This does not reflect industrial practice where the same tool is used at different feeds and depths of cut in sequential passes. This paper presents a comparison of signals originating from laboratory and industrial cutting force sensors. The usability of the sensor output was studied during a laboratory simulation of industrial cutting conditions. Instead of building mathematical models for the correlation between tool wear and cutting force, an FFBP artificial neural network was used to find which combination of input data would provide an acceptable estimation of tool wear. The results obtained proved that cross talk between channels has an important influence on cutting force measurements, however this input configuration can be used for a tool condition monitoring system

  8. Remote monitoring as a tool in condition assessment of a highway bridge

    Science.gov (United States)

    Tantele, Elia A.; Votsis, Renos A.; Onoufriou, Toula; Milis, Marios; Kareklas, George

    2016-08-01

    The deterioration of civil infrastructure and their subsequent maintenance is a significant problem for the responsible managing authorities. The ideal scenario is to detect deterioration and/or structural problems at early stages so that the maintenance cost is kept low and the safety of the infrastructure remains undisputed. The current inspection regimes implemented mostly via visual inspection are planned at specific intervals but are not always executed on time due to shortcomings in expert personnel and finance. However the introduction of technological advances in the assessment of infrastructures provides the tools to alleviate this problem. This study describes the assessment of a highway RC bridge's structural condition using remote structural health monitoring. A monitoring plan is implemented focusing on strain measurements; as strain is a parameter influenced by the environmental conditions supplementary data are provided from temperature and wind sensors. The data are acquired using wired sensors (deployed at specific locations) which are connected to a wireless sensor unit installed at the bridge. This WSN application enables the transmission of the raw data from the field to the office for processing and evaluation. The processed data are then used to assess the condition of the bridge. This case study, which is part of an undergoing RPF research project, illustrates that remote monitoring can alleviate the problem of missing structural inspections. Additionally, shows its potential to be the main part of a fully automated smart procedure of obtaining structural data, processed them and trigger an alarm when certain undesirable conditions are met.

  9. Panorama Image Processing for Condition Monitoring with Thermography in Power Plant

    Energy Technology Data Exchange (ETDEWEB)

    Jeon, Byoung Joon; Kim, Tae Hwan; Kim, Soon Geol; Mo, Yoon Syub [UNETWARE, Seoul (Korea, Republic of); Kim, Won Tae [Kongju National University, Gongju (Korea, Republic of)

    2010-04-15

    In this paper, imaging processing study obtained from CCD image and thermography image was performed in order to treat easily thermographic data without any risks of personnel who conduct the condition monitoring for the abnormal or failure status occurrable in industrial power plants. This imaging processing is also applicable to the predictive maintenance. For confirming the broad monitoring, a methodology producting single image from the panorama technique was developed no matter how many cameras are employed, including fusion method for discrete configuration for the target. As results, image fusion from quick realtime processing was obtained and it was possible to save time to track the location monitoring in matching the images between CCTV and thermography

  10. Panorama Image Processing for Condition Monitoring with Thermography in Power Plant

    International Nuclear Information System (INIS)

    Jeon, Byoung Joon; Kim, Tae Hwan; Kim, Soon Geol; Mo, Yoon Syub; Kim, Won Tae

    2010-01-01

    In this paper, imaging processing study obtained from CCD image and thermography image was performed in order to treat easily thermographic data without any risks of personnel who conduct the condition monitoring for the abnormal or failure status occurrable in industrial power plants. This imaging processing is also applicable to the predictive maintenance. For confirming the broad monitoring, a methodology producting single image from the panorama technique was developed no matter how many cameras are employed, including fusion method for discrete configuration for the target. As results, image fusion from quick realtime processing was obtained and it was possible to save time to track the location monitoring in matching the images between CCTV and thermography

  11. Using modular neural networks to monitor accident conditions in nuclear power plants

    International Nuclear Information System (INIS)

    Guo, Z.

    1992-01-01

    Nuclear power plants are very complex systems. The diagnoses of transients or accident conditions is very difficult because a large amount of information, which is often noisy, or intermittent, or even incomplete, need to be processed in real time. To demonstrate their potential application to nuclear power plants, neural networks axe used to monitor the accident scenarios simulated by the training simulator of TVA's Watts Bar Nuclear Power Plant. A self-organization network is used to compress original data to reduce the total number of training patterns. Different accident scenarios are closely related to different key parameters which distinguish one accident scenario from another. Therefore, the accident scenarios can be monitored by a set of small size neural networks, called modular networks, each one of which monitors only one assigned accident scenario, to obtain fast training and recall. Sensitivity analysis is applied to select proper input variables for modular networks

  12. An Updated Methodology for Enhancing Risk Monitors with Integrated Equipment Condition Assessment

    Energy Technology Data Exchange (ETDEWEB)

    Ramuhalli, Pradeep [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Hirt, Evelyn H. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Coles, Garill A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Bonebrake, Christopher A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Ivans, William J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Wootan, David W. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Mitchell, Mark R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2014-07-18

    Small modular reactors (SMRs) generally include reactors with electric output of ~350 MWe or less (this cutoff varies somewhat but is substantially less than full-size plant output of 700 MWe or more). Advanced SMRs (AdvSMRs) refer to a specific class of SMRs and are based on modularization of advanced reactor concepts. Enhancing affordability of AdvSMRs will be critical to ensuring wider deployment, as AdvSMRs suffer from loss of economies of scale inherent in small reactors when compared to large (~greater than 600 MWe output) reactors and the controllable day-to-day costs of AdvSMRs will be dominated by operation and maintenance (O&M) costs. Technologies that help characterize real-time risk are important for controlling O&M costs. Risk monitors are used in current nuclear power plants to provide a point-in-time estimate of the system risk given the current plant configuration (e.g., equipment availability, operational regime, and environmental conditions). However, current risk monitors are unable to support the capability requirements listed above as they do not take into account plant-specific normal, abnormal, and deteriorating states of active components and systems. This report documents technology developments towards enhancing risk monitors that, if integrated with supervisory plant control systems, can provide the capability requirements listed and meet the goals of controlling O&M costs. The report describes research results on augmenting an initial methodology for enhanced risk monitors that integrate real-time information about equipment condition and POF into risk monitors. Methods to propagate uncertainty through the enhanced risk monitor are evaluated. Available data to quantify the level of uncertainty and the POF of key components are examined for their relevance, and a status update of this data evaluation is described. Finally, we describe potential targets for developing new risk metrics that may be useful for studying trade-offs for economic

  13. A case study of remaining storage life prediction using stochastic filtering with the influence of condition monitoring

    International Nuclear Information System (INIS)

    Wang, Zhaoqiang; Hu, Changhua; Wang, Wenbin; Zhou, Zhijie; Si, Xiaosheng

    2014-01-01

    Some systems may spend most of their time in storage, but once needed, must be fully functional. Slow degradation occurs when the system is in storage, so to ensure the functionality of these systems, condition monitoring is usually conducted periodically to check the condition of the system. However, taking the condition monitoring data may require putting the system under real testing situation which may accelerate the degradation, and therefore, shorten the storage life of the system. This paper presents a case study of condition-based remaining storage life prediction for gyros in the inertial navigation system on the basis of the condition monitoring data and the influence of the condition monitoring data taking process. A stochastic-filtering-based degradation model is developed to incorporate both into the prediction of the remaining storage life distribution. This makes the predicted remaining storage life depend on not only the condition monitoring data but also the testing process of taking the condition monitoring data, which the existing prognostic techniques and algorithms did not consider. The presented model is fitted to the real condition monitoring data of gyros testing using the maximum likelihood estimation method for parameter estimation. Comparisons are made with the model without considering the process of taking the condition monitoring data, and the results clearly demonstrate the superiority of the newly proposed model

  14. Construction Condition and Damage Monitoring of Post-Tensioned PSC Girders Using Embedded Sensors.

    Science.gov (United States)

    Shin, Kyung-Joon; Lee, Seong-Cheol; Kim, Yun Yong; Kim, Jae-Min; Park, Seunghee; Lee, Hwanwoo

    2017-08-10

    The potential for monitoring the construction of post-tensioned concrete beams and detecting damage to the beams under loading conditions was investigated through an experimental program. First, embedded sensors were investigated that could measure pre-stress from the fabrication process to a failure condition. Four types of sensors were installed on a steel frame, and the applicability and the accuracy of these sensors were tested while pre-stress was applied to a tendon in the steel frame. As a result, a tri-sensor loading plate and a Fiber Bragg Grating (FBG) sensor were selected as possible candidates. With those sensors, two pre-stressed concrete flexural beams were fabricated and tested. The pre-stress of the tendons was monitored during the construction and loading processes. Through the test, it was proven that the variation in thepre-stress had been successfully monitored throughout the construction process. The losses of pre-stress that occurred during a jacking and storage process, even those which occurred inside the concrete, were measured successfully. The results of the loading test showed that tendon stress and strain within the pure span significantly increased, while the stress in areas near the anchors was almost constant. These results prove that FBG sensors installed in a middle section can be used to monitor the strain within, and the damage to pre-stressed concrete beams.

  15. Abnormal Condition Monitoring of Workpieces Based on RFID for Wisdom Manufacturing Workshops

    Directory of Open Access Journals (Sweden)

    Cunji Zhang

    2015-12-01

    Full Text Available Radio Frequency Identification (RFID technology has been widely used in many fields. However, previous studies have mainly focused on product life cycle tracking, and there are few studies on real-time status monitoring of workpieces in manufacturing workshops. In this paper, a wisdom manufacturing model is introduced, a sensing-aware environment for a wisdom manufacturing workshop is constructed, and RFID event models are defined. A synthetic data cleaning method is applied to clean the raw RFID data. The Complex Event Processing (CEP technology is adopted to monitor abnormal conditions of workpieces in real time. The RFID data cleaning method and data mining technology are examined by simulation and physical experiments. The results show that the synthetic data cleaning method preprocesses data well. The CEP based on the Rifidi® Edge Server technology completed abnormal condition monitoring of workpieces in real time. This paper reveals the importance of RFID spatial and temporal data analysis in real-time status monitoring of workpieces in wisdom manufacturing workshops.

  16. Abnormal Condition Monitoring of Workpieces Based on RFID for Wisdom Manufacturing Workshops

    Science.gov (United States)

    Zhang, Cunji; Yao, Xifan; Zhang, Jianming

    2015-01-01

    Radio Frequency Identification (RFID) technology has been widely used in many fields. However, previous studies have mainly focused on product life cycle tracking, and there are few studies on real-time status monitoring of workpieces in manufacturing workshops. In this paper, a wisdom manufacturing model is introduced, a sensing-aware environment for a wisdom manufacturing workshop is constructed, and RFID event models are defined. A synthetic data cleaning method is applied to clean the raw RFID data. The Complex Event Processing (CEP) technology is adopted to monitor abnormal conditions of workpieces in real time. The RFID data cleaning method and data mining technology are examined by simulation and physical experiments. The results show that the synthetic data cleaning method preprocesses data well. The CEP based on the Rifidi® Edge Server technology completed abnormal condition monitoring of workpieces in real time. This paper reveals the importance of RFID spatial and temporal data analysis in real-time status monitoring of workpieces in wisdom manufacturing workshops. PMID:26633418

  17. Verification of the machinery condition monitoring technology by fault simulation tests

    International Nuclear Information System (INIS)

    Maehara, Takafumi; Watanabe, Yukio; Osaki, Kenji; Higuma, Koji; Nakano, Tomohito

    2009-01-01

    This paper shows the test items and equipments introduced by Japan Nuclear Energy Safety Organization to establish the monitoring technique for machinery conditions. From the result of vertical pump simulation tests, it was confirmed that fault analysis was impossible by measuring the accelerations on both motor and pump column pipes, however, was possible by measuring of pump shaft vibrations. Because hydraulic whirls by bearing wear had significant influences over bearing misalignments and flow rates, the monitoring trends must be done under the same condition (on bearing alignments and flow rates). We have confirmed that malfunctions of vertical pumps can be diagnosed using measured shaft vibration by ultrasonic sensors from outer surface of pump casing on the floor. (author)

  18. Wireless acceleration sensor of moving elements for condition monitoring of mechanisms

    Science.gov (United States)

    Sinitsin, Vladimir V.; Shestakov, Aleksandr L.

    2017-09-01

    Comprehensive analysis of the angular and linear accelerations of moving elements (shafts, gears) allows an increase in the quality of the condition monitoring of mechanisms. However, existing tools and methods measure either linear or angular acceleration with postprocessing. This paper suggests a new construction design of an angular acceleration sensor for moving elements. The sensor is mounted on a moving element and, among other things, the data transfer and electric power supply are carried out wirelessly. In addition, the authors introduce a method for processing the received information which makes it possible to divide the measured acceleration into the angular and linear components. The design has been validated by the results of laboratory tests of an experimental model of the sensor. The study has shown that this method provides a definite separation of the measured acceleration into linear and angular components, even in noise. This research contributes an advance in the range of methods and tools for condition monitoring of mechanisms.

  19. Monitoring psychosocial stress at work: development of the Psychosocial Working Conditions Questionnaire.

    Science.gov (United States)

    Widerszal-Bazyl, M; Cieślak, R

    2000-01-01

    Many studies on the impact of psychosocial working conditions on health prove that psychosocial stress at work is an important risk factor endangering workers' health. Thus it should be constantly monitored like other work hazards. The paper presents a newly developed instrument for stress monitoring called the Psychosocial Working Conditions Questionnaire (PWC). Its structure is based on Robert Karasek's model of job stress (Karasek, 1979; Karasek & Theorell, 1990). It consists of 3 main scales Job Demands, Job Control, Social Support and 2 additional scales adapted from the Occupational Stress Questionnaire (Elo, Leppanen, Lindstrom, & Ropponen, 1992), Well-Being and Desired Changes. The study of 8 occupational groups (bank and insurance specialists, middle medical personnel, construction workers, shop assistants, government and self-government administration officers, computer scientists, public transport drivers, teachers, N = 3,669) indicates that PWC has satisfactory psychometrics parameters. Norms for the 8 groups were developed.

  20. Wireless acceleration sensor of moving elements for condition monitoring of mechanisms

    International Nuclear Information System (INIS)

    Sinitsin, Vladimir V; Shestakov, Aleksandr L

    2017-01-01

    Comprehensive analysis of the angular and linear accelerations of moving elements (shafts, gears) allows an increase in the quality of the condition monitoring of mechanisms. However, existing tools and methods measure either linear or angular acceleration with postprocessing. This paper suggests a new construction design of an angular acceleration sensor for moving elements. The sensor is mounted on a moving element and, among other things, the data transfer and electric power supply are carried out wirelessly. In addition, the authors introduce a method for processing the received information which makes it possible to divide the measured acceleration into the angular and linear components. The design has been validated by the results of laboratory tests of an experimental model of the sensor. The study has shown that this method provides a definite separation of the measured acceleration into linear and angular components, even in noise. This research contributes an advance in the range of methods and tools for condition monitoring of mechanisms. (paper)

  1. Artificial Neural Network Algorithm for Condition Monitoring of DC-link Capacitors Based on Capacitance Estimation

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Wang, Huai; Gadalla, Brwene Salah Abdelkarim

    2015-01-01

    challenges. A capacitance estimation method based on Artificial Neural Network (ANN) algorithm is therefore proposed in this paper. The implemented ANN estimated the capacitance of the DC-link capacitor in a back-toback converter. Analysis of the error of the capacitance estimation is also given......In power electronic converters, reliability of DC-link capacitors is one of the critical issues. The estimation of their health status as an application of condition monitoring have been an attractive subject for industrial field and hence for the academic research filed as well. More reliable...... solutions are required to be adopted by the industry applications in which usage of extra hardware, increased cost, and low estimation accuracy are the main challenges. Therefore, development of new condition monitoring methods based on software solutions could be the new era that covers the aforementioned...

  2. A Two-Stage Diagnosis Framework for Wind Turbine Gearbox Condition Monitoring

    Directory of Open Access Journals (Sweden)

    Janet M. Twomey

    2013-01-01

    Full Text Available Advances in high performance sensing technologies enable the development of wind turbine condition monitoring system to diagnose and predict the system-wide effects of failure events. This paper presents a vibration-based two stage fault detection framework for failure diagnosis of rotating components in wind turbines. The proposed framework integrates an analytical defect detection method and a graphical verification method together to ensure the diagnosis efficiency and accuracy. The efficacy of the proposed methodology is demonstrated with a case study with the gearbox condition monitoring Round Robin study dataset provided by the National Renewable Energy Laboratory (NREL. The developed methodology successfully picked five faults out of seven in total with accurate severity levels without producing any false alarm in the blind analysis. The case study results indicated that the developed fault detection framework is effective for analyzing gear and bearing faults in wind turbine drive train system based upon system vibration characteristics.

  3. Condition monitoring of a check valve for nuclear power plants by means of acoustic emission technique

    International Nuclear Information System (INIS)

    Lee, M. R.; Lee, J. H.; Kim, J. T.; Kim, J. S.; Luk, V. K.

    2003-01-01

    This work performed in support of the International Nuclear Energy Research Institute (INERI) program, which was to develop and demonstrate advanced sensor and computational technology for on-line monitoring of the condition of components, structures, and systems in advanced and next-generation nuclear power plants (NPPs). This primary object of this work is to investigate advanced condition monitoring systems based on acoustic emission detection that can provide timely detection of check valve degeneration and service aging so that maintenance/replacement could be preformed prior to loss safety function. The research is focused on the capability of AE technique to provide diagnostic information useful in determining check valve aging and degradation check valve failure and undesirable operating modes. This work also includes the investigation and adaptation of several advanced sensor technologies such as accelerometer and advanced ultrasonic technique. In addition, this work will develop advanced sophisticated signal processing, noise reduction, and pattern recognition techniques and algorithms from check valve degradation.

  4. Development Of A Sensor Network Test Bed For ISD Materials And Structural Condition Monitoring

    International Nuclear Information System (INIS)

    Zeigler, K.; Ferguson, B.; Karapatakis, D.; Herbst, C.; Stripling, C.

    2011-01-01

    The P Reactor at the Savannah River Site is one of the first reactor facilities in the US DOE complex that has been placed in its end state through in situ decommissioning (ISD). The ISD end state consists of a grout-filled concrete civil structure within the concrete frame of the original building. To evaluate the feasibility and utility of remote sensors to provide verification of ISD system conditions and performance characteristics, an ISD Sensor Network Test Bed has been designed and deployed at the Savannah River National Laboratory. The test bed addresses the DOE-EM Technology Need to develop a remote monitoring system to determine and verify ISD system performance. Commercial off-the-shelf sensors have been installed on concrete blocks taken from walls of the P Reactor Building. Deployment of this low-cost structural monitoring system provides hands-on experience with sensor networks. The initial sensor system consists of: (1) Groutable thermistors for temperature and moisture monitoring; (2) Strain gauges for crack growth monitoring; (3) Tiltmeters for settlement monitoring; and (4) A communication system for data collection. Preliminary baseline data and lessons learned from system design and installation and initial field testing will be utilized for future ISD sensor network development and deployment.

  5. DEVELOPMENT OF A SENSOR NETWORK TEST BED FOR ISD MATERIALS AND STRUCUTRAL CONDITION MONITORING

    Energy Technology Data Exchange (ETDEWEB)

    Zeigler, K.; Ferguson, B.; Karapatakis, D.; Herbst, C.; Stripling, C.

    2011-07-06

    The P Reactor at the Savannah River Site is one of the first reactor facilities in the US DOE complex that has been placed in its end state through in situ decommissioning (ISD). The ISD end state consists of a grout-filled concrete civil structure within the concrete frame of the original building. To evaluate the feasibility and utility of remote sensors to provide verification of ISD system conditions and performance characteristics, an ISD Sensor Network Test Bed has been designed and deployed at the Savannah River National Laboratory. The test bed addresses the DOE-EM Technology Need to develop a remote monitoring system to determine and verify ISD system performance. Commercial off-the-shelf sensors have been installed on concrete blocks taken from walls of the P Reactor Building. Deployment of this low-cost structural monitoring system provides hands-on experience with sensor networks. The initial sensor system consists of: (1) Groutable thermistors for temperature and moisture monitoring; (2) Strain gauges for crack growth monitoring; (3) Tiltmeters for settlement monitoring; and (4) A communication system for data collection. Preliminary baseline data and lessons learned from system design and installation and initial field testing will be utilized for future ISD sensor network development and deployment.

  6. Autonomous monitoring of control hardware to predict off-normal conditions using NIF automatic alignment systems

    International Nuclear Information System (INIS)

    Awwal, Abdul A.S.; Wilhelmsen, Karl; Leach, Richard R.; Miller-Kamm, Vicki; Burkhart, Scott; Lowe-Webb, Roger; Cohen, Simon

    2012-01-01

    Highlights: ► An automatic alignment system was developed to process images of the laser beams. ► System uses processing to adjust a series of control loops until alignment criteria are satisfied. ► Monitored conditions are compared against nominal values with an off-normal alert. ► Automated health monitoring system trends off-normals with a large image history. - Abstract: The National Ignition Facility (NIF) is a high power laser system capable of supporting high-energy-density experimentation as a user facility for the next 30 years. In order to maximize the facility availability, preventive maintenance enhancements are being introduced into the system. An example of such an enhancement is a camera-based health monitoring system, integrated into the automated alignment system, which provides an opportunity to monitor trends in measurements such as average beam intensity, size of the beam, and pixel saturation. The monitoring system will generate alerts based on observed trends in measurements to allow scheduled pro-active maintenance before routine off-normal detection stops system operations requiring unscheduled intervention.

  7. Autonomous monitoring of control hardware to predict off-normal conditions using NIF automatic alignment systems

    Energy Technology Data Exchange (ETDEWEB)

    Awwal, Abdul A.S., E-mail: awwal1@llnl.gov [Lawrence Livermore National Laboratory, Livermore, CA 94550 (United States); Wilhelmsen, Karl; Leach, Richard R.; Miller-Kamm, Vicki; Burkhart, Scott; Lowe-Webb, Roger; Cohen, Simon [Lawrence Livermore National Laboratory, Livermore, CA 94550 (United States)

    2012-12-15

    Highlights: Black-Right-Pointing-Pointer An automatic alignment system was developed to process images of the laser beams. Black-Right-Pointing-Pointer System uses processing to adjust a series of control loops until alignment criteria are satisfied. Black-Right-Pointing-Pointer Monitored conditions are compared against nominal values with an off-normal alert. Black-Right-Pointing-Pointer Automated health monitoring system trends off-normals with a large image history. - Abstract: The National Ignition Facility (NIF) is a high power laser system capable of supporting high-energy-density experimentation as a user facility for the next 30 years. In order to maximize the facility availability, preventive maintenance enhancements are being introduced into the system. An example of such an enhancement is a camera-based health monitoring system, integrated into the automated alignment system, which provides an opportunity to monitor trends in measurements such as average beam intensity, size of the beam, and pixel saturation. The monitoring system will generate alerts based on observed trends in measurements to allow scheduled pro-active maintenance before routine off-normal detection stops system operations requiring unscheduled intervention.

  8. Indicators for monitoring of safety operation and condition of nuclear power stations

    International Nuclear Information System (INIS)

    Manova, D.

    2001-01-01

    A common goal of all employees in the nuclear power field is safety operation of nuclear power stations. The evaluation and control of NPP safety operation are a part of the elements of safety management. The present report is related only to a part of the total assessment and control of the plant safety operation, namely - the indicator system for monitoring of Kozloduy NPP operation and condition. (author)

  9. Monitoring of Double-Stud Wall Moisture Conditions in the Northeast

    Energy Technology Data Exchange (ETDEWEB)

    Ueno, K. [Building Science Corporation, Westford, MA (United States)

    2015-03-01

    Double-stud walls insulated with cellulose or low-density spray foam can have R-values of 40 or higher. However, double-stud walls have a higher risk of interior-sourced condensation moisture damage when compared with high-R approaches using exterior insulating sheathing. Moisture conditions in double-stud walls were monitored in Zone 5A (Massachusetts); three double-stud assemblies were compared.

  10. Remote support services using condition monitoring and online sensor data for offshore oilfield

    OpenAIRE

    Du, Baoli

    2013-01-01

    Master's thesis in Offshore technology Based on advanced technology in condition monitoring and online sensor data, a new style of operation and maintenance management called remote operation and maintenance support services has been created to improve oil and gas E&P performance. This master thesis will look into how the remote support service is conducted including the concept, design, technology and management philosophies; the current implementation of remote support services in China,...

  11. A STUDY OF CONDITION MONITORING IN WATER PIPE USING VIBRATION SENSOR

    OpenAIRE

    角田, 裕紀

    2013-01-01

    This paper describes a study of condition monitoring in water pipe using vibration sensor. The vibration sensor composed of condenser microphone is placed at water pipe. This sensor picks up vibration by water flow. We estimate of flow rate from the output voltage waveform. It is high cost that any conventional flowmeter which use at outside pipe such as ultrasonic flowmeter. We develop a lower cost system and make measurement of flow rate in water pipe easier. The validity of sensing pipe vi...

  12. Informal and formal trail monitoring protocols and baseline conditions: Acadia National Park

    Science.gov (United States)

    Marion, Jeffrey L.; Wimpey, Jeremy F.; Park, L.

    2011-01-01

    At Acadia National Park, changing visitor use levels and patterns have contributed to an increasing degree of visitor use impacts to natural and cultural resources. To better understand the extent and severity of these resource impacts and identify effective management techniques, the park sponsored this research to develop monitoring protocols, collect baseline data, and identify suggestions for management strategies. Formal and informal trails were surveyed and their resource conditions were assessed and characterized to support park planning and management decision-making.

  13. Energy-efficient strain gauges for the wireless condition monitoring systems in mechanical engineering

    Energy Technology Data Exchange (ETDEWEB)

    Berndt, Michael; Fellner, Thomas; Zeiser, Roderich; Wilde, Juergen [Freiburg Univ. (Germany). Dept. for Microsystems Engineering (IMTEK)

    2012-07-01

    This work focuses on the development of novel strain gauges, which are suited for the operation in autonomous wireless condition monitoring systems. For this purpose, capacitive as well as highly resistive strain gauges were designed and fabricated. The C- and R-sensors were utilised in combination with demonstration circuits, which integrate the circuits for instrumentation, A/D-conversion and furthermore comprise a microcontroller with a wireless transceiver system, all on a small separate printed wiring board. (orig.)

  14. Development of an In-Situ Decommissioning Sensor Network Test Bed for Structural Condition Monitoring - 12156

    Energy Technology Data Exchange (ETDEWEB)

    Zeigler, Kristine E.; Ferguson, Blythe A. [Savannah River National Laboratory, Aiken, South Carolina 29808 (United States)

    2012-07-01

    The Savannah River National Laboratory (SRNL) has established an In Situ Decommissioning (ISD) Sensor Network Test Bed, a unique, small scale, configurable environment, for the assessment of prospective sensors on actual ISD system material, at minimal cost. The Department of Energy (DOE) is presently implementing permanent entombment of contaminated, large nuclear structures via ISD. The ISD end state consists of a grout-filled concrete civil structure within the concrete frame of the original building. Validation of ISD system performance models and verification of actual system conditions can be achieved through the development a system of sensors to monitor the materials and condition of the structure. The ISD Sensor Network Test Bed has been designed and deployed to addresses the DOE-Environmental Management Technology Need to develop a remote monitoring system to determine and verify ISD system performance. Commercial off-the-shelf sensors have been installed on concrete blocks taken from walls of the P Reactor Building at the Savannah River Site. Deployment of this low-cost structural monitoring system provides hands-on experience with sensor networks. The initial sensor system consists of groutable thermistors for temperature and moisture monitoring, strain gauges for crack growth monitoring, tilt-meters for settlement monitoring, and a communication system for data collection. Baseline data and lessons learned from system design and installation and initial field testing will be utilized for future ISD sensor network development and deployment. The Sensor Network Test Bed at SRNL uses COTS sensors on concrete blocks from the outer wall of the P Reactor Building to measure conditions expected to occur in ISD structures. Knowledge and lessons learned gained from installation, testing, and monitoring of the equipment will be applied to sensor installation in a meso-scale test bed at FIU and in future ISD structures. The initial data collected from the sensors

  15. Detection of correct and incorrect measurements in real-time continuous glucose monitoring systems by applying a postprocessing support vector machine.

    Science.gov (United States)

    Leal, Yenny; Gonzalez-Abril, Luis; Lorencio, Carol; Bondia, Jorge; Vehi, Josep

    2013-07-01

    Support vector machines (SVMs) are an attractive option for detecting correct and incorrect measurements in real-time continuous glucose monitoring systems (RTCGMSs), because their learning mechanism can introduce a postprocessing strategy for imbalanced datasets. The proposed SVM considers the geometric mean to obtain a more balanced performance between sensitivity and specificity. To test this approach, 23 critically ill patients receiving insulin therapy were monitored over 72 h using an RTCGMS, and a dataset of 537 samples, classified according to International Standards Organization (ISO) criteria (372 correct and 165 incorrect measurements), was obtained. The results obtained were promising for patients with septic shock or with sepsis, for which the proposed system can be considered as reliable. However, this approach cannot be considered suitable for patients without sepsis.

  16. Monitoring growth condition of spring maize in Northeast China using a process-based model

    Science.gov (United States)

    Wang, Peijuan; Zhou, Yuyu; Huo, Zhiguo; Han, Lijuan; Qiu, Jianxiu; Tan, Yanjng; Liu, Dan

    2018-04-01

    Early and accurate assessment of the growth condition of spring maize, a major crop in China, is important for the national food security. This study used a process-based Remote-Sensing-Photosynthesis-Yield Estimation for Crops (RS-P-YEC) model, driven by satellite-derived leaf area index and ground-based meteorological observations, to simulate net primary productivity (NPP) of spring maize in Northeast China from the first ten-day (FTD) of May to the second ten-day (STD) of August during 2001-2014. The growth condition of spring maize in 2014 in Northeast China was monitored and evaluated spatially and temporally by comparison with 5- and 13-year averages, as well as 2009 and 2013. Results showed that NPP simulated by the RS-P-YEC model, with consideration of multi-scattered radiation inside the crop canopy, could reveal the growth condition of spring maize more reasonably than the Boreal Ecosystem Productivity Simulator. Moreover, NPP outperformed other commonly used vegetation indices (e.g., Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) for monitoring and evaluating the growth condition of spring maize. Compared with the 5- and 13-year averages, the growth condition of spring maize in 2014 was worse before the STD of June and after the FTD of August, and it was better from the third ten-day (TTD) of June to the TTD of July across Northeast China. Spatially, regions with slightly worse and worse growth conditions in the STD of August 2014 were concentrated mainly in central Northeast China, and they accounted for about half of the production area of spring maize in Northeast China. This study confirms that NPP is a good indicator for monitoring and evaluating growth condition because of its capacity to reflect the physiological characteristics of crops. Meanwhile, the RS-P-YEC model, driven by remote sensing and ground-based meteorological data, is effective for monitoring crop growth condition over large areas in a near real

  17. The condition monitoring system of turbine system components for nuclear power plants

    International Nuclear Information System (INIS)

    Ono, Shigetoshi

    2013-01-01

    The thermal and nuclear power plants have been imposed a stable supply of electricity. To certainly achieve this, we built the plant condition monitoring system based on the heat and mass balance calculation. If there are some performance changes on the turbine system components of their power plants, the heat and mass balance of the turbine system will change. This system has ability to detect the abnormal signs of their components by finding the changes of the heat and mass balance. Moreover we note that this system is built for steam turbine cycle operating with saturated steam conditions. (author)

  18. Run II performance of luminosity and beam condition monitors at CMS

    Energy Technology Data Exchange (ETDEWEB)

    Leonard, Jessica Lynn [DESY, Hamburg (Germany)

    2016-07-01

    The BRIL (Beam Radiation Instrumentation and Luminosity) system of CMS consists of instrumentation to measure the luminosity online and offline, and to monitor the LHC beam conditions inside CMS. An accurate luminosity measurement is essential to the CMS physics program, and measurement of the beam background is necessary to ensure safe operation of CMS. Many of the BRIL subsystems have been upgraded and others have been added for LHC Run II to complement the existing measurements. The beam condition monitor (BCM) consists of several sets of diamond sensors used to measure online luminosity and beam background with a single-bunch-crossing resolution. The BCM also detects when beam conditions become unfavorable for CMS running and may trigger a beam abort to protect the detector. The beam halo monitor (BHM) uses quartz bars to measure the background of the incoming beams at larger radii. The pixel luminosity telescope (PLT) consists of telescopes of silicon sensors designed to provide a CMS online and offline luminosity measurement. In addition, the forward hadronic calorimeter (HF) delivers an independent luminosity measurement, making the whole system robust and allowing for cross-checks of the systematics. An overview of the performance during 2015 LHC running for the new/updated BRIL subsystems will be given, including the uncertainties of the luminosity measurements.

  19. Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods

    Directory of Open Access Journals (Sweden)

    Peng Guo

    2011-11-01

    Full Text Available Condition Monitoring (CM of wind turbines can greatly reduce the maintenance costs for wind farms, especially for offshore wind farms. A new condition monitoring method for a wind turbine gearbox using temperature trend analysis is proposed. Autoassociative Kernel Regression (AAKR is used to construct the normal behavior model of the gearbox temperature. With a proper construction of the memory matrix, the AAKR model can cover the normal working space for the gearbox. When the gearbox has an incipient failure, the residuals between AAKR model estimates and the measurement temperature will become significant. A moving window statistical method is used to detect the changes of the residual mean value and standard deviation in a timely manner. When one of these parameters exceeds predefined thresholds, an incipient failure is flagged. In order to simulate the gearbox fault, manual temperature drift is added to the initial Supervisory Control and Data Acquisitions (SCADA data. Analysis of simulated gearbox failures shows that the new condition monitoring method is effective.

  20. Results of Recent DOE Research on Development of Cable Condition Monitoring and Aging Management Technologies

    International Nuclear Information System (INIS)

    Campbell, C.J.; McConkey, J.B.; Hashemian, H.M.; Sexton, C.D.; Cummins, D.S.

    2012-01-01

    Analysis and Measurement Services (AMS) Corporation has been conducting two research projects focused on understanding cable aging and developing cable condition monitoring technologies for nuclear power plants. The goal of the first project is to correlate cable faults with testing techniques that can identify and locate the faults whether they are in the cable, conductor, or the insulation. This project involves laboratory experiments using low and medium voltage cable types typically installed in nuclear power plants. The second project is focused on development of an integrated cable condition monitoring system for nuclear facilities. This system integrates a number of cable testing and cable condition monitoring techniques, such as the time domain reflectometry (TDR), frequency domain reflectometry (FDR), inductance, capacitance, resistance (LCR), reverse TDR (RTDR), current-to-voltage (IV) for testing of nuclear instrumentation sensors, insulation resistance (IR) and other techniques. The purpose of the project is to combine all proven technologies into one system to detect and pinpoint problems in cable circuits as well as cable insulation, shield, or jacket material. (author)

  1. Demonstration of TEG-powered wireless autonomous transducer solution for condition monitoring in industrial environment

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Ziyang; Patrascu, Mihai; Su, Jiale; Vullers, Ruud J.M. [imec the Netherlands, Eindhoven (Netherlands)

    2011-07-01

    Imec/Holst Centre focuses on the development of wireless autonomous transducer solution, which is poised to bring about huge impact in the sectors of health care, machinery, transportation and energy, etc. In this paper, we first showcase a TEG-powered demonstration for condition monitoring in industrial environment. Composing of sensor-actuator, front-end interface, digital signal processing unit and radio, the developed wireless sensor node can monitor the changing operating condition, i.e. the loading on a rolling-element bearing, on a rotating shaft. The use of a specially designed TEG, working in tandem with an energy storage device, can significantly improve the energy autonomy of the condition monitoring system as a whole. The different components in the demonstration are presented. Subsequently, the experimental results of vibration signature analysis are exhibited. On one hand, the presented demonstration sheds light on the huge potential of thermoelectric energy harvesting to achieve energy autonomy. On the other hand, it also points to the aspects that are in need of further development, namely miniaturization and cost reduction of energy harvesters. Aimed at the delivery of cost-effective miniaturized thermoelectric harvesting devices, imec/Holst Centre has been tackling with the relevant challenges by resorting to, but not limited to, its expertise in micromachining. An update on the latest research results is subsequently given with regard to various micromachined thermoelectric devices, fully fledged wearable TEGs with custom designed package and thermoelectric material property optimization. (orig.)

  2. Modelling and measurement of wear particle flow in a dual oil filter system for condition monitoring

    DEFF Research Database (Denmark)

    Henneberg, Morten; Eriksen, René Lynge; Fich, Jens

    2016-01-01

    . The quantity of wear particles in gear oil is analysed with respect to system running conditions. It is shown that the model fits the data in terms of startup “particle burst” phenomenon, quasi-stationary conditions during operation, and clean-up filtration when placed out of operation. In order to establish...... boundary condition for particle burst phenomenon, the release of wear particles from a pleated mesh filter is measured in a test rig and included in the model. The findings show that a dual filter model, with startup phenomenon included, can describe trends in the wear particle flow observed in the gear...... particle generation is made possible by model parameter estimation and identification of an unintended lack of filter change. The model may also be used to optimise system and filtration performance, and to enable continuous condition monitoring....

  3. Assessment of monitored energy use and thermal comfort conditions in mosques in hot-humid climates

    Energy Technology Data Exchange (ETDEWEB)

    Al-Homoud, Mohammad S.; Abdou, Adel A.; Budaiwi, Ismail M. [Architectural Engineering Department, KFUPM, Dhahran 31261 (Saudi Arabia)

    2009-06-15

    In harsh climatic regions, buildings require air-conditioning in order to provide an acceptable level of thermal comfort. In many situations buildings are over cooled or the HVAC system is kept running for a much longer time than needed. In some other situations thermal comfort is not achieved due to improper operation practices coupled with poor maintenance and even lack it, and consequently inefficient air-conditioning systems. Mosques represent one type of building that is characterized by their unique intermittent operating schedule determined by prayer times, which vary continuously according to the local solar time. This paper presents the results of a study designed to monitor energy use and thermal comfort conditions of a number of mosques in a hot-humid climate so that both energy efficiency and the quality of thermal comfort conditions especially during occupancy periods in such intermittently operated buildings can be assessed accurately. (author)

  4. Investigation of an experimental ejector refrigeration machine operating with refrigerant R245fa at design and off-design working conditions. Part 1. Theoretical analysis

    KAUST Repository

    Shestopalov, K.O.

    2015-07-01

    © 2015 Elsevier Ltd and IIR.All rights reserved. The ejector refrigeration machine (ERM) offers several advantages over other heat-driven refrigeration machine, including simplicity in design and operation, high reliability and low installation cost, which enable its wide application in the production of cooling. In this paper the theoretical analysis of ejector design and ejector refrigeration cycle performance is presented. It is shown that ERM performance characteristics depend strongly on the operating conditions, the efficiency of the ejector used, and the thermodynamic properties of the refrigerant used. A 1-D model for the prediction of the entrainment ratio ω, and an optimal design for ejectors with cylindrical and conical-cylindrical mixing chambers are presented in this paper. In order to increase ERM performance values, it is necessary first of all to improve the performance of the ejector.

  5. Innovation prize for air-conditioned assembly shop - Constant temperature allows the assembly of high-precision machining centres; Innovationspreis fuer klimatisierte Montagehalle

    Energy Technology Data Exchange (ETDEWEB)

    Schmid, W.

    2002-07-01

    This article describes the clever combination of various techniques to achieve the goal of providing a stable ambient temperature with an accuracy of +/- 1 K in the assembly shop of a German manufacturer of precision machine tools. The requirements placed on the assembly and operation of machine tools operating to an accuracy of less that a hundredth of a millimetre are discussed. The award-winning heating and cooling system, which features the use of gravity cooling, geothermal energy (ground water for cooling) and the use of constructional elements (floor, facades, windows) for thermal buffering is described. The ingenious control system with 32 control zones and 64 sensors is described, which also provides the company's management with long-term documentation of temperature conditions for quality assurance purposes. Technical data on the installation is provided in table form.

  6. Impact Analysis of Temperature and Humidity Conditions on Electrochemical Sensor Response in Ambient Air Quality Monitoring

    Directory of Open Access Journals (Sweden)

    Peng Wei

    2018-01-01

    Full Text Available The increasing applications of low-cost air sensors promises more convenient and cost-effective systems for air monitoring in many places and under many conditions. However, the data quality from such systems has not been fully characterized and may not meet user expectations in research and regulatory uses, or for use in citizen science. In our study, electrochemical sensors (Alphasense B4 series for carbon monoxide (CO, nitric oxide (NO, nitrogen dioxide (NO2, and oxidants (Ox were evaluated under controlled laboratory conditions to identify the influencing factors and quantify their relation with sensor outputs. Based on the laboratory tests, we developed different correction methods to compensate for the impact of ambient conditions. Further, the sensors were assembled into a monitoring system and tested in ambient conditions in Hong Kong side-by-side with regulatory reference monitors, and data from these tests were used to evaluate the performance of the models, to refine them, and validate their applicability in variable ambient conditions in the field. The more comprehensive correction models demonstrated enhanced performance when compared with uncorrected data. One over-arching observation of this study is that the low-cost sensors may promise excellent sensitivity and performance, but it is essential for users to understand and account for several key factors that may strongly affect the nature of sensor data. In this paper, we also evaluated factors of multi-month stability, temperature, and humidity, and considered the interaction of oxidant gases NO2 and ozone on a newly introduced oxidant sensor.

  7. Impact Analysis of Temperature and Humidity Conditions on Electrochemical Sensor Response in Ambient Air Quality Monitoring

    Science.gov (United States)

    Ning, Zhi; Ye, Sheng; Sun, Li; Yang, Fenhuan; Wong, Ka Chun; Westerdahl, Dane; Louie, Peter K. K.

    2018-01-01

    The increasing applications of low-cost air sensors promises more convenient and cost-effective systems for air monitoring in many places and under many conditions. However, the data quality from such systems has not been fully characterized and may not meet user expectations in research and regulatory uses, or for use in citizen science. In our study, electrochemical sensors (Alphasense B4 series) for carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), and oxidants (Ox) were evaluated under controlled laboratory conditions to identify the influencing factors and quantify their relation with sensor outputs. Based on the laboratory tests, we developed different correction methods to compensate for the impact of ambient conditions. Further, the sensors were assembled into a monitoring system and tested in ambient conditions in Hong Kong side-by-side with regulatory reference monitors, and data from these tests were used to evaluate the performance of the models, to refine them, and validate their applicability in variable ambient conditions in the field. The more comprehensive correction models demonstrated enhanced performance when compared with uncorrected data. One over-arching observation of this study is that the low-cost sensors may promise excellent sensitivity and performance, but it is essential for users to understand and account for several key factors that may strongly affect the nature of sensor data. In this paper, we also evaluated factors of multi-month stability, temperature, and humidity, and considered the interaction of oxidant gases NO2 and ozone on a newly introduced oxidant sensor. PMID:29360749

  8. Technical Report on Preliminary Methodology for Enhancing Risk Monitors with Integrated Equipment Condition Assessment

    Energy Technology Data Exchange (ETDEWEB)

    Ramuhalli, Pradeep; Coles, Garill A.; Coble, Jamie B.; Hirt, Evelyn H.

    2013-09-17

    Small modular reactors (SMRs) generally include reactors with electric output of ~350 MWe or less (this cutoff varies somewhat but is substantially less than full-size plant output of 700 MWe or more). Advanced SMRs (AdvSMRs) refer to a specific class of SMRs and are based on modularization of advanced reactor concepts. AdvSMRs may provide a longer-term alternative to traditional light-water reactors (LWRs) and SMRs based on integral pressurized water reactor concepts currently being considered. Enhancing affordability of AdvSMRs will be critical to ensuring wider deployment. AdvSMRs suffer from loss of economies of scale inherent in small reactors when compared to large (~greater than 600 MWe output) reactors. Some of this loss can be recovered through reduced capital costs through smaller size, fewer components, modular fabrication processes, and the opportunity for modular construction. However, the controllable day-to-day costs of AdvSMRs will be dominated by operation and maintenance (O&M) costs. Technologies that help characterize real-time risk are important for controlling O&M costs. Risk monitors are used in current nuclear power plants to provide a point-in-time estimate of the system risk given the current plant configuration (e.g., equipment availability, operational regime, and environmental conditions). However, current risk monitors are unable to support the capability requirements listed above as they do not take into account plant-specific normal, abnormal, and deteriorating states of active components and systems. This report documents technology developments that are a step towards enhancing risk monitors that, if integrated with supervisory plant control systems, can provide the capability requirements listed and meet the goals of controlling O&M costs. The report describes research results from an initial methodology for enhanced risk monitors by integrating real-time information about equipment condition and POF into risk monitors.

  9. Real Time In-circuit Condition Monitoring of MOSFET in Power Converters

    Directory of Open Access Journals (Sweden)

    Shakeb A. Khan

    2015-03-01

    Full Text Available Abstract:This paper presents simple and low-cost, real time in-circuit condition monitoring of MOSFET in power electronic converters. Design metrics requirements like low cost, small size, high power factor, low percentage of total harmonic distortion etc. requires the power electronic systems to operate at high frequencies and at high power density. Failures of power converters are attributed largely by aging of power MOSFETs at high switching frequencies. Therefore, real time in-circuit prognostic of MOSFET needs to be done before their selection for power system design. Accelerated aging tests are performed in different circuits to determine the wear out failure of critical components based on their parametric degradation. In this paper, the simple and low-cost test beds are designed for real time in-circuit prognostics of power MOSFETs. The proposed condition monitoring scheme helps in estimating the condition of MOSFETs at their maximum rated operating condition and will aid the system designers to test their reliability and benchmark them before selecting in power converters.

  10. Graphite Based Electrode for ECG Monitoring: Evaluation under Freshwater and Saltwater Conditions

    Directory of Open Access Journals (Sweden)

    Tharoeun Thap

    2016-04-01

    Full Text Available We proposed new electrodes that are applicable for electrocardiogram (ECG monitoring under freshwater- and saltwater-immersion conditions. Our proposed electrodes are made of graphite pencil lead (GPL, a general-purpose writing pencil. We have fabricated two types of electrode: a pencil lead solid type (PLS electrode and a pencil lead powder type (PLP electrode. In order to assess the qualities of the PLS and PLP electrodes, we compared their performance with that of a commercial Ag/AgCl electrode, under a total of seven different conditions: dry, freshwater immersion with/without movement, post-freshwater wet condition, saltwater immersion with/without movement, and post-saltwater wet condition. In both dry and post-freshwater wet conditions, all ECG-recorded PQRST waves were clearly discernible, with all types of electrodes, Ag/AgCl, PLS, and PLP. On the other hand, under the freshwater- and saltwater-immersion conditions with/without movement, as well as post-saltwater wet conditions, we found that the proposed PLS and PLP electrodes provided better ECG waveform quality, with significant statistical differences compared with the quality provided by Ag/AgCl electrodes.

  11. RECREATION MONITORING OF RESOURCE CONDITIONS IN THE KRONOTSKY STATE NATURAL BIOSPHERE PRESERVE (KAMCHATKA: AN INITIAL ASSESSMENT

    Directory of Open Access Journals (Sweden)

    Anna Zavadskaya

    2011-01-01

    Full Text Available The paper describes assessment and monitoring program which has been designed and initiated for monitoring recreational impacts in some wildernesses areas of Kamchatka. The framework of the recreational assessment was tested through its application in a case study conducted during the summer 2008 in the Kronotsky State Natural Biosphere Preserve (the Kamchatka peninsula, Russia. The overall objective of the case study was to assess the existing campsite and trail recreation impacts and to establish a network of key sites for the subsequent long-term impact monitoring. The detailed assessment of different components of natural complexes of the Kronotsky State Natural Preserve and the obtained maps of their ecological conditions showed that some sites had been highly disturbed. The results of these works have given rise to a concern that the intensive use of these areas would make an unacceptable impact on the nature. Findings of our initial work corroborate the importance of founding wilderness management programs on knowledge about the trail and campsite impacts and emphasize the necessity of adopting the recreational assessment and monitoring framework to the practice of decision-making.

  12. Monitoring and analysis of air emissions based on condition models derived from process history

    Directory of Open Access Journals (Sweden)

    M. Liukkonen

    2016-12-01

    Full Text Available Evaluation of online information on operating conditions is necessary when reducing air emissions in energy plants. In this respect, automated monitoring and control are of primary concern, particularly in biomass combustion. As monitoring of emissions in power plants is ever more challenging because of low-grade fuels and fuel mixtures, new monitoring applications are needed to extract essential information from the large amount of measurement data. The management of emissions in energy boilers lacks economically efficient, fast, and competent computational systems that could support decision-making regarding the improvement of emission efficiency. In this paper, a novel emission monitoring platform based on the self-organizing map method is presented. The system is capable, not only of visualizing the prevailing status of the process and detecting problem situations (i.e. increased emission release rates, but also of analyzing these situations automatically and presenting factors potentially affecting them. The system is demonstrated using measurement data from an industrial circulating fluidized bed boiler fired by forest residue as the primary fuel and coal as the supporting fuel.

  13. A digital filter-based approach to the remote condition monitoring of railway turnouts

    International Nuclear Information System (INIS)

    Garcia Marquez, Fausto Pedro; Schmid, Felix

    2007-01-01

    Railway operations in Europe have changed dramatically since the early 1990s, partly as a result of new European Union Directives. Performance targets have become more and more exacting, due to reductions in state support for railways and the need to increasing traffic. More intensive operations also place greater demands on the hardware of the railway. This is true for both rolling stock and infrastructure subsystems and components, particularly so in the case of the latter where the time available for maintenance is being reduced. The authors of this paper focus on the railway infrastructure, and more specifically on points. These are critical elements whose reliability is key to the operation of the whole system. Using intelligent monitoring systems, it is possible to predict problems and enable quick recovery before component failures disrupt operations. The authors have studied the application of remote condition monitoring to point mechanisms and their operation, and have identified algorithms which may be used to identify incipient failures. In this paper, the authors propose a Kalman filter for the linear discrete data filtering problem encountered when using current sensor data in a point condition monitoring system. The reason for applying Kalman filtering in this study was to increase the reliability of the model presented to the rule-based decision mechanism

  14. Condition monitoring through advanced sensor and computational technology : final report (January 2002 to May 2005).

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jung-Taek (Korea Atomic Energy Research Institute, Daejon, Korea); Luk, Vincent K.

    2005-05-01

    The overall goal of this joint research project was to develop and demonstrate advanced sensors and computational technology for continuous monitoring of the condition of components, structures, and systems in advanced and next-generation nuclear power plants (NPPs). This project included investigating and adapting several advanced sensor technologies from Korean and US national laboratory research communities, some of which were developed and applied in non-nuclear industries. The project team investigated and developed sophisticated signal processing, noise reduction, and pattern recognition techniques and algorithms. The researchers installed sensors and conducted condition monitoring tests on two test loops, a check valve (an active component) and a piping elbow (a passive component), to demonstrate the feasibility of using advanced sensors and computational technology to achieve the project goal. Acoustic emission (AE) devices, optical fiber sensors, accelerometers, and ultrasonic transducers (UTs) were used to detect mechanical vibratory response of check valve and piping elbow in normal and degraded configurations. Chemical sensors were also installed to monitor the water chemistry in the piping elbow test loop. Analysis results of processed sensor data indicate that it is feasible to differentiate between the normal and degraded (with selected degradation mechanisms) configurations of these two components from the acquired sensor signals, but it is questionable that these methods can reliably identify the level and type of degradation. Additional research and development efforts are needed to refine the differentiation techniques and to reduce the level of uncertainties.

  15. Wind Turbine Condition Monitoring Strategy through Multiway PCA and Multivariate Inference

    Directory of Open Access Journals (Sweden)

    Francesc Pozo

    2018-03-01

    Full Text Available This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA. Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide range of significance, α ∈ [ 1 % , 13 % ] , the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.

  16. Breath acetone to monitor life style interventions in field conditions: an exploratory study.

    Science.gov (United States)

    Samudrala, Devasena; Lammers, Gerwen; Mandon, Julien; Blanchet, Lionel; Schreuder, Tim H A; Hopman, Maria T; Harren, Frans J M; Tappy, Luc; Cristescu, Simona M

    2014-04-01

    To assess whether breath acetone concentration can be used to monitor the effects of a prolonged physical activity on whole body lipolysis and hepatic ketogenesis in field conditions. Twenty-three non-diabetic, 11 type 1 diabetic, and 17 type 2 diabetic subjects provided breath and blood samples for this study. Samples were collected during the International Four Days Marches, in the Netherlands. For each participant, breath acetone concentration was measured using proton transfer reaction ion trap mass spectrometry, before and after a 30-50 km walk on four consecutive days. Blood non-esterified free fatty acid (NEFA), beta-hydroxybutyrate (BOHB), and glucose concentrations were measured after walking. Breath acetone concentration was significantly higher after than before walking, and was positively correlated with blood NEFA and BOHB concentrations. The effect of walking on breath acetone concentration was repeatedly observed on all four consecutive days. Breath acetone concentrations were higher in type 1 diabetic subjects and lower in type 2 diabetic subjects than in control subjects. Breath acetone can be used to monitor hepatic ketogenesis during walking under field conditions. It may, therefore, provide real-time information on fat burning, which may be of use for monitoring the lifestyle interventions. Copyright © 2014 The Obesity Society.

  17. Aging and condition monitoring of electric cables in nuclear power plants

    International Nuclear Information System (INIS)

    Lofaro, R.J.; Grove, E.; Soo, P.

    1998-05-01

    There are a variety of environmental stressors in nuclear power plants that can influence the aging rate of components; these include elevated temperatures, high radiation fields, and humid conditions. Exposure to these stressors over long periods of time can cause degradation of components that may go undetected unless the aging mechanisms are identified and monitored. In some cases the degradation may be mitigated by maintenance or replacement. However, some components receive neither and are thus more susceptible to aging degradation, which might lead to failure. One class of components that falls in this category is electric cables. Cables are very often overlooked in aging analyses since they are passive components that require no maintenance. However, they are very important components since they provide power to safety related equipment and transmit signals to and from instruments and controls. This paper will look at the various aging mechanisms and failure modes associated with electric cables. Condition monitoring techniques that may be useful for monitoring degradation of cables will also be discussed

  18. A diagnostic signal selection scheme for planetary gearbox vibration monitoring under non-stationary operational conditions

    International Nuclear Information System (INIS)

    Feng, Ke; Wang, KeSheng; Zhang, Mian; Ni, Qing; Zuo, Ming J

    2017-01-01

    The planetary gearbox, due to its unique mechanical structures, is an important rotating machine for transmission systems. Its engineering applications are often in non-stationary operational conditions, such as helicopters, wind energy systems, etc. The unique physical structures and working conditions make the vibrations measured from planetary gearboxes exhibit a complex time-varying modulation and therefore yield complicated spectral structures. As a result, traditional signal processing methods, such as Fourier analysis, and the selection of characteristic fault frequencies for diagnosis face serious challenges. To overcome this drawback, this paper proposes a signal selection scheme for fault-emphasized diagnostics based upon two order tracking techniques. The basic procedures for the proposed scheme are as follows. (1) Computed order tracking is applied to reveal the order contents and identify the order(s) of interest. (2) Vold–Kalman filter order tracking is used to extract the order(s) of interest—these filtered order(s) constitute the so-called selected vibrations. (3) Time domain statistic indicators are applied to the selected vibrations for faulty information-emphasized diagnostics. The proposed scheme is explained and demonstrated in a signal simulation model and experimental studies and the method proves to be effective for planetary gearbox fault diagnosis. (paper)

  19. An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

    Science.gov (United States)

    Zhang, Fan; Zhou, Zude; Liu, Quan; Xu, Wenjun

    2017-02-01

    Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.

  20. Development of on-line condition monitoring system in aerospace structures using advanced composite materials

    International Nuclear Information System (INIS)

    Khan, Z.M.

    2005-01-01

    This research aims to develop condition monitoring systems for advanced aerospace composite structures. To perform these functions successfully a smart system is required that could autonomously respond to environmental changes. The integrated structure senses the environments, conveys the message to central processing unit and reacts instantaneously to external stimuli. Such structures not only monitor their own health but also for warn about onset of failures, fatigue and impending disasters. This required development of methods for embedding optical fibers in composite panels for sensing given defect. The thick and cylindrical composite structures have layer waviness due to fiber microbend defect. Such kind of defect is characteristically hard to detect. It leads to delamination, cracking and deterioration of mechanical properties. The experimental investigation revealed correlation of the intensity of light with the microbend defect in composite structure. (author)