WorldWideScience

Sample records for monitor machine condition

  1. 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 ·   ...

  2. Thermal Analysis for Condition Monitoring of Machine Tool Spindles

    Science.gov (United States)

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

    2012-05-01

    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.

  3. 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

  4. Application of TRIZ approach to machine vibration condition monitoring problems

    Science.gov (United States)

    Cempel, Czesław

    2013-12-01

    Up to now machine condition monitoring has not been seriously approached by TRIZ1TRIZ= Russian acronym for Inventive Problem Solving System, created by G. Altshuller ca 50 years ago. users, and the knowledge of TRIZ methodology has not been applied there intensively. However, there are some introductory papers of present author posted on Diagnostic Congress in Cracow (Cempel, in press [11]), and Diagnostyka Journal as well. But it seems to be further need to make such approach from different sides in order to see, if some new knowledge and technology will emerge. In doing this we need at first to define the ideal final result (IFR) of our innovation problem. As a next we need a set of parameters to describe the problems of system condition monitoring (CM) in terms of TRIZ language and set of inventive principles possible to apply, on the way to IFR. This means we should present the machine CM problem by means of contradiction and contradiction matrix. When specifying the problem parameters and inventive principles, one should use analogy and metaphorical thinking, which by definition is not exact but fuzzy, and leads sometimes to unexpected results and outcomes. The paper undertakes this important problem again and brings some new insight into system and machine CM problems. This may mean for example the minimal dimensionality of TRIZ engineering parameter set for the description of machine CM problems, and the set of most useful inventive principles applied to given engineering parameter and contradictions of TRIZ.

  5. 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.

  6. Research on Key Techniques of Condition Monitoring and Fault Diagnosing Systems of Machine Groups

    Institute of Scientific and Technical Information of China (English)

    WANG Yan-kai; LIAO Ming-fu; WANG Si-ji

    2005-01-01

    This paper describes the development of the condition monitoring and fault diagnosing system of a group of rotating machinery. The data management is performed by means of double redundant data bases stored simultaneously in both the analyzing server and monitoring client. In this way, high reliability of the storage of data is guaranteed. Condensation of trend data releases much space resource of the hard disk. Diagnosing strategies orientated to different typical faults of rotating machinery are developed and incorporated into the system. Experimental verification shows that the system is suitable and effective for condition monitoring and fault diagnosing for a rotating machine group.

  7. An Embedded Condition Monitoring and Fault Diagnosis System for Rotary Machines

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    An intelligent machine is the earnest aspiration of people. From the point of view to construct an intelligent machine with self-monitoring and self-diagnosis abilities, the technology for realizing an internet oriented embedded intelligent condition monitoring and fault diagnosis system for the rotating machine with remote monitoring, diagnosis, maintenance and upgrading functions is introduced systematically. Based on the DSP ( Digital Signal Processor) and embedded microcomputer, the system can measure and store the machine work status in real time, such as the rotating speed and vibration,etc. In the system, the DSP chip is used to do the fault signal processing and feature extraction, and the embedded microcomputer with a customized Linux operation system is used to realize the internet oriented remote software upgrading and system maintenance. Embedded fault diagnosis software based on mobile agent technology is also designed in the system, which can interconnect with the remote fault diagnosis center to realize the collaborative diagnosis. The embedded condition monitoring and fault diagnosis technology proposed in this paper will effectively improve the intelligence degree of the fault diagnosis system.

  8. 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.)

  9. Approach towards sensor placement, selection and fusion for real-time condition monitoring of precision machines

    Science.gov (United States)

    Er, Poi Voon; Teo, Chek Sing; Tan, Kok Kiong

    2016-02-01

    Moving mechanical parts in a machine will inevitably generate vibration profiles reflecting its operating conditions. Vibration profile analysis is a useful tool for real-time condition monitoring to avoid loss of performance and unwanted machine downtime. In this paper, we propose and validate an approach for sensor placement, selection and fusion for continuous machine condition monitoring. The main idea is to use a minimal series of sensors mounted at key locations of a machine to measure and infer the actual vibration spectrum at a critical point where it is not suitable to mount a sensor. The locations for sensors' mountings which are subsequently used for vibration inference are identified based on sensitivity calibration at these locations moderated with normalized Fisher Information (NFI) associated with the measurement quality of the sensor at that location. Each of the identified sensor placement location is associated with one or more sensitive frequencies for which it ranks top in terms of the moderated sensitivities calibrated. A set of Radial Basis Function (RBF), each of them associated with a range of sensitive frequencies, is used to infer the vibration at the critical point for that frequency. The overall vibration spectrum of the critical point is then fused from these components. A comprehensive set of experimental results for validation of the proposed approach is provided in the paper.

  10. Development of Cutting Condition Monitoring System for ID-blade Saw Slicing Machine

    Institute of Scientific and Technical Information of China (English)

    JIANG Zhongwei; LI Fenlan; KAWASHIMA Kazuo

    2006-01-01

    The purpose of this study is focused on development of an online monitoring system for measuring and evaluating the cutting condition as the ID-blade saw is cutting a silicon ingot. First, the cutting experiments are carried out and the cutting signals during the blade slicing a six-inch ingot are measured by a 3-axes load sensor which is mounted on the top of the ingot. To evaluate the blade condition in slicing, a novel data processing method, combining the discrete Fourier transform(DFT) with the discrete Wavelet transform(DWT), is proposed in this paper for extracting the components due to the rotation of the blade and the cutting impedance. To validate the effect of the method, four ID-blades with three different types of the blade edge are used and discussed. The obtained results show that the component induced from the rotation and the component due to the blade slicing can be extracted efficiently by introduction of the proposed method. Furthermore, a simple online monitoring system,which consists of a 3-axes load sensor or acceleration sensor, DC cuts high-pass filter, and AD converter embedded microcomputer, is designed. The estimated cutting condition information obtained from the proposed monitoring system can be used as a feedback signal to the slicing machine for production of high quality wafer.

  11. An Illustration of New Methods in Machine Condition Monitoring, Part II: Adaptive outlier detection

    Science.gov (United States)

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

    2017-05-01

    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 second paper in the pair will deal with novelty detection. Although there has been considerable progress in the use of outlier analysis for novelty detection, most of the papers produced so far have suffered from the fact that simple algorithms break down if multiple outliers are present or if damage is already present in a training set. The objective of the current paper is to illustrate the use of phase-space thresholding; an algorithm which has the ability to detect multiple outliers inclusively in a data set.

  12. 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 for extensive datasets that are representative of different machine fault conditions. The envelope of the filtered signal is referred to as a discrepancy transform, since the discrepancy signal indicates the presence of fault-induced signal distortions...

  13. Feature Selection by Merging Sequential Bidirectional Search into Relevance Vector Machine in Condition Monitoring

    Institute of Scientific and Technical Information of China (English)

    ZHANG Kui; DONG Yu; BALL Andrew

    2015-01-01

    For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties in fault classification. Actually, the classification methods are simply intractable when applied to high-dimensional condition monitoring data. In order to solve the problem, engineers have to resort to complicated feature extraction methods to reduce the dimensionality of data. However, the features transformed by the methods cannot be understood by the engineers due to a loss of the original engineering meaning. In this paper, other forms of dimensionality reduction technique(feature selection methods) are employed to identify machinery condition, based only on frequency spectrum data. Feature selection methods are usually divided into three main types: filter, wrapper and embedded methods. Most studies are mainly focused on the first two types, whilst the development and application of the embedded feature selection methods are very limited. This paper attempts to explore a novel embedded method. The method is formed by merging a sequential bidirectional search algorithm into scale parameters tuning within a kernel function in the relevance vector machine. To demonstrate the potential for applying the method to machinery fault diagnosis, the method is implemented to rolling bearing experimental data. The results obtained by using the method are consistent with the theoretical interpretation, proving that this algorithm has important engineering significance in revealing the correlation between the faults and relevant frequency features. The proposed method is a theoretical extension of relevance vector machine, and provides an effective solution to detect the fault-related frequency components with high efficiency.

  14. Feature selection by merging sequential bidirectional search into relevance vector machine in condition monitoring

    Science.gov (United States)

    Zhang, Kui; Dong, Yu; Ball, Andrew

    2015-11-01

    For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties in fault classification. Actually, the classification methods are simply intractable when applied to high-dimensional condition monitoring data. In order to solve the problem, engineers have to resort to complicated feature extraction methods to reduce the dimensionality of data. However, the features transformed by the methods cannot be understood by the engineers due to a loss of the original engineering meaning. In this paper, other forms of dimensionality reduction technique(feature selection methods) are employed to identify machinery condition, based only on frequency spectrum data. Feature selection methods are usually divided into three main types: filter, wrapper and embedded methods. Most studies are mainly focused on the first two types, whilst the development and application of the embedded feature selection methods are very limited. This paper attempts to explore a novel embedded method. The method is formed by merging a sequential bidirectional search algorithm into scale parameters tuning within a kernel function in the relevance vector machine. To demonstrate the potential for applying the method to machinery fault diagnosis, the method is implemented to rolling bearing experimental data. The results obtained by using the method are consistent with the theoretical interpretation, proving that this algorithm has important engineering significance in revealing the correlation between the faults and relevant frequency features. The proposed method is a theoretical extension of relevance vector machine, and provides an effective solution to detect the fault-related frequency components with high efficiency.

  15. 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.

  16. Preliminary Study on Machining Condition Monitoring System Using 3-Channel Force Sensor Analyzed by I-kaz Multilevel Method

    Directory of Open Access Journals (Sweden)

    Z. Karim

    2016-08-01

    Full Text Available Cutting tool wear is one of the major problems affecting the finished product in term of surface finish quality, dimensional precision and the cost of the defect. This paper discusses the preliminary study on machining condition monitoring system using force data captured using 3-channel force sensor. The data were analyzed by I-kaz multilevel method to monitor the flank wear progression during the machining. The flank wear of the cutting insert was measured using Moticom magnifier under two different operational conditions in turning process. A 3-channel Kistler force sensor was assembled to hold the tool holder to measure the force on the cutting tool in the tangential, radial and feed direction during the machining process. The signals were transmitted to the data acquisition equipment, and finally to the computer system. I-kaz multilevel method was used to identify and characterize the changes in the signals from the sensors under two different experimental set up. The values of I-kaz multilevel coefficients for all channels are strongly correlated with the cutting tool wear condition. This preliminary study can be further developed to efficiently monitor and predict flank wear level which can be used in the real machining industry.

  17. Channel Efficiency with Security Enhancement for Remote Condition Monitoring of Multi Machine System Using Hybrid Huffman Coding

    Science.gov (United States)

    Datta, Jinia; Chowdhuri, Sumana; Bera, Jitendranath

    2016-12-01

    This paper presents a novel scheme of remote condition monitoring of multi machine system where a secured and coded data of induction machine with different parameters is communicated between a state-of-the-art dedicated hardware Units (DHU) installed at the machine terminal and a centralized PC based machine data management (MDM) software. The DHUs are built for acquisition of different parameters from the respective machines, and hence are placed at their nearby panels in order to acquire different parameters cost effectively during their running condition. The MDM software collects these data through a communication channel where all the DHUs are networked using RS485 protocol. Before transmitting, the parameter's related data is modified with the adoption of differential pulse coded modulation (DPCM) and Huffman coding technique. It is further encrypted with a private key where different keys are used for different DHUs. In this way a data security scheme is adopted during its passage through the communication channel in order to avoid any third party attack into the channel. The hybrid mode of DPCM and Huffman coding is chosen to reduce the data packet length. A MATLAB based simulation and its practical implementation using DHUs at three machine terminals (one healthy three phase, one healthy single phase and one faulty three phase machine) proves its efficacy and usefulness for condition based maintenance of multi machine system. The data at the central control room are decrypted and decoded using MDM software. In this work it is observed that Chanel efficiency with respect to different parameter measurements has been increased very much.

  18. Modeling the Relationship between Vibration Features and Condition Parameters Using Relevance Vector Machines for Health Monitoring of Rolling Element Bearings under Varying Operation Conditions

    Directory of Open Access Journals (Sweden)

    Lei Hu

    2015-01-01

    Full Text Available Rotational speed and load usually change when rotating machinery works. Both this kind of changing operational conditions and machine fault could make the mechanical vibration characteristics change. Therefore, effective health monitoring method for rotating machinery must be able to adjust during the change of operational conditions. This paper presents an adaptive threshold model for the health monitoring of bearings under changing operational conditions. Relevance vector machines (RVMs are used for regression of the relationships between the adaptive parameters of the threshold model and the statistical characteristics of vibration features. The adaptive threshold model is constructed based on these relationships. The health status of bearings can be indicated via detecting whether vibration features exceed the adaptive threshold. This method is validated on bearings running at changing speeds. The monitoring results show that this method is effective as long as the rotational speed is higher than a relative small value.

  19. 'MDI Wind' machine diagnostic interface. The online condition monitoring system that's not just for wind turbines; Machine Diagnostic Interface 'MDI-Wind'. Online Condition Monitoring System nicht nur fuer Windenergieanlagen

    Energy Technology Data Exchange (ETDEWEB)

    Eicke, Andreas [ThyssenKrupp System Engineering GmbH, Langenhagen (Germany). Messtechnik

    2012-07-01

    It is becoming increasingly important to be able to implement condition monitoring to protect high-grade investments such as wind turbines and other major industrial plant and installations. ThyssenKrupp System Engineering has developed a Machine Diagnostic Interface (MDI) for this purpose that is based on proven and reliable standard components in terms of the hardware used. As regards the software, the measuring and automation system used is based on mature technology, was developed in-house and has proved its worth over many years on testing and assembly lines in the automotive and supply industry. The basic concept of the Condition Monitoring System (CMS) and the essential technical elements of the MDI are introduced here. The development was funded by the German Federal Ministry of Economics and Technology (BMWi). (orig.)

  20. Method and apparatus for monitoring machine performance

    Science.gov (United States)

    Smith, Stephen F.; Castleberry, Kimberly N.

    1996-01-01

    Machine operating conditions can be monitored by analyzing, in either the time or frequency domain, the spectral components of the motor current. Changes in the electric background noise, induced by mechanical variations in the machine, are correlated to changes in the operating parameters of the machine.

  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

    Science.gov (United States)

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

    2011-07-01

    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. Five-Axis Machine Tool Condition Monitoring Using dSPACE Real-Time System

    Science.gov (United States)

    Sztendel, S.; Pislaru, C.; Longstaff, A. P.; Fletcher, S.; Myers, A.

    2012-05-01

    This paper presents the design, development and SIMULINK implementation of the lumped parameter model of C-axis drive from GEISS five-axis CNC machine tool. The simulated results compare well with the experimental data measured from the actual machine. Also the paper describes the steps for data acquisition using ControlDesk and hardware-in-the-loop implementation of the drive models in dSPACE real-time system. The main components of the HIL system are: the drive model simulation and input - output (I/O) modules for receiving the real controller outputs. The paper explains how the experimental data obtained from the data acquisition process using dSPACE real-time system can be used for the development of machine tool diagnosis and prognosis systems that facilitate the improvement of maintenance activities.

  4. 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 ...... diferent processing conditions were evaluated to correlate the process parameter levels influence on the selected responses, considering both average values (for processes characterization) and standard derivations (for process robustness assessment)....

  5. Computational Intelligence for Condition Monitoring

    OpenAIRE

    Marwala, Tshilidzi; Vilakazi, Christina Busisiwe

    2007-01-01

    Condition monitoring techniques are described in this chapter. Two aspects of condition monitoring process are considered: (1) feature extraction; and (2) condition classification. Feature extraction methods described and implemented are fractals, Kurtosis and Mel-frequency Cepstral Coefficients. Classification methods described and implemented are support vector machines (SVM), hidden Markov models (HMM), Gaussian mixture models (GMM) and extension neural networks (ENN). The effectiveness of...

  6. Studies of the Machine Induced Background, simulations for the design of the Beam Condition Monitor and implementation of the Inclusive $\\phi$ Trigger at the LHCb experiment at CERN

    CERN Document Server

    Lieng, Magnus

    2011-01-01

    LHCb is one of the four major experiments of the LHC at CERN, built to perform precision measurements of CP violation and rare decays. In order to protect the sensitive elements of the experiment from adverse beam conditions the Beam Condition Monitor has been created. Such conditions increase the particle flux arriving from the LHC, known as Machine Induced Background. These particles interfere with the experiment, for example through the physics trigger. In this thesis software development and simulations for the design and validation of the Beam Condition Monitor is shown, ranging from LHCb-specific algorithm implementation to beam dump threshold determination. Furthermore, software development in order to attain a complete simulation chain of machine induced background is shown. The results of these simulations are compared to early data collected at LHCb. Lastly, the development and implementation of the Inclusive $\\phi$ trigger line for the High Level Trigger is presented. This line aims to reconstruct ...

  7. Computational Intelligence for Condition Monitoring

    CERN Document Server

    Marwala, Tshilidzi

    2007-01-01

    Condition monitoring techniques are described in this chapter. Two aspects of condition monitoring process are considered: (1) feature extraction; and (2) condition classification. Feature extraction methods described and implemented are fractals, Kurtosis and Mel-frequency Cepstral Coefficients. Classification methods described and implemented are support vector machines (SVM), hidden Markov models (HMM), Gaussian mixture models (GMM) and extension neural networks (ENN). The effectiveness of these features were tested using SVM, HMM, GMM and ENN on condition monitoring of bearings and are found to give good results.

  8. 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

  9. 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.

  10. Computer monitors mine conditions

    Energy Technology Data Exchange (ETDEWEB)

    Brezovec, D.

    1981-08-01

    At Cape Breton Development Corp's No. 26 Colliery in Canada, a Transmitton microprocessor-based system monitors methane concentrations, air velocities and pressures, fan vibration, machine temperatures and pump pressures continuously. Longwall mining at the colliery operating under the ocean is briefly described.

  11. Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform.

    Science.gov (United States)

    Ben Salem, Samira; Bacha, Khmais; Chaari, Abdelkader

    2012-09-01

    In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor.

  12. Monitoring Vibration of A Model of Rotating Machine

    Directory of Open Access Journals (Sweden)

    Arko Djajadi

    2012-03-01

    Full Text Available Mechanical movement or motion of a rotating machine normally causes additional vibration. A vibration sensing device must be added to constantly monitor vibration level of the system having a rotating machine, since the vibration frequency and amplitude cannot be measured quantitatively by only sight or touch. If the vibration signals from the machine have a lot of noise, there are possibilities that the rotating machine has defects that can lead to failure. In this experimental research project, a vibration structure is constructed in a scaled model to simulate vibration and to monitor system performance in term of vibration level in case of rotation with balanced and unbalanced condition. In this scaled model, the output signal of the vibration sensor is processed in a microcontroller and then transferred to a computer via a serial communication medium, and plotted on the screen with data plotter software developed using C language. The signal waveform of the vibration is displayed to allow further analysis of the vibration. Vibration level monitor can be set in the microcontroller to allow shutdown of the rotating machine in case of excessive vibration to protect the rotating machine from further damage. Experiment results show the agreement with theory that unbalance condition on a rotating machine can lead to larger vibration amplitude compared to balance condition. Adding and reducing the mass for balancing can be performed to obtain lower vibration level. 

  13. Monitoring Grinding Wheel Redress-life Using Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    Xun Chen; Thitikorn Limchimchol

    2006-01-01

    Condition monitoring is a very important aspect in automated manufacturing processes. Any malfunction of a machining process will deteriorate production quality and efficiency. This paper presents an application of support vector machines in grinding process monitoring. The paper starts with an overview of grinding behaviour. Grinding force is analysed through a Short Time Fourier Transform (STFT) to identify features for condition monitoring. The Support Vector Machine (SVM) methodology is introduced as a powerful tool for the classification of different wheel wear situations.After training with available signal data, the SVM is able to identify the state of a grinding process. The requirement and strategy for using SVM for grinding process monitoring is discussed, while the result of the example illustrates how effective SVMs can be in determining wheel redress-life.

  14. Monitor For Electrical-Discharge Machining

    Science.gov (United States)

    Burley, Richard K.

    1993-01-01

    Circuit monitors electrical-discharge-machining (EDM) process to detect and prevent abnormal arcing, which can produce unacceptable "burn" marks on workpiece. When voltage between EDM electrode and workpiece behaves in manner indicative of abnormal arcing, relay made to switch off EDM power, which remains off until operator attends to EDM setup and resets monitor.

  15. Switchgear condition monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Budyn, M. [ABB Corporate Research, Krakow (Poland); Karandikar, H.M.; Urmson, M.G. [ABB Inc., Lake Mary, FL (United States)

    2010-07-01

    Electric utilities strive to keep switchgear in proper condition over their long life. Medium voltage switchgear are one of the key components in electrical power systems used to distribute electrical power, selectively isolate electrical loads and protect loads from cascading failure. They generally include a combination of electrical elements such as disconnectors, fuses, circuit breakers and distribution bus bars arranged in a lineup of frames. Since switchgear distributes electrical current, heat buildup becomes an important characteristic to monitor. The most significant amount of heat dissipation is on distribution elements like bus bars. Unexpected temperature rise at a particular location may indicate corrosion or a defect. If left uncorrected, this defect could result in catastrophic failure resulting in deactivated loads and potentially hazardous conditions to personnel. Currently, switchgear bus temperature monitoring is done periodically by manual inspections using IR cameras or by fibre-optic systems. Both methods have limitations, such as inaccurate and infrequent readouts, high implementation cost and limited monitoring area. This paper presented a modern approach for condition monitoring based on passive, SAW-based, wireless sensors, reducing installation costs and enhancing monitoring by allowing measurements in previously unreachable locations. A practical implementation of the wireless condition monitoring system was illustrated as a part of a general, built-in, switchgear diagnostics and maintenance system. The use of miniature SAW sensors proved effective in monitoring breaker connectors and non-invasive installation inside the switchgear. 8 refs., 5 figs.

  16. 数控机床伺服进给系统状态监测方案的分析比较%Comparing and Analysis of Schemes of Condition Monitoring for CNC Machine Tools Servo Feed System

    Institute of Scientific and Technical Information of China (English)

    韩军; 常瑞丽

    2014-01-01

    为了得到准确获取机床状态监测信号的方法,研究了机床状态信号获取的3种方案,即外置传感器信号、内置传感器信号和伺服驱动器监测端口信号。这3种信号都是可靠的信息来源,但是信号获取的难度和信号质量各有优劣。分析比较了这3种方案,分析结果对研究数控机床伺服进给系统在线监测技术、提高机床可靠性和加工质量具有参考意义。%In order to get methods of accurately obtaining machine tools condition monitoring signals,the three schemes of obtai-ning machine tools condition signals were researched,which included external setting sensor signals,internal setting sensor signals and servo drivers monitoring interface signals. The three signals were all reliable signal sources,however,the quality and difficulty level to obtain of signals were different. These three schemes were compared and analyzed. The analysis result has reference significance for re-searching the monitoring technology on-line of CNC machine tools servo feed system and improving reliability and machining quality of machine tools.

  17. An On-line Ferrograph for Monitoring Machine Wear

    Institute of Scientific and Technical Information of China (English)

    L(U) Xiao-jun; JING Min-qing; XIE You-bai

    2005-01-01

    In order to improve an on-line ferrograph, this paper simulates a three dimensional magnetic field distribution of an electromagnet, builds a sinking motion model of a wear particle, and investigates the motion law of wear particles under two different conditions. Both numeric results and experimental results show that the on-line ferrograph is capable of monitoring machine wear conditions by measuring the concentration and size distribution of wear particles in lubricating oil.

  18. Oil sensor system for online condition monitoring of technical equipment and machines; Oelsensorsystem zur Echtzeit-Zustandsueberwachung von technischen Anlagen und Maschinen

    Energy Technology Data Exchange (ETDEWEB)

    Gegner, Juergen [SKF GmbH, Schweinfurt (Germany); Kuipers, Ulrich [Fachhochschule Suedwestfalen, Hagen (Germany); Mauntz, Manfred [cmc Instruments GmbH, Eschborn (Germany)

    2010-07-01

    A novel oil sensor system is introduced for the continuous online measurement of the oil quality via the parameters electrical conductivity and relative permittivity for the evaluation of component wear and oil aging. The determination of contamination and decrease of lubricant quality permits on-demand maintenance. Since the conductivity of the oil is considerably lower compared to impurities, there is a direct correlation to the degree of contamination. Moisture content in oil or the decomposition of additives is quantified by an accurate measurement of the relative permittivity. Important applications are the online lubricant condition monitoring in (industrial) gearboxes, hydraulic systems, turbines, generators, and transformers. (orig.)

  19. Virtual Machine Monitor Indigenous Memory Reclamation Technique

    Directory of Open Access Journals (Sweden)

    Muhammad Shams Ul Haq

    2016-04-01

    Full Text Available Sandboxing is a mechanism to monitor and control the execution of malicious or untrusted program. Memory overhead incurred by sandbox solutions is one of bottleneck for sandboxing most of applications in a system. Memory reclamation techniques proposed for traditional full virtualization do not suit sandbox environment due to lack of full scale guest operating system in sandbox. In this paper, we propose memory reclamation technique for sandboxed applications. The proposed technique indigenously works in virtual machine monitor layer without installing any driver in VMX non root mode and without new communication channel with host kernel. Proposed Page reclamation algorithm is a simple modified form of Least recently used page reclamation and Working set page reclamation algorithms. For efficiently collecting working set of application, we use a hardware virtualization extension, page Modification logging introduced by Intel. We implemented proposed technique with one of open source sandboxes to show effectiveness of proposed memory reclamation method. Experimental results show that proposed technique successfully reclaim up to 11% memory from sandboxed applications with negligible CPU overheads

  20. 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......, neither machine parameters nor thermal impedance parameters are required in the scheme. Simulation results under various operating conditions confirm the proposed sensor-less online thermal monitoring approach....

  1. Condition Indicators for Gearbox Condition Monitoring Systems

    OpenAIRE

    P. Večeř; M. Kreidl; R. Šmíd

    2005-01-01

    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 ge...

  2. 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. 

  3. Bio-inspired computational techniques based on advanced condition monitoring

    Institute of Scientific and Technical Information of China (English)

    Su Liangcheng; He Shan; Li Xiaoli; Li Xinglin

    2011-01-01

    The application of bio-inspired computational techniques to the field of condition monitoring is addressed.First, the bio-inspired computational techniques are briefly addressed; the advantages and disadvantages of these computational methods are made clear. Then, the roles of condition monitoring in the predictive maintenance and failures prediction and the development trends of condition monitoring are discussed. Finally, a case study on the condition monitoring of grinding machine is described, which shows the application of bio-inspired computational technique to a practical condition monitoring system.

  4. STUDIES ON TOOL WEAR CONDITION MONITORING

    Directory of Open Access Journals (Sweden)

    Hüseyin Metin ERTUNÇ

    2001-01-01

    Full Text Available In this study, wear mechanisms on cutting tools, especially for the drill bits, during the cutting operation have been investigated. As the importance of full automation in industry has gained substantial importance, tool wear condition monitoring during the cutting operation has been the subject of many investigators. Tool condition monitoring is very crucial in order to change the tool before breakage. Because tool breakage can cause considerable economical damage to both the machine tool and workpiece. In this paper, the studies on the monitoring of drill bit wear in literature have been introduced; the direct/indirect techniques used and sensor fusion techniques have been summarized. The methods which were proposed to determine tool wear evolution as processing the sensor signals collected have been provided and their references have been given for detailed information.

  5. 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

  6. A distributed monitoring system for spinning-machine's spindle

    Science.gov (United States)

    Hong, Yang; Ping, Yang; Zhou, Jian Ping

    2005-12-01

    As a key unit with textile coil process technology, spinning-machine's spindles composes of a braking switch, a threephase current motor, rolling bearings and a rotary cup. Aiming at on line monitoring and fault diagnosis, a distributed monitoring system was proposed for real-time data collection and high-speed transmission. In this system, an IPC worked as an upper deck computer and many single chip processors served as bottom controllers that working status data collection and transmission can be conveniently conducted. With the features of bulk processing data and large quantities of controlled nodal points in a workshop condition, the distributed monitoring system was developed with adoption of particular approaches such as a distributed configuration with PCI bus for real time data collection and highspeed transmission, logic compression algorithm for data processing, etc. Therefore this system realizes reliable and high-speed bulk data collection, transmission and processing to meet needs of real-time monitor and control of spindle units.

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

    Science.gov (United States)

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

    2012-05-01

    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.

  8. Condition monitoring of multistage printing presses

    Science.gov (United States)

    Wang, W.; Golnaraghi, F.; Ismail, F.

    2004-03-01

    The main concern in printing quality in multistage presses is doubling. Doubling is caused by imperfections either within stages (units) or in links connecting different stages, mainly resulting from machine vibration, gear damage, and excessive run-out. In this paper, we propose new means for printing quality control via geared system health condition monitoring. The diagnosis is based on the signals acquired from inexpensive magnetic pickups. A new technique is developed to monitor the gear rotation synchronization among different stages in order to isolate possible sources of the doubling problem. A new approach is proposed to determine the gear run-out. Moreover, gear tooth damage detection is conducted using the beta kurtosis and the continuous wavelet transform based on the overall residual signal. The beta kurtosis of original signal average is also shown here to be useful in detecting excessive gear run-out. Test results from printing presses demonstrated the viability of the proposed methods.

  9. OTVE turbopump condition monitoring, task E.5

    Science.gov (United States)

    Coleman, Paul T.; Collins, J. J.

    1989-01-01

    Recent work has been carried out on development of isotope wear analysis and optical and eddy current technologies to provide bearing wear measurements and real time monitoring of shaft speed, shaft axial displacement and shaft orbit of the Orbit Transfer Vehicle hydrostatic bearing tester. Results show shaft axial displacement can be optically measured (at the same time as shaft orbital motion and speed) to within 0.3 mils by two fiberoptic deflectometers. Evaluation of eddy current probes showed that, in addition to measuring shaft orbital motion, they can be used to measure shaft speed without having to machine grooves on the shaft surface as is the usual practice for turbomachinery. The interim results of this condition monitoring effort are presented.

  10. Condition Monitoring of Control Loops

    OpenAIRE

    Horch, Alexander

    2000-01-01

    The main concern of this work is the development of methodsfor automatic condition monitoring of control loops withapplication to the process industry. By condition monitoringboth detection and diagnosis of malfunctioning control loops isunderstood, using normal operating data and a minimum amount ofprocess knowledge. The use of indices for quantifying loop performance is dealtwith in the first part of the thesis. The starting point is anindex proposed by Harris (1989). This index has been mo...

  11. 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.

  12. Research on Geographical Urban Conditions Monitoring

    Institute of Scientific and Technical Information of China (English)

    2012-01-01

    by LUO A1inghai Abstract Geographical national conditions monitoring has become an important task of surveying and geographical information industry, and will make a profound influence on the development of surveying and ge- ographical information. This paper introduced the basic concept of ge- ographical national conditions monitoring, and discussed its main tasks including complete surveying, dynamic monitoring, statistical analysis and regular release, and expounded the main content of geographical urban conditions monitoring including urbanization monitoring, social- economic development monitoring, transportation foundation monitor- ing and natural ecological environment monitoring, and put forwards the framework system of geographical urban conditions monitoring. Key words surveying and mapping ,geographical national conditions, monitoring ( Page:l )

  13. A Review on Approaches for Condition Based Maintenance in Applications with Induction Machines located Offshore

    Directory of Open Access Journals (Sweden)

    J. Cibulka

    2012-04-01

    Full Text Available This paper presents a review of different approaches for Condition Based Maintenance (CBM of induction machines and drive trains in offshore applications. The paper contains an overview of common failure modes, monitoring techniques, approaches for diagnostics, and an overview of typical maintenance actions. Although many papers have been written in this area before, this paper puts an emphasis on recent developments and limits the scope to induction machines and drive trains applied in applications located offshore.

  14. 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.

  15. 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...

  16. Wind Turbine Drivetrain Condition Monitoring - An Overview

    Energy Technology Data Exchange (ETDEWEB)

    Sheng, S; Veers, P.

    2011-10-01

    This paper provides an overview of wind turbine drivetrain condition monitoring based on presentations from a condition monitoring workshop organized by the National Renewable Energy Laboratory in 2009 and on additional references.

  17. Evolutionary Support Vector Machines for Transient Stability Monitoring

    Science.gov (United States)

    Dora Arul Selvi, B.; Kamaraj, N.

    2012-03-01

    Currently, power systems are in the need of fast and reliable contingency monitoring systems for the purpose of maintaining stability in the presence of deregulated and open market environment. In this paper, a quick and unfailing transient stability monitoring algorithm that considers both the symmetrical and unsymmetrical faults is presented. support vector machines (SVMs) are employed as pattern classifiers so as to construct fast relation mappings between the transient stability results and the selected input attributes using mutual information. The type of fault is recognized by a SVM classifier and the critical clearing time of the fault is estimated by a support vector regression machine. The SVM parameters are tuned by an elitist multi-objective non-dominated sorting genetic algorithm in such a manner that the best classification and regression performance are accomplished. To demonstrate the good potential of the scheme, IEEE 3 generator system and a South Indian Grid are utilized.

  18. Network Based Real Time Condition Monitoring of Rotating Machinery

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    This paper presents the development of a network based real time condition monitoring system of rotating machinery. The system is built up in a double net structure consisting of local net (including client and server) and intranet. The client serves as a field data collector and processor that samples the vibration signals and process parameters of a machine monitored in the net and processes the sampled data. The data collected by the client are transmitted to the server that processes the data further and provides the results of the diagnosis of each machine to any distant terminals through intranet or internet. Such a structure of the monitoring system is advantageous in safety, reliability and reasonably shares the existing net resources. In order to ensure real time transmission of the data, two procedures of data transmission, virtual channel and data pool, are developed and applied in the monitoring system. The experimental results show that the monitoring system works well and is suitable to monitor a large group of rotating machines.

  19. 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...

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. Lagoon Monitoring and Condition Parameters

    OpenAIRE

    Harrison, John; Smith, Dallen

    2004-01-01

    Lagoons combine storage and tr eatment functions and thus are more sensitive to management inputs than are solid or slurry facilities. The est ablishment and maintenance of desirable microbiological populations in lagoons requires more specific procedures in the way lagoons are loaded and monitored.

  5. Wormholes, time machines, and the weak energy condition

    OpenAIRE

    Morris, Michael S.; Thorne, Kip S.; Yurtsever, Ulvi

    1988-01-01

    It is argued that, if the laws of physics permit an advanced civilization to create and maintain a wormhole in space for interstellar travel, then that wormhole can be converted into a time machine with which causality might be violatable. Whether wormholes can be created and maintained entails deep, ill-understood issues about cosmic censorship, quantum gravity, and quantum field theory, including the question of whether field theory enforces an averaged version of the weak energy condition.

  6. Condition Monitoring of Power Cables

    OpenAIRE

    Lewin, P L; L. Hao; Swaffield, D J; Swingler, S.G.

    2007-01-01

    A National Grid funded research project at Southampton has investigated possible methodologies for data acquisition, transmission and processing that will facilitate on-line continuous monitoring of partial discharges in high voltage polymeric cable systems. A method that only uses passive components at the measuring points has been developed and is outlined in this paper. More recent work, funded through the EPSRC Supergen V, UK Energy Infrastructure (AMPerES) grant in collaboration with UK ...

  7. 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...

  8. A Web-based machining process monitoring system for E-manufacturing implementation

    Institute of Scientific and Technical Information of China (English)

    SHIN Bong-cheol; KIM Gun-hee; CHOI Jin-hwa; JEON Byung-cheol; LEE Honghee; CHO Myeong-woo; HAN Jin-yong; PARK Dong-sam

    2006-01-01

    Recently, with the rapid growth ofinformation technology, many studies have been performed to implement Web-based manufacturing system. Such technologies are expected to meet the need of many manufacturing industries who want to adopt E-manufacturing system for the construction of globalization, agility, and digitalization to cope with the rapid changing market requirements. In this research, a real-time Web-based machine tool and machining process monitoring system is developed as the first step for implementing E-manufacturing system. In this system, the current variations of the main spindle and feeding motors are measured using hall sensors. And the relationship between the cutting force and the spindle motor RMS (Root Mean Square) current at various spindle rotational speeds is obtained. Thermocouples are used to measure temperature variations of important heat sources of a machine tool. Also, a rule-based expert system is applied in order to decide the machining process and machine tool are in normal conditions. Finally, the effectiveness of the developed system is verified through a series of experiments.

  9. Multiphysicsbased Condition Monitoring of Composite Materials

    OpenAIRE

    Xue, Hui; Sharma, Puneet; Khawaja, Hassan Abbas

    2015-01-01

    Composites are increasingly being used in products such as: automobiles, bridges, boats, drillships, offshore platforms, aircrafts and satellites. The increased usage of these composite materials and the fact that the conditions pertaining to their failure are not fully understood makes it imperative to develop condition monitoring systems for composite structures. In this work, we present a theoretical framework for the development of a condition monitoring system. For this, we plan...

  10. Liquid intake monitoring through breathing signal using machine learning

    Science.gov (United States)

    Dong, Bo; Biswas, Subir

    2013-05-01

    This paper presents the design, system structure and performance for a wireless and wearable diet monitoring system. Food and drink intake can be detected by the way of detecting a person's swallow events. The system works based on the key observation that a person's otherwise continuous breathing process is interrupted by a short apnea when she or he swallows as a part of solid or liquid intake process. We detect the swallows through the difference between normal breathing cycle and breathing cycle with swallows using a wearable chest-belt. Three popular machine learning algorithms have been applied on both time and frequency domain features. Discrimination power of features is then analyzed for applications where only small number of features is allowed. It is shown that high detection performance can be achieved with only few features.

  11. Support vector machine for classification of walking conditions using miniature kinematic sensors.

    Science.gov (United States)

    Lau, Hong-Yin; Tong, Kai-Yu; Zhu, Hailong

    2008-06-01

    A portable gait analysis and activity-monitoring system for the evaluation of activities of daily life could facilitate clinical and research studies. This current study developed a small sensor unit comprising an accelerometer and a gyroscope in order to detect shank and foot segment motion and orientation during different walking conditions. The kinematic data obtained in the pre-swing phase were used to classify five walking conditions: stair ascent, stair descent, level ground, upslope and downslope. The kinematic data consisted of anterior-posterior acceleration and angular velocity measured from the shank and foot segments. A machine learning technique known as support vector machine (SVM) was applied to classify the walking conditions. SVM was also compared with other machine learning methods such as artificial neural network (ANN), radial basis function network (RBF) and Bayesian belief network (BBN). The SVM technique was shown to have a higher performance in classification than the other three methods. The results using SVM showed that stair ascent and stair descent could be distinguished from each other and from the other walking conditions with 100% accuracy by using a single sensor unit attached to the shank segment. For classification results in the five walking conditions, performance improved from 78% using the kinematic signals from the shank sensor unit to 84% by adding signals from the foot sensor unit. The SVM technique with the portable kinematic sensor unit could automatically recognize the walking condition for quantitative analysis of the activity pattern.

  12. 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.

  13. OTVE combustor wall condition monitoring

    Science.gov (United States)

    Szemenyei, Brian; Nelson, Robert S.; Barkhoudarian, S.

    1989-01-01

    Conventional ultrasonics, eddy current, and electromagnetic acoustic transduction (EMAT) technologies were evaluated to determine their capability of measuring wall thickness/wear of individual cooling channels in test specimens simulating conditions in the throat region of an OTVE combustion chamber liner. Quantitative results are presented for the eddy current technology, which was shown to measure up to the optimum 20-mil wall thickness with near single channel resolution. Additional results demonstrate the capability of the conventional ultrasonics and EMAT technologies to detect a thinning or cracked wall. Recommendations for additional eddy current and EMAT development tests are presented.

  14. Integrating structural health and condition monitoring

    DEFF Research Database (Denmark)

    May, Allan; Thöns, Sebastian; McMillan, David

    2015-01-01

    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...

  15. An advanced condition monitoring system for turbopumps

    Science.gov (United States)

    Cross, George S.; Barkhoudarian, Sarkis

    1991-01-01

    Advanced condition monitoring (ACM) technologies developed for in situ turbomachinery applications are reviewed. The ACM concepts are based on direct in situ hardware monitoring and between-flight inspections, using novel real-time, automated, noncontacting, and nonintrusive sensor and associated electronic technologies.

  16. Application of Cerebellar Model Articulation Controller(CMAC) with a Modified Algorithm in Monitoring Machine Performance Degradation

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jing-yong; ZHANG Lei; CAO Qi-xin; Jay Lee

    2005-01-01

    On the basis of CMAC-PDM (Pattern Discrimination Model), a novel algorithm of CMAC for monitoring machine degradation is proposed in this paper. The output of CMAC with the novel algorithm represents the state of a machine and PDM is not needed. The principle was explained by analyzing the modified mapping process of CMAC. The novel CMAC is applied to a tool condition monitoring system and two methodologies (novel CMAC and CMAC-PDM) are compared. The results prove that the novel algorithm is feasible and its computational complexity is reduced significantly.

  17. Methodology of Testing Shot Blasting Machines in Industrial Conditions

    Directory of Open Access Journals (Sweden)

    R. Wrona

    2012-04-01

    Full Text Available Shot blasting machines are widely used for automated surface treatment and finishing of castings. In shot blasting processes the stream of shots is generated and shaped by blasting turbines, making up a kinetic and dynamic system comprising a separating rotor, an adapting sleeve and a propelling rotor provided with blades. The shot blasting performance- i.e. the quality of shot treated surfaces depends on the actual design and operational parameters of the unit whilst the values of relevant parameters are associated with the geometry of turbine components and the level of its integration with the separator system. The circulation of the blasting medium becomes the integrating factor of the process line, starting from the hopper, through the propeller turbine, casting treatment, separation of contaminated abrasive mixture, to its recycling and reuse.Inferior quality of the abrasive agent (shot and insufficient purity of the abrasive mixture are responsible for low effectiveness of shot blasting. However, most practitioners fail to fully recognise the importance of proper diagnostics of the shot blasting process in industrial conditions. The wearing of major machine components and of the blasting agent and quality of shot treated surfaces are often misinterpreted, hence the need to take into account all factors involved in the process within the frame of a comprehensive methodology.This paper is an attempt to formulate and apply the available testing methods to the engineering practice in industrial conditions.

  18. Electrical machines monitoring using partial discharges; Monitorizacion de maquinas electricas mediante descargas parciales

    Energy Technology Data Exchange (ETDEWEB)

    Cano, J. C.; Rodriguez Ruiz, S.

    2006-07-01

    Electrical Machines Monitoring is a philosophy that is being more and more accepted in maintenance, the application of these techniques has a lot of advantages as the life evaluation non-intrusively and the detection and evolution evaluation of defects. this paper presents the monitoring of electrical machines using the Partial Discharges technique, which allow the evaluation of insulation of Electrical Machines. Real Cases are included in the paper as samples in which this techniques has been useful to detecting defects. (Author)

  19. Suitability of MEMS Accelerometers for Condition Monitoring: An experimental study.

    Science.gov (United States)

    Albarbar, Alhussein; Mekid, Samir; Starr, Andrew; Pietruszkiewicz, Robert

    2008-02-06

    With increasing demands for wireless sensing nodes for assets control and condition monitoring; needs for alternatives to expensive conventional accelerometers in vibration measurements have been arisen. Micro-Electro Mechanical Systems (MEMS) accelerometer is one of the available options. The performances of three of the MEMS accelerometers from different manufacturers are investigated in this paper and compared to a well calibrated commercial accelerometer used as a reference for MEMS sensors performance evaluation. Tests were performed on a real CNC machine in a typical industrial environmental workshop and the achieved results are presented.

  20. 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.

  1. Sequential monitoring with conditional randomization tests

    CERN Document Server

    Plamadeala, Victoria; 10.1214/11-AOS941

    2012-01-01

    Sequential monitoring in clinical trials is often employed to allow for early stopping and other interim decisions, while maintaining the type I error rate. However, sequential monitoring is typically described only in the context of a population model. We describe a computational method to implement sequential monitoring in a randomization-based context. In particular, we discuss a new technique for the computation of approximate conditional tests following restricted randomization procedures and then apply this technique to approximate the joint distribution of sequentially computed conditional randomization tests. We also describe the computation of a randomization-based analog of the information fraction. We apply these techniques to a restricted randomization procedure, Efron's [Biometrika 58 (1971) 403--417] biased coin design. These techniques require derivation of certain conditional probabilities and conditional covariances of the randomization procedure. We employ combinatoric techniques to derive t...

  2. A Rough Set-Based Effective State Identification Method of Multisensor Tool Condition Monitoring System

    Directory of Open Access Journals (Sweden)

    Nan Xie

    2014-06-01

    Full Text Available Multisensor improves the accuracy of machine tool condition monitoring system, which provides the critical feedback information to the manufacture process controller. Multisensor monitoring system needs to collect abundant data to employ attribute extraction, election, reduction, and classification to form the decision knowledge. A machine tool condition monitoring system has been built and the method of tool condition decision knowledge discovery is also presented. Multiple sensors include vibration, force, acoustic emission, and main spindle current. The novel approach engages rough theory as a knowledge extraction tool to work on the data that are obtained from both multisensor and machining parameters and then extracts a set of minimal state identification rules encoding the preference pattern of decision making by domain experts. By means of the knowledge acquired, the tool conditions are identified. A case study is presented to illustrate that the approach produces effective and minimal rules and provides satisfactory accuracy.

  3. 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.

  4. Condition Monitoring under In-situ Lubrication Status of Bearing Using Infrared Thermography

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Dong Yeon; Hong, Dong Pyo; Yu, Chung Hwan [Chonbuk National University, Jeonju (Korea, Republic of); Kim, Won Tae [Kongju National University, Kongju (Korea, Republic of)

    2010-04-15

    The infrared thermography technology rather than traditional nondestructive methods has benefits with non-contact and non-destructive testings in measuring for the fault diagnosis of the rotating machine. In this work, condition monitoring measurements using this advantage of thermography were proposed. From this study, the novel approach for the damage detection of a rotating machine was conducted based on the spectrum analysis. As results, by adopting the ball bearing used in the rotating machine applied extensively, an spectrum analysis with thermal imaging experiment was performed. Also, as analysing the temperature characteristics obtained from the infrared thermography for in-situ rotating ball bearing under the lubrication condition, it was concluded that infrared thermography for condition monitoring in the rotating machine at real time could be utilized in many industrial fields

  5. Crosslinking and condition monitoring with wind power plants; Vernetzung und Condition Monitoring bei Windenergieanlagen

    Energy Technology Data Exchange (ETDEWEB)

    Spelter, Frank [Bachmann Electronic GmbH, Feldkirch (Austria). Unternehmenskommunikation

    2010-10-15

    Condition monitoring of wind power systems is getting increasingly important, and there are various possible approaches. The Bachmann M1 automation system allows the implementation of measuring and control processes and evaluations up to comprehensive condition monitoring. In combination with an expert system, it is possible to monitor mechanical and technical components and to detect defects before these will have negative effects on the system condition. (orig.)

  6. Actualities and Development of Heavy-Duty CNC Machine Tool Thermal Error Monitoring Technology

    Science.gov (United States)

    Zhou, Zu-De; Gui, Lin; Tan, Yue-Gang; Liu, Ming-Yao; Liu, Yi; Li, Rui-Ya

    2017-09-01

    Thermal error monitoring technology is the key technological support to solve the thermal error problem of heavy-duty CNC (computer numerical control) machine tools. Currently, there are many review literatures introducing the thermal error research of CNC machine tools, but those mainly focus on the thermal issues in small and medium-sized CNC machine tools and seldom introduce thermal error monitoring technologies. This paper gives an overview of the research on the thermal error of CNC machine tools and emphasizes the study of thermal error of the heavy-duty CNC machine tool in three areas. These areas are the causes of thermal error of heavy-duty CNC machine tool and the issues with the temperature monitoring technology and thermal deformation monitoring technology. A new optical measurement technology called the "fiber Bragg grating (FBG) distributed sensing technology" for heavy-duty CNC machine tools is introduced in detail. This technology forms an intelligent sensing and monitoring system for heavy-duty CNC machine tools. This paper fills in the blank of this kind of review articles to guide the development of this industry field and opens up new areas of research on the heavy-duty CNC machine tool thermal error.

  7. Integrated condition monitoring of space information network

    Science.gov (United States)

    Wang, Zhilin; Li, Xinming; Li, Yachen; Yu, Shaolin

    2015-11-01

    In order to solve the integrated condition monitoring problem in space information network, there are three works finished including analyzing the characteristics of tasks process and system health monitoring, adopting the automata modeling method, and respectively establishing the models for state inference and state determination. The state inference model is a logic automaton and is gotten by concluding engineering experiences. The state determination model is a double-layer automaton, the lower automaton is responsible for parameter judge and the upper automaton is responsible for state diagnosis. At last, the system state monitoring algorithm has been proposed, which realizes the integrated condition monitoring for task process and system health, and can avoid the false alarm.

  8. Real-time machine vision FPGA implementation for microfluidic monitoring on Lab-on-Chips.

    Science.gov (United States)

    Sotiropoulou, Calliope-Louisa; Voudouris, Liberis; Gentsos, Christos; Demiris, Athanasios M; Vassiliadis, Nikolaos; Nikolaidis, Spyridon

    2014-04-01

    A machine vision implementation on a field-programmable gate array (FPGA) device for real-time microfluidic monitoring on Lab-On-Chips is presented in this paper. The machine vision system is designed to follow continuous or plug flows, for which the menisci of the fluids are always visible. The system discriminates between the front or "head" of the flow and the back or "tail" and is able to follow flows with a maximum speed of 20 mm/sec in circular channels of a diameter of 200 μm (corresponding to approx. 60 μl/sec ). It is designed to be part of a complete Point-of-Care system, which will be portable and operate in non-ideal laboratory conditions. Thus, it is able to cope with noise due to lighting conditions and small LoC displacements during the experiment execution. The machine vision system can be used for a variety of LoC devices, without the need for fiducial markers (such as redundancy patterns) for its operation. The underlying application requirements called for a complete hardware implementation. The architecture uses a variety of techniques to improve performance and minimize memory access requirements. The system input is 8 bit grayscale uncompressed video of up to 1 Mpixel resolution. The system uses an operating frequency of 170 Mhz and achieves a computational time of 13.97 ms (worst case), which leads to a throughput of 71.6 fps for 1 Mpixel video resolution.

  9. Survey of Condition Indicators for Condition Monitoring Systems (Open Access)

    Science.gov (United States)

    2014-09-29

    Renewable Energy Laboratory (NREL) published a document named ‘Wind Turbine Gearbox Condition Monitoring Round Robin Study – Vibration Analysis’ in 2012... Mean Square (RMS) RMS describes the energy content of the signal. RMS is used to evaluate the overall condition of the components. Therefore, it...13) ̅ is the mean value of signal N is the number of data point in the dataset x Energy

  10. 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.

  11. Advanced condition monitoring at E.ON

    Energy Technology Data Exchange (ETDEWEB)

    Burridge-Oakland, Ty [E.ON, Nottingham (United Kingdom)

    2012-07-01

    Advanced Condition Monitoring (ACM) is a term for a new style of condition monitoring at E.ON. E.ON has developed a software tool known as SpheriCAL to perform ACM. SpheriCAL monitors plant health on a near real time basis via the signals stored in OSIsoft PI Servers. It produces alarms based on significant changes in condition, using the relationship between multiple plant signals of any type as a reference. Monitoring in this way can give early warning of developing problems investigating further investigation and maintenance planning. After a two year trial period, a substantial value case has been created and accepted for the use of ACM at E.ON. It has been found that the use of ACM in combination with a complimentary maintenance strategy can generate an Internal Rate of Return (IRR) of 57%. This value is based on reduction of plant trips and a reduction in unplanned unavailability by 20%, through planned instead of reactive maintenance. E.ON is currently in the process of implementing ACM at all of the Large Frame Gas Turbine sites across the European fleet of power stations. (orig.)

  12. Electrical condition monitoring method for polymers

    Energy Technology Data Exchange (ETDEWEB)

    Watkins, Jr. Kenneth S. (Dahlonega, GA); Morris, Shelby J. (Hampton, VA); Masakowski, Daniel D. (Worcester, MA); Wong, Ching Ping (Duluth, GA); Luo, Shijian (Boise, ID)

    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. Characteristics of machined surface controlled by cutting tools and conditions in machining of brittle material

    Institute of Scientific and Technical Information of China (English)

    Yong-Woo KIM; Soo-Chang CHOI; Jeung-Woo PARK; Deug-Woo LEE

    2009-01-01

    One of the ultra-precision machining methods was adapted for brittle material as well as soft material by using multi-arrayed diamond tips and high speed spindle. Conventional machining method is too hard to control surface roughness and surface texture against brittle material because the particles of grinding tools are irregular size and material can be fragile. Therefore, we were able to design tool paths and machine controlled pattern on surface by multi-arrayed diamond tips with uniform size made in MEMS fabrication and high speed spindle, and the maximum speed was about 3×105 r/min. We defined several parameters that can affect the machining surface. Those were multi-array of diamond tips (n×n), speed of air spindle and feeding rate. The surface roughness and surface texture can be controlled by those parameters for micro machining.

  14. Reusable rocket engine turbopump condition monitoring

    Science.gov (United States)

    Hampson, M. E.; Barkhoudarian, S.

    1985-01-01

    Significant improvements in engine readiness with attendant reductions in maintenance costs and turnaround times can be achieved with an engine condition monitoring system (CMS). The CMS provides real time health status of critical engine components, without disassembly, through component monitoring with advanced sensor technologies. Three technologies were selected to monitor the rotor bearings and turbine blades: the isotope wear detector and fiber optic deflectometer (bearings), and the fiber optic pyrometer (blades). Signal processing algorithms were evaluated and ranked for their utility in providing useful component health data to unskilled maintenance personnel. Design modifications to current configuration Space Shuttle Main Engine (SSME) high pressure turbopumps and the MK48-F turbopump were developed to incorporate the sensors.

  15. A Review of Sensor System and Application in Milling Process for Tool Condition Monitoring

    Directory of Open Access Journals (Sweden)

    Muhammad Rizal

    2014-02-01

    Full Text Available This study presents a review of the state-of-the-art in sensor technologies and its application in milling process to measure machining signal for Tool Condition Monitoring (TCM systems. Machining signals such as cutting force, torque, vibration, acoustic emission, current/power, sound and temperature from milling operation are briefly reviewed with the goal of indentifying the parameters for TCM. Sensors reviewed include both commercial and research devices that can measure machining signals. In this study describes trends in the sensor systems used and its potential for future research.

  16. Prediction of Surface Roughness Based on Machining Condition and Tool Condition in Boring EN31 Steel

    Directory of Open Access Journals (Sweden)

    P. Mohanaraman

    2016-04-01

    Full Text Available Prediction of Surface roughness plays a vital role in manufacturing process. In manufacturing industries, productions of metallic materials require high surface finish in various components. In the present work, the effect of spindle speed, feed rate, depth of cut and flank wear of the tool on the surface roughness has been studied. Carbide tipped insert was used for boring operation. Experiments were conducted in CNC lathe. The experimental setup was prepared with sixteen levels of cutting parameters and was conducted with two tool tip conditions in dry machining. A piezoelectric accelerometer was used to measure the vibrational signals while machining. The data acquisition card which connected between accelerometer and lab-view software to record the signals. Simple linear and least median regression models were used for prediction of surface roughness. The models were developed by weka analysis software. The best suitable regression model is implemented based on maximum correlation coefficient and the minimum error values.

  17. A Resilient Condition Assessment Monitoring System

    Energy Technology Data Exchange (ETDEWEB)

    Humberto Garcia; Wen-Chiao Lin; Semyon M. Meerkov

    2012-08-01

    An architecture and supporting methods are presented for the implementation of a resilient condition assessment monitoring system that can adaptively accommodate both cyber and physical anomalies to a monitored system under observation. In particular, the architecture includes three layers: information, assessment, and sensor selection. The information layer estimates probability distributions of process variables based on sensor measurements and assessments of the quality of sensor data. Based on these estimates, the assessment layer then employs probabilistic reasoning methods to assess the plant health. The sensor selection layer selects sensors so that assessments of the plant condition can be made within desired time periods. Resilient features of the developed system are then illustrated by simulations of a simplified power plant model, where a large portion of the sensors are under attack.

  18. 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...

  19. 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

    2008-08-19

    An electrical condition monitoring method utilizes measurement of electrical resistivity of an age sensor made of a conductive matrix or composite disposed in a polymeric structure such as an electrical cable. The conductive matrix comprises a base polymer and conductive filler. The method includes communicating the resistivity to a measuring instrument and correlating resistivity of the conductive matrix of the polymeric structure with resistivity of an accelerated-aged conductive composite.

  20. Reusable rocket engine optical condition monitoring

    Science.gov (United States)

    Wyett, L.; Maram, J.; Barkhoudarian, S.; Reinert, J.

    1987-01-01

    Plume emission spectrometry and optical leak detection are described as two new applications of optical techniques to reusable rocket engine condition monitoring. Plume spectrometry has been used with laboratory flames and reusable rocket engines to characterize both the nominal combustion spectra and anomalous spectra of contaminants burning in these plumes. Holographic interferometry has been used to identify leaks and quantify leak rates from reusable rocket engine joints and welds.

  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. A low cost implementation of multi-parameter patient monitor using intersection kernel support vector machine classifier

    Science.gov (United States)

    Mohan, Dhanya; Kumar, C. Santhosh

    2016-03-01

    Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heart rate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board.

  3. Condition monitoring of rotary blood pumps.

    Science.gov (United States)

    Jammu, V B; Malanoski, S; Walter, T; Smith, W

    1997-01-01

    Long-term, trouble-free operation of ventricular assist devices (VADs) is critical to the patient. A catastrophic failure of the VAD could cost the patient's life, thus defeating the purpose of the device. The targeted 90% 5 year reliability also implies that the average device life would exceed the 5 year limit. Time based explantation of the device after the fifth year will replace many devices with significant additional life, subject the patient to unnecessary surgical risk, and increase costs. To preclude the need for time based replacements and prevent catastrophic failures, a condition monitor is proposed in this article for early detection of faults in VADs. To develop this monitor, the effectiveness of various sensing and monitoring methods for determining the VAD condition is investigated. A Hemadyne pump was instrumented with a set of eight sensors, and a series of experiments were performed to record and analyze signals from the normal and abnormal pumps with five different faults. Statistical, spectral, envelope, and ensemble averaging analyses were performed to characterize changes in sensor signals due to faults. Experimental results indicate that statistical and frequency information from the acceleration and dynamic pressure signals can clearly detect and identify various VAD faults.

  4. Parallel Dynamic Learnable Immune Evolutionary Algorithm for Permanent Magnet Synchronous Machine Parameter Condition Monitoring%基于并行动态学习型免疫算法的永磁同步电机状态监测

    Institute of Scientific and Technical Information of China (English)

    刘朝华; 李小花; 张红强; 周少武

    2015-01-01

    为提高永磁同步电机(Permanent magnet synchronous machine,PMSM)系统参数辨识与状态监测效率,利用图形处理器(Graphics processing unit,GPU)并行计算与人工免疫技术相结合的研究方法,建立面向永磁同步电机系统基于GPU并行动态学习型免疫进化的参数估计与状态监测模型.为提高算法的动态跟踪性能,在抗体演化进程中,通过知识学习策略来引导算法进化过程,首先将抗体群划分为B细胞群、浆细胞群以及记忆细胞群,对处于不同进化群体中的抗体分别设计免疫综合学习策略、免疫反向学习策略和高斯学习策略,以增强抗体间的信息交互;接着,应用图形处理器并行计算技术进一步加速算法求解过程;最后,将所提算法应用于永磁同步电机系统参数辨识与状态监测中,实验表明,所提方法能同时准确地对电机的定子电阻、dq轴电感和永磁磁链等系统关键参数进行估计.依据参数变化实现对系统运行状态进行在线监测与预警.计算结果表明,GPU并行技术能大幅度提高计算效率.

  5. On-Line Condition Monitoring using Computational Intelligence

    CERN Document Server

    Vilakazi, C B; Mautla, P; Moloto, E

    2007-01-01

    This paper presents bushing condition monitoring frameworks that use multi-layer perceptrons (MLP), radial basis functions (RBF) and support vector machines (SVM) classifiers. The first level of the framework determines if the bushing is faulty or not while the second level determines the type of fault. The diagnostic gases in the bushings are analyzed using the dissolve gas analysis. MLP gives superior performance in terms of accuracy and training time than SVM and RBF. In addition, an on-line bushing condition monitoring approach, which is able to adapt to newly acquired data are introduced. This approach is able to accommodate new classes that are introduced by incoming data and is implemented using an incremental learning algorithm that uses MLP. The testing results improved from 67.5% to 95.8% as new data were introduced and the testing results improved from 60% to 95.3% as new conditions were introduced. On average the confidence value of the framework on its decision was 0.92.

  6. Condition Monitoring of Large-Scale Facilities

    Science.gov (United States)

    Hall, David L.

    1999-01-01

    This document provides a summary of the research conducted for the NASA Ames Research Center under grant NAG2-1182 (Condition-Based Monitoring of Large-Scale Facilities). The information includes copies of view graphs presented at NASA Ames in the final Workshop (held during December of 1998), as well as a copy of a technical report provided to the COTR (Dr. Anne Patterson-Hine) subsequent to the workshop. The material describes the experimental design, collection of data, and analysis results associated with monitoring the health of large-scale facilities. In addition to this material, a copy of the Pennsylvania State University Applied Research Laboratory data fusion visual programming tool kit was also provided to NASA Ames researchers.

  7. Development of non-contact structural health monitoring system for machine tools

    Directory of Open Access Journals (Sweden)

    Deepam Goyal

    2016-08-01

    Full Text Available In this era of flexible manufacturing systems, a real-time structural health monitoring (SHM is paramount for machining processes which are of great relevance today, when there is a constant call for better productivity with high quality at low price. During machining, vibrations are always brought forth because of mechanical disturbances from various sources such as an engine, a sound, and noise, among others. A SHM system provides significant economic benefits when applied to machine tools and machining processes. This study demonstrates a non contact SHM system for machine tools based on the vibration signal collected through a low-cost, microcontroller based data acquisition system. The examination tests of this developed system have been carried out on a vibration rig. The readings have also been calibrated with the accelerometer to validate the proposed system. The developed system results in quick measurement, enables reliable monitoring, and is cost effective with no need to alter the structure of the machine tool. It is expected that the system can forewarn the operator for timely based maintenance actions in addition to reducing the costs of machine downtime and acquiring equipments with reduction in complexity of machine tools.

  8. Condition monitoring of gearboxes using synchronously averaged electric motor signals

    Science.gov (United States)

    Ottewill, J. R.; Orkisz, M.

    2013-07-01

    Due to their prevalence in rotating machinery, the condition monitoring of gearboxes is extremely important in the minimization of potentially dangerous and expensive failures. Traditionally, gearbox condition monitoring has been conducted using measurements obtained from casing-mounted vibration transducers such as accelerometers. A well-established technique for analyzing such signals is the synchronous signal average, where vibration signals are synchronized to a measured angular position and then averaged from rotation to rotation. Driven, in part, by improvements in control methodologies based upon methods of estimating rotor speed and torque, induction machines are used increasingly in industry to drive rotating machinery. As a result, attempts have been made to diagnose defects using measured terminal currents and voltages. In this paper, the application of the synchronous signal averaging methodology to electric drive signals, by synchronizing stator current signals with a shaft position estimated from current and voltage measurements is proposed. Initially, a test-rig is introduced based on an induction motor driving a two-stage reduction gearbox which is loaded by a DC motor. It is shown that a defect seeded into the gearbox may be located using signals acquired from casing-mounted accelerometers and shaft mounted encoders. Using simple models of an induction motor and a gearbox, it is shown that it should be possible to observe gearbox defects in the measured stator current signal. A robust method of extracting the average speed of a machine from the current frequency spectrum, based on the location of sidebands of the power supply frequency due to rotor eccentricity, is presented. The synchronous signal averaging method is applied to the resulting estimations of rotor position and torsional vibration. Experimental results show that the method is extremely adept at locating gear tooth defects. Further results, considering different loads and different

  9. 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.

  10. 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...

  11. 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.

  12. Monitoring of cigarette smoking using wearable sensors and support vector machines.

    Science.gov (United States)

    Lopez-Meyer, Paulo; Tiffany, Stephen; Patil, Yogendra; Sazonov, Edward

    2013-07-01

    Cigarette smoking is a serious risk factor for cancer, cardiovascular, and pulmonary diseases. Current methods of monitoring of cigarette smoking habits rely on various forms of self-report that are prone to errors and under reporting. This paper presents a first step in the development of a methodology for accurate and objective assessment of smoking using noninvasive wearable sensors (Personal Automatic Cigarette Tracker-PACT) by demonstrating feasibility of automatic recognition of smoke inhalations from signals arising from continuous monitoring of breathing and hand-to-mouth gestures by support vector machine classifiers. The performance of subject-dependent (individually calibrated) models was compared to performance of subject-independent (group) classification models. The models were trained and validated on a dataset collected from 20 subjects performing 12 different activities representative of everyday living (total duration 19.5 h or 21,411 breath cycles). Precision and recall were used as the accuracy metrics. Group models obtained 87% and 80% of average precision and recall, respectively. Individual models resulted in 90% of average precision and recall, indicating a significant presence of individual traits in signal patterns. These results suggest the feasibility of monitoring cigarette smoking by means of a wearable and noninvasive sensor system in free living conditions.

  13. Fully automatic spray-LBL machine with monitoring the real time growth of multilayer films using Quartz Crystal Microbalance

    Directory of Open Access Journals (Sweden)

    Shiratori S.

    2013-08-01

    Full Text Available A fully automatic spray-LBL machine with monitoring the real time growth of multilayer films using Quartz Crystal Microbalance (QCM techniques was newly developed. We established fully automatic spray layer-by-layer method by precisely controlling air pressure, solution flow, and spray pattern. The movement pattern towards the substrate during solution spraying allowed fabrication of a nano-scale, flat, thin film over a wide area. Optimization of spray conditions permitted fabrication of the flat film with high and low refractive indexes, and they were piled up alternatively to constitute a one-dimensional photonic crystal with near-infrared reflection characteristics. The heat shield effect of the near-infrared reflective film was also confirmed under natural sunlight. It was demonstrated that the fabrication using the automatic spray-LBL machine and real-time QCM monitoring allows the fabrication of optical quality thin films with precise thickness.

  14. Electrical discharge machining: occupational hygienic characterization using emission-based monitoring.

    Science.gov (United States)

    Evertz, Sven; Dott, Wolfgang; Eisentraeger, Adolf

    2006-09-01

    Hazardous potential in industrial environments is normally assessed by means of immission-based sampling and analyses. This approach is not adequate, if effects of specific technical adjustments at machine tools or working processes on hygienic parameters should be assessed. This work has focused on the optimization of a manufacturing process (electrical discharge machining, EDM), with regard to risk reduction assessment. It is based on emission analyses rather than immision analyses. Several technical EDM parameters have been examined for their influence on air-based emissions. Worktools and workpieces used have a strong influence on aliphatic compounds and metals but not on volatile organic compounds (benzene, toluene, ethylene-benzene and xylene (BTEX)) and polycyclic aromatic hydrocarbons (PAHs) in air emissions. Increasing the dielectric (mineral oil) level above processing location decreases BTEX, chromium, nickel and PAH emissions. Aliphatic compounds, in contrast, increase in air emissions. EDM current used has a positive relationship with all substances analyzed in air emissions. Indicative immission concentrations, as can be expected under EDM conditions, are estimated in a predictive scenario. The results of this characterization give rise to an important conclusion in that risk assessment so far has been using incorrect parameters: total aliphatic compounds. Maximum level of chromium is reached long before limit values of aliphatic compounds are exceeded. Because of the fact that metals, like chromium, also have a higher hazardous potential, metal analysis should be introduced in future risk assessment. This experimental approach, that captures total emission of the electrical discharge machine, and is not solely based on immission values, has lead to a better understanding of the production process. This information is used to extract recommendations regarding monitoring aspects and protection measures.

  15. Condition monitoring of distributed systems using two-stage Bayesian inference data fusion

    Science.gov (United States)

    Jaramillo, Víctor H.; Ottewill, James R.; Dudek, Rafał; Lepiarczyk, Dariusz; Pawlik, Paweł

    2017-03-01

    In industrial practice, condition monitoring is typically applied to critical machinery. A particular piece of machinery may have its own condition monitoring system that allows the health condition of said piece of equipment to be assessed independently of any connected assets. However, industrial machines are typically complex sets of components that continuously interact with one another. In some cases, dynamics resulting from the inception and development of a fault can propagate between individual components. For example, a fault in one component may lead to an increased vibration level in both the faulty component, as well as in connected healthy components. In such cases, a condition monitoring system focusing on a specific element in a connected set of components may either incorrectly indicate a fault, or conversely, a fault might be missed or masked due to the interaction of a piece of equipment with neighboring machines. In such cases, a more holistic condition monitoring approach that can not only account for such interactions, but utilize them to provide a more complete and definitive diagnostic picture of the health of the machinery is highly desirable. In this paper, a Two-Stage Bayesian Inference approach allowing data from separate condition monitoring systems to be combined is presented. Data from distributed condition monitoring systems are combined in two stages, the first data fusion occurring at a local, or component, level, and the second fusion combining data at a global level. Data obtained from an experimental rig consisting of an electric motor, two gearboxes, and a load, operating under a range of different fault conditions is used to illustrate the efficacy of the method at pinpointing the root cause of a problem. The obtained results suggest that the approach is adept at refining the diagnostic information obtained from each of the different machine components monitored, therefore improving the reliability of the health assessment of

  16. Web-Enabled Remote Machine Monitoring and Prognostics

    Institute of Scientific and Technical Information of China (English)

    Jay Lee; Jun Ni

    2004-01-01

    @@ 1 INTRODUCTION Today's machine tool industries are facing unprecedented challenges brought about by development of outsourcing and low cost manufac-turing in Asia. Manufacturing outsourcing provided many opportu-nities but also added challenges to produce and deliver products with improved productivity, quality, service and costs. Lead times must be cut short to their extreme extend to meet need the changing demands of customers in different regions of the world. Products are required to be make-to-order, which requires a tight control and near-zero downtime of the plant floor, equipment and devices.

  17. 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.

  18. A wavelet bicoherence-based quadratic nonlinearity feature for translational axis condition monitoring.

    Science.gov (United States)

    Li, Yong; Wang, Xiufeng; Lin, Jing; Shi, Shengyu

    2014-01-27

    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.

  19. 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

  20. Process monitoring evaluation and implementation for the wood abrasive machining process.

    Science.gov (United States)

    Saloni, Daniel E; Lemaster, Richard L; Jackson, Steven D

    2010-01-01

    Wood processing industries have continuously developed and improved technologies and processes to transform wood to obtain better final product quality and thus increase profits. Abrasive machining is one of the most important of these processes and therefore merits special attention and study. The objective of this work was to evaluate and demonstrate a process monitoring system for use in the abrasive machining of wood and wood based products. The system developed increases the life of the belt by detecting (using process monitoring sensors) and removing (by cleaning) the abrasive loading during the machining process. This study focused on abrasive belt machining processes and included substantial background work, which provided a solid base for understanding the behavior of the abrasive, and the different ways that the abrasive machining process can be monitored. In addition, the background research showed that abrasive belts can effectively be cleaned by the appropriate cleaning technique. The process monitoring system developed included acoustic emission sensors which tended to be sensitive to belt wear, as well as platen vibration, but not loading, and optical sensors which were sensitive to abrasive loading.

  1. The ATLAS Beam Condition Monitor Commissioning

    CERN Document Server

    Gorisek, A

    2008-01-01

    The ATLAS Beam Condition Monitor (BCM) based on radiation hard pCVD diamond sensors and event-by-event measurements of environment close to interaction point (z=±184 cm, r=5.5 cm) has been installed in the Pixel detector since early 2008 and together with the Pixel detector in the ATLAS cavern since June 2008. The sensors and front end electronics were shown to withstand 50 Mrad and 1015 particles/cm2 expected in LHC lifetime. Recently the full readout chain, partly made of radiation tolerant electronics, still inside of the ATLAS spectrometer and partly in the electronics room, was completed and the system was operated in time of the first LHC single beams and is ready now for the first collisions which will follow after the LHC repair.

  2. Functioning condition monitoring of industrial equipment

    Science.gov (United States)

    Ungureanu, N. S.; Petrovan, A.; Ungureanu, M.; Alexandrescu, M.

    2017-02-01

    The paper analyses the theoretical aspects related to monitoring industrial equipment. Are treated issues that concern the choosing of industrial equipment to be monitored, the parameters to be monitored, monitoring mode (local or remote) and the mode of collection and transmission of data.

  3. LabVIEW based Condition Monitoring of Induction Machines

    Directory of Open Access Journals (Sweden)

    K.Vinoth Kumar

    2012-04-01

    Full Text Available This paper focuses on experimental results to prove that motor current signature analysis (MCSA can diagnose shorted turns in low voltage stator windings of 3-phase induction motors using LabVIEW. The diagnostic strategy is presented and variables that influence the diagnosis are discussed. Current spectra from motors with short-circuited turns (with and without short circuit current limiting resistors are presented and fully analyzed. Results from motors tested to failure are reported. The results in this paper were from industrial motors of different pole numbers with concentric and lap wound winding designs. Since stator failures account for a high percentage of failures the results are particularly relevant to industry.

  4. 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.

  5. ONBOARD MONITORING OF ENGINE OIL RESOURCE WORKING-OUT RATE IN WHEELED AND CATERPILLAR MACHINES

    Directory of Open Access Journals (Sweden)

    Yu. D. Karpievich

    2014-01-01

    Full Text Available An engine oil is capable reliably and longtime to perform specified functions only in the case when its properties correspond to those thermal, mechanical and chemical impacts to which the oil is subjected in the engine. Compatibility of the engine design, its uprate and oil properties is one of the main conditions for provision of high operational reliability. Type and properties of fuel, quality of an engine oil, engine type, its design, its health, its operational regime and conditions and a number of other factors influence on intensity of oil contamination in the operated engine. Oil quality is deteriorated due to accumulation of incomplete combustion products in it and this process is associated with the engine's health. This leads to reduction of viscosity, deterioration of lubrication ability, troubles in fluid friction mode. Combustion products have rather high amount of aggressive corrosive oxides.Service-life of engine oil prior to its change is determined not only by automobile mileage or tractor operating time but also by the period of time within which this work has been carried out. Corrosion processes are speeding up, protective processes are worsening, oil ageing is accelerating when vehicles have short daily and small mileages. So it is necessary to change oil at least annually.A new method for onboard monitoring of engine oil resource working-out rate in wheeled and caterpillar machines has been developed in the paper. Usage of fuel expended volume by engine while determining engine oil resource working-out rate makes it possible timely to assess a residual resource of the engine oil and also predict the date of its change at any operational period of wheeled and caterpillar machines.

  6. Monitoring Polaris and Seeing Conditions at PARI

    Science.gov (United States)

    Crawford, April

    2016-01-01

    Pisgah Astronomical Research Institute (PARI) was originally built by NASA to track and collect data from satellites. The location in the Pisgah National Forest was chosen due to the excellent ability of the surrounding mountains to block radio interference and light pollution. The PARI observatory has been monitoring Polaris for over 10 years and has amassed a large collection of images of the star and those surrounding it. While several telescopes have been used throughout the project, we are currently using a Omni XLT Series Celestron and an SBIG ST-8300M CCD camera with a 0.70 arcsecond/pixel ratio. The software is run on Windows, however, we will be making a switch to Linux and implementing a new program to control the camera. The new images, once converted to a usable format (ST10 to FITS), can be automatically fed into an in-house Java program to track the variability of the star and simultaneously determine the seeing conditions experienced on the campus. Since we have several years worth of data, the program will also be used to provide a history of variability and seeing conditions. We ultimately hope to be able to track the possible changes in variability of Polaris, as it's current location on the HR diagram is being studied. The data could also prove valuable for our on-site scientists and many visiting students to study on campus. We are also developing a relative scale for our seeing conditions, accompanied by FWHM measurements in arcseconds that will can be compared to those of surrounding observatories in mountainous areas.

  7. IDENTIFICATION AND MONITORING OF NOISE SOURCES OF CNC MACHINE TOOLS BY ACOUSTIC HOLOGRAPHY METHODS

    Directory of Open Access Journals (Sweden)

    Jerzy Józwik

    2016-06-01

    Full Text Available The paper presents the analysis of sound field emitted by selected CNC machine tools. The identification of noise sources and level was measured by acoustic holography for the 3-axis DMC 635eco machine tool and the 5-axis vertical machining centre DMU 65 monoBlock. The acoustic holography method allows precise identification and measurement of noise sources at different bandwidths of frequency. Detection of noise sources in tested objects allows diagnosis of their technical condition, as well as choice of effective means of noise reduction, which is highly significant from the perspective of minimising noise at the CNC machine operator workstation. Test results were presented as acoustic maps in various frequency ranges. Noise sources of the machine tool itself were identified, as well as the range of noise influence and the most frequent places of reflections and their span. The results of measurements were presented in figures and diagrams.

  8. Electro-pump Fault Diagnosis of Marine Ship by Vibration Condition Monitoring

    Directory of Open Access Journals (Sweden)

    Payman Salami

    2010-05-01

    Full Text Available The objective of this research is to investigate the correlation between vibration analysis and fault diagnosis. This was achieved by vibration analysis of an electro-pump of marine ship. The vibration analysis was initially run under regular interval during electro-pump life. Some series of tests were then conducted under the operating hours of stone crasher. Vibration data was regularly collected. The overall vibration data produced by vibration analysis was compared with previous data, in order to quantify the effectiveness of the results of vibration condition monitoring technique. Numerical data produced by vibration analysis were compared with vibration spectra in standard condition of healthy machine, in order to quantify the effectiveness of the vibration condition monitoring technique. The results of this paper have given more understanding on the dependent roles of vibration analysis in predicting and diagnosing machine faults.

  9. Comprehensive bearing condition monitoring algorithm for incipient fault detection using acoustic emission

    Directory of Open Access Journals (Sweden)

    Amit R. Bhende

    2014-09-01

    Full Text Available The bearing reliability plays major role in obtaining the desired performance of any machine. A continuous condition monitoring of machine is required in certain applications where failure of machine leads to loss of production, human safety and precision. Machine faults are often linked to the bearing faults. Condition monitoring of machine involves continuous watch on the performance of bearings and predicting the faults of bearing before it cause any adversity. This paper investigates an experimental study to diagnose the fault while bearing is in operation. An acoustic emission technique is used in the experimentation. An algorithm is developed to process various types of signals generated from different bearing defects. The algorithm uses time domain analysis along with combination low frequency analysis technique such as fast Fourier transform and high frequency envelope detection. Two methods have adopted for envelope detection which are Hilbert transform and order analysis. Experimental study is carried out for deep groove ball bearing cage defect. Results show the potential effectiveness of the proposed algorithm to determine presence of fault, exact location and severity of fault.

  10. An online technique for condition monitoring the induction generators used in wind and marine turbines

    Science.gov (United States)

    Yang, Wenxian; Tavner, P. J.; Court, R.

    2013-07-01

    Induction generators have been successfully applied to a variety of industries. However, their operation and maintenance in renewable wind and marine energy industries still face challenges due to harsh environments, limited access to site and relevant reliability issues. Hence, further enhancing their condition monitoring is regarded as one of the essential measures for improving their availability. To date, much effort has been made to monitor induction motors, which can be equally applied to monitoring induction generators. However, the achieved techniques still have constrains in particular when dealing with the condition monitoring problems in wind and marine turbine generators. For example, physical measurements of partial discharge, noise and temperature have been widely applied to monitoring induction machinery. They are simple and cost-effective, but unable to be used for fault diagnosis. The spectral analysis of vibration and stator current signals is also a mature technique popularly used in motor/generator condition monitoring practice. However, it often requires sufficient expertise for data interpretation, and significant pre-knowledge about the machines and their components. In particular in renewable wind and marine industries, the condition monitoring results are usually coupled with load variations, which further increases the difficulty of obtaining a reliable condition monitoring result. In view of these issues, a new condition monitoring technique is developed in this paper dedicated for wind and marine turbine generators. It is simple, informative and less load-dependent thus more reliable to deal with the online motor/generator condition monitoring problems under varying loading conditions. The technique has been verified through both simulated and practical experiments. It has been shown that with the aid of the proposed technique, not only the electrical faults but also the shaft unbalance occurring in the generator become detectable

  11. 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.

  12. Condition Monitoring for Helicopter Data. Appendix A

    Science.gov (United States)

    Wen, Fang; Willett, Peter; Deb, Somnath

    2000-01-01

    In this paper the classical "Westland" set of empirical accelerometer helicopter data is analyzed with the aim of condition monitoring for diagnostic purposes. The goal is to determine features for failure events from these data, via a proprietary signal processing toolbox, and to weigh these according to a variety of classification algorithms. As regards signal processing, it appears that the autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. As regards classification, several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately.

  13. Application of EDA methodology for assessment of the rotating machines insulation system condition

    Directory of Open Access Journals (Sweden)

    Ilić Denis

    2016-01-01

    Full Text Available The insulation system of rotating machines of high importance has always been the object of thorough screening with certified test methods that are getting constantly improved. Low-power machines and 'less important' machines like high voltage motors, are rarely subjected to detailed electrical tests because of low resources allocated for their maintenance. The introduction of the EDA methodology in practice creates the conditions for reliable and complete diagnostics of stator windings of big machines, as well as the fast, easy and inexpensive screening for low-power machines (i.e. HV motors. The aim of the paper is to present the EDA methodology and its possibilities, including the solutions within the hardware and software.

  14. Performance Reliability Prediction of Complex System Based on the Condition Monitoring Information

    Directory of Open Access Journals (Sweden)

    Hongxing Wang

    2013-01-01

    Full Text Available Complex system performance reliability prediction is one of the means to understand complex systems reliability level, make maintenance decision, and guarantee the safety of operation. By the use of complex system condition monitoring information and condition monitoring information based on support vector machine, the paper aims to provide an evaluation of the degradation of complex system performance. With degradation assessment results as input variables, the prediction model of reliability is established in Winer random process. Taking the aircraft engine as an example, the effectiveness of the proposed method is verified in the paper.

  15. The monitoring of transient regimes on machine tools based on speed, acceleration and active electric power absorbed by motors

    Science.gov (United States)

    Horodinca, M.

    2016-08-01

    This paper intend to propose some new results related with computer aided monitoring of transient regimes on machine-tools based on the evolution of active electrical power absorbed by the electric motor used to drive the main kinematic chains and the evolution of rotational speed and acceleration of the main shaft. The active power is calculated in numerical format using the evolution of instantaneous voltage and current delivered by electrical power system to the electric motor. The rotational speed and acceleration of the main shaft are calculated based on the signal delivered by a sensor. Three real-time analogic signals are acquired with a very simple computer assisted setup which contains a voltage transformer, a current transformer, an AC generator as rotational speed sensor, a data acquisition system and a personal computer. The data processing and analysis was done using Matlab software. Some different transient regimes were investigated; several important conclusions related with the advantages of this monitoring technique were formulated. Many others features of the experimental setup are also available: to supervise the mechanical loading of machine-tools during cutting processes or for diagnosis of machine-tools condition by active electrical power signal analysis in frequency domain.

  16. 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...

  17. The evaluation of functional heart condition with machine learning algorithms

    Science.gov (United States)

    Overchuk, K. V.; Lezhnina, I. A.; Uvarov, A. A.; Perchatkin, V. A.; Lvova, A. B.

    2017-08-01

    This paper is considering the most suitable algorithms to build a classifier for evaluating of the functional heart condition with the ability to estimate the direction and progress of the patient’s treatment. The cons and pros of algorithms was analyzed with respect to the problem posed. The most optimal solution has been given and justified.

  18. Force sensor based tool condition monitoring using a heterogeneous ensemble learning model.

    Science.gov (United States)

    Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

    2014-11-14

    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.

  19. Research on Remote Video Monitoring System Used for Numerical Control Machine Tools Based on Embedded Technology

    Institute of Scientific and Technical Information of China (English)

    LIU Quan; QU Xuehong; ZHOU Henglin; LONG Yihong

    2006-01-01

    This paper designed an embedded video monitoring system using DSP(Digital Signal Processing) and ARM(Advanced RISC Machine). This system is an important part of self-service operation of numerical control machine tools. At first the analog input signals from the CCD(Charge Coupled Device) camera are transformed into digital signals, and then output to the DSP system, where the video sequence is encoded according to the new generation image compressing standard called H.264. The code will be transmitted to the ARM system through xBus, and then be packed in the ARM system and transmitted to the client port through the gateway. Web technology, embedded technology and image compressing as well as coding technology are integrated in the system, which can be widely used in self-service operation of numerical control machine tools and intelligent robot control areas.

  20. 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

  1. Operant Conditioning of Mental Retardates' Visual Monitoring.

    Science.gov (United States)

    Perryman, Roy E.; And Others

    1981-01-01

    To study improvement of visual monitoring of retardates, specialized training methods backed up by incentives were used. The extent to which these training techniques might be expected to produce results which would generalize was explored. Subjects were eight female mental retardates (ages 15-22) with IQs from 38 to 69. (Author/SJL)

  2. STUDY OF MACHINING PROCESS MONITORING OF FMS BASED ON TIME SERIES ANALYSIS

    Institute of Scientific and Technical Information of China (English)

    Zhang Libin; Su Jian; Liu Yumei; Jia Yazhou

    2004-01-01

    FMS is a sort of highly automatic machining system,how to ensure part quality is master key to system highly active running.At first, series of machining dimension and process capability of flexible manufacturing system(FMS), is analyzed.Result of its, strong self-correlation of data series shows that time series analysis is applicable to data series analyzed.Based on-line modeling and forecasting for data series, principle and method of feedback compensation control is proposed.On a foundation of the virtual instrument platform, Labview of national instrument (NI), FMS dimension and process capability monitoring system(monitoring system) is developed.In practice, it is proved that part quality and process capability of FMS are greatly improved.

  3. VIBRATION SENSOR FOR HEALTH MONITORING OF ELECTRICAL MACHINES IN POWER STATION

    Directory of Open Access Journals (Sweden)

    Neha Gupta

    2012-04-01

    Full Text Available Vibration monitoring in high power electric machines, such as generators and transformers, presents some difficulties due to the insulation and EM immunity requirements .However, the negative influence of the electromagnetic interference (EMI can be a real problem when electrical signals are used to detect and transmitphysical parameters in noisy environments such as electric power generator plants with high levels of EMI. Such problems can be solved using optical fiber sensors, which allow in situ measurements and remote control without electrical wires. The present paper describes a novel fiber optic vibration sensor for health monitoring of electrical machines, which utilizes relatively simple technologies and offers moderate costs. The sensor is optimized for detection of mechanical vibrations in the frequency range 20-100 Hz. Design details and experimental results are reported.

  4. A Novel Framework for Agent-Based Production Remote Monitoring System Design: A Case Study of Injection Machines

    Directory of Open Access Journals (Sweden)

    Yun-Yao Chen

    2013-01-01

    Full Text Available Currently, many injection machine controllers in the market involve PC-based architecture, so engineers can conduct simple and quick operation on the controller via a human-machine interface. However, when there are too many machines in a factory, mining algorithms for multimachines and development of rear-end applications are often trivial and complicated. The operation systems of the machines in factories are different, and different machine models need different transfer protocols for data mining. Therefore, we need to develop different information platforms and machine production information mining systems for cross platform controllers. This research proposed an agent based remote monitoring system for injection machines to solve this problem. The agent-based production remote monitor system framework in this research has the following advantages. (1 It can transmit machine information cross platforms regard of constraints of different operating systems. Controlling frameworks can process data mining and transmission. (2 It can send back machine information actively to the manager without operation of machine operators, mine specific information effectively, and screen unnecessary machine information. (3 It can categorize the required information, filter extra information, and elicit data the user needs.

  5. A Wireless Distributed Condition Monitoring System Based on Bluetooth Technology

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Based on the discussion of bluetooth and network technology, this paper proposed an entire framework of a wireless distributed monitoring system by combining the characteristics of industry application. The feasibility of putting this kind of system in practice is discussed. The wireless distributed monitoring system can enhance the performance of condition monitoring more than the traditional one used now.

  6. 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.

  7. 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.

  8. A novel framework of change-point detection for machine monitoring

    Science.gov (United States)

    Lu, Guoliang; Zhou, Yiqi; Lu, Changhou; Li, Xueyong

    2017-01-01

    The need for automatic machine monitoring has been well known in industries for many years. Although it has been widely accepted that a change in the structural property can indicate the fault in rotating machinery components (e.g., bearing and gears), automatic algorithms for this task are still in progress. In this paper, we propose a novel framework for change-point detection in machine monitoring. The framework includes two phases: (1) anomaly measure: on the basis of an automatic regression (AR) model, a new computation method is proposed to measure anomalies in a given time series which does not require any reference data from other measurement(s); (2) change detection: a new statistical test is employed by using martingale for detecting a potential change in the series which can be operated in an unsupervised and self-conducted manner. Experimental results on testing data captured in real scenarios demonstrated the effectiveness and the realizability of the proposed framework for change-point detection in machine monitoring, which suggests that our framework can be directly applicable in many real-world applications.

  9. Ozone Monitoring Using Support Vector Machine and K-Nearest Neighbors Methods

    Directory of Open Access Journals (Sweden)

    FALEH Rabeb

    2017-05-01

    Full Text Available Due to health impacts caused by the pollutant gases, monitoring and controlling air quality is an important field of interest. This paper deals with ozone monitoring in four stations measuring air quality located in many Tunisian cities using numerous measuring instruments and polluting gas analyzers. Prediction of ozone concentrations in two Tunisian cities, Tunis and Sfax is screened based on supervised classification models. The K -Nearest neighbors results reached 98.7 % success rate in the recognition and ozone identification. Support Vector Machines (SVM with the linear, polynomial and RBF kernel were applied to build a classifier and full accuracy (100% was again achieved with the RBF kernel.

  10. Research on Remote Monitoring and Fault Diagnosis Technology of Numerical Control Machine

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jianyu; GAO Lixin; CUI Lingli; LI Xianghui; WANG Yingwang

    2006-01-01

    Based on the internet technology, it has become possible to complete remote monitoring and fault diagnosis for the numerical control machine. In order to capture the micro-shock signal induced by the incipient fault on the rotating parts, the resonance demodulation technology is utilized in the system. As a subsystem of the remote monitoring system, the embedded data acquisition instrument not only integrates the demodulation board but also complete the collection and preprocess of monitoring data from different machines. Furthermore, through connecting to the internet, the data can be transferred to the remote diagnosis center and data reading and writing function can be finished in the database. At the same time, the problem of the IP address floating in the dial-up of web server is solved by the dynamic DNS technology. Finally, the remote diagnosis software developed on the LabVIEW platform can analyze the monitoring data from manufacturing field. The research results have indicated that the equipment status can be monitored by the system effectively.

  11. Decentralized and overall condition monitoring system for large-scale mobile and complex equipment

    Institute of Scientific and Technical Information of China (English)

    Cao Jianjun; Zhang Peilin; Ren Guoquan; Fu Jianping

    2007-01-01

    It is an urgent project to realize online and overall condition monitoring and timely fault diagnosis for large-scale mobile and complex equipment. Moreover, most of the existing large-scale complex equipment has quite insufficient accessibility of examination, although it still has quite a long service life. The decentralized and overall condition monitoring, as a new concept, is proposed from the point of view of the whole system. A set of complex equipment is divided into several parts in terms of concrete equipment. Every part is processed via one detecting unit, and the main detecting unit is connected with other units. The management work and communications with the remote monitoring center have been taken on by it. Consequently, the difficulty of realizing a condition monitoring system and the complexity of processing information is reduced greatly. Furthermore, excellent maintainability of the condition monitoring system is obtained because of the modularization design. Through an application example,the design and realization of the decentralized and overall condition monitoring system is introduced specifically.Some advanced technologies, such as, micro control unit (MCU), advanced RISC machines (ARM), and control area network (CAN), have been adopted in the system. The system's applicability for the existing large-scale mobile and complex equipment is tested.

  12. Predictive Condition Monitoring of Induction Motor Bearing Using Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Prof. Rakeshkumar A. Patel

    2012-10-01

    Full Text Available Induction motor is critical component in industrial processes and is frequently integrated in commercially available equipment. Safety, reliability, efficiency and performance are the major concerns of induction motor applications. Due to high reliability requirements and cost of breakdown, condition monitoring, diagnosis and Protection increasing importance. Protection of an induction motor (IM against possible problems, such as stator faults, rotor faults and mechanical faults, occurring in the course of its operation is very important, because it is very popular in industries. Bearing fault is well known mechanical fault of IM.41�0faults related to bearing in IM. To avoid break down of IM condition monitoring of motor bearing condition is very important during the normal operation. Various classical and AI techniques like fuzzy logic, neural network, neuro-fuzzy are used for condition monitoring and diagnosis of IM. Among the above mentioned AI techniques, Fuzzy logic is the best technique for condition monitoring and diagnosis of IM bearing condition. Therefore, the present paper focuses on fuzzy logic technique. In this paper Fuzzy logic is design for the condition monitoring and diagnosis of induction motor bearing condition using motor current and speed. After applying Fuzzy logic it has been seen that continuous monitoring of the current and speed values of the motor conditioned monitoring and diagnosis of induction motor bearing condition can be done.

  13. Condition Monitoring of Forward Curved Centrifugal Blower Using Coast Down Time Analysis

    Directory of Open Access Journals (Sweden)

    G. R. Rameshkumar

    2010-01-01

    Full Text Available Mechanical malfunctions such as, rotor unbalance and shaft misalignment are the most common causes of vibration in rotating machineries. Vibration is the most widely used parameter to monitor and asses the machine health condition. In this work, the Coast Down Time (CDT, which is an indicator of faults, is used to assess the condition of the rotating machine as a condition monitoring parameter. CDT is the total time taken by the system to dissipate the momentum acquired during sustained operation. Extensive experiments were conducted on Forward Curved Centrifugal Blower Test Rig at selected cutoff speeds for several combinations of combined horizontal and vertical parallel misalignment, combined parallel and angular misalignment, as well as for various unbalance conditions. As mechanical faults increase, a drastic decrease in CDT is found and this is represented as CDT reduction percentage. A specific correlation between the CDT reduction percentage, level of mechanical faults, and rotational cutoff speeds is observed. The results are analyzed and compared with vibration analysis for potential use of CDT as one of the condition monitoring parameter.

  14. Noncontacting measurement technologies for space propulsion condition monitoring

    Science.gov (United States)

    Randall, M. R.; Barkhoudarian, S.; Collins, J. J.; Schwartzbart, A.

    1987-01-01

    This paper describes four noncontacting measurement technologies that can be used in a turbopump condition monitoring system. The isotope wear analyzer, fiberoptic deflectometer, brushless torque-meter, and fiberoptic pyrometer can be used to monitor component wear, bearing degradation, instantaneous shaft torque, and turbine blade cracking, respectively. A complete turbopump condition monitoring system including these four technologies could predict remaining component life, thus reducing engine operating costs and increasing reliability.

  15. Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

    Directory of Open Access Journals (Sweden)

    Jing-Kui Zhang

    2016-03-01

    Full Text Available The impact-echo (IE method is a popular non-destructive testing (NDT technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location, because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures.

  16. Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines.

    Science.gov (United States)

    Zhang, Jing-Kui; Yan, Weizhong; Cui, De-Mi

    2016-03-26

    The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures.

  17. Influence of Induction Machine and Mechanism Parameters on Starting Transient Processes in Case of Constant Load Conditions

    Directory of Open Access Journals (Sweden)

    Dimitar Spirov

    2005-10-01

    Full Text Available Two-phase induction machine dynamic model in a coordinate system which rotates at synchronous speed and one-mass dynamic model of mechanism driven in relative units describing transient processes when starting an induction machine in case of constant load conditions are developed.The influence of equivalent circuit parameters of induction machine and mechanism parameters on impact currents and torques and starting time of common used induction machines is studied by means of design of experiment method.

  18. 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. 

  19. 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.

  20. Determination of Oil Life for Crane Liebherr LHM 500g Model 301 by Oil Condition Monitoring

    Directory of Open Access Journals (Sweden)

    Payman Salami

    2010-05-01

    Full Text Available The objective of this research is to choose and investigate the best oil replacement time by oil condition monitoring for crane Liebherr LHM 500G model 301 that works near the sea in marine company. This was achieved by investigating different oil sam ple analyses of crane Liebherr LHM 500G model 301. The oil analysis was initially run under regular interval during machines life. Some series of tests were then conducted under the operating hours of machine. Oil samples were regularly collected. Numerical data produced by oil analysis were compared with another sample, in order to quantify the effectiveness of the results of oil condition monitoring technique. The results from this paper have given more understanding on the dependent and independent roles of oil analysis in predicting which oil is more suitable for working machine condition. According to the results, the oil used in this crane can be used more than 130 h and the best oil running time is 160 h.

  1. Effect of the hydrodynamic conditions of electrolyte flow on critical states in electrochemical machining

    Science.gov (United States)

    Sawicki, Jerzy; Paczkowski, Tomasz

    2015-05-01

    The paper presents the results of experimental studies of electrochemical machining process oriented on occurring in the treatment critical states caused by electrolyte flow hydrodynamic conditions in the gap between electrodes. Material forming in electrochemical machining is carried out by anodic dissolution. In general in ECM process, the essence of the treatment is that the workpiece is the anode and the tool is the cathode. The space between the anode and cathode is filled by electrolyte. The current flow between the electrodes causes anodic dissolution process, resulting in the removal of material from the anode. Choosing in the process of electrochemical machining, respectively: anode and cathode material, electrolyte and processing parameters, such conditions can be created that enable a high process efficiency and smoothness of the surface. Inappropriate selection of machining parameters can cause the emergence of critical states in the ECM, which are mainly related to the flow of the electrolyte in the gap between electrodes. This work is an attempt to assess the occurring critical states in ECM on the example of machining of curved surfaces with any sort of outline and curved rotating surfaces.

  2. PREDICTION OF TOOL CONDITION DURING TURNING OF ALUMINIUM/ALUMINA/GRAPHITE HYBRID METAL MATRIX COMPOSITES USING MACHINE LEARNING APPROACH

    Directory of Open Access Journals (Sweden)

    N. RADHIKA

    2015-10-01

    Full Text Available Aluminium/alumina/graphite hybrid metal matrix composites manufactured using stir casting technique was subjected to machining studies to predict tool condition during machining. Fresh tool as well as tools with specific amount of wear deliberately created prior to machining experiments was used. Vibration signals were acquired using an accelerometer for each tool condition. These signals were then processed to extract statistical and histogram features to predict the tool condition during machining. Two classifiers namely, Random Forest and Classification and Regression Tree (CART were used to classify the tool condition. Results showed that histogram features with Random Forest classifier yielded maximum efficiency in predicting the tool condition. This machine learning approach enables the prediction of tool failure in advance, thereby minimizing the unexpected breakdown of tool and machine.

  3. Distributed flexible reconfigurable condition monitoring and diagnosis technology

    Institute of Scientific and Technical Information of China (English)

    HU You-min; YANG Shu-zi; DU Run-sheng

    2006-01-01

    As manufacturing becomes increasingly decentralized,flexible and reconfigurable,more research needs to be done on monitoring and diagnosis technology that accommodate these new trends.The distributed condition monitoring and diagnosis technology based on the "flexible and reconfigurable" concept is studied here.A condition monitoring diagnosis model based on the distributed flexible and reconfigurable idea is proposed in this paper.The component makeup and functions of this model are discussed in detail.The model can fulfill in most instances the manufacturing system requirements for changing the configuration of the monitoring diagnosis system according to different manufacturing system configurations.This model also realizes the flexibility and reconfigurability of the monitoring diagnosis system in some degree.The model has already spawned a successful prototype for monitoring a chemical plant in accomplishing monitoring and control of the production process and equipment.Finally,some future research work is pointed out.

  4. 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...

  5. The use of frequency and wavelet analysis for monitoring surface quality of wood machining applications.

    Science.gov (United States)

    Lemaster, Richard L

    2010-01-01

    The research described in this study is part of a project to provide the technology and theory to quantify surface quality for a variety of wood and wood-based products. The ultimate goal is to provide a means of monitoring trends in surface quality, which can be used to discriminate between "good" products and "bad" products (the methods described in this research are not intended to provide "grading" of individual workpieces) as well as to provide information to the machine operator as to the source of poor-quality machined surfaces. This research investigates the use of both frequency domain analysis as well as the more advanced joint time frequency analysis (JTFA). The disadvantages of traditional frequency analysis as well as the potential of the JTFA are illustrated. Sample surface profiles from actual machining defects were analyzed using traditional frequency analysis. A surface with multiple machining defects was analyzed with both traditional frequency analysis and JTFA (harmonic wavelet). Although the analysis was empirical in nature, the results show that the harmonic wavelet transform is able to detect both stationary and non-stationary surface irregularities as well as pulses (localized defects).

  6. Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning.

    Science.gov (United States)

    Whiteside, David; Cant, Olivia; Connolly, Molly; Reid, Machar

    2017-02-09

    Quantifying external workload is fundamental to training prescription in sport. In tennis, global positioning data are imprecise and fail to capture hitting loads. The current gold standard (manual notation) is time intensive and often not possible given players' heavy travel schedules. The aim of this study was to develop an automated stroke classification system to help quantify hitting load in tennis. 18 athletes wore an inertial measurement unit (IMU) on their wrist during 66 video-recorded training sessions. Video footage was manually notated such that known shot type (serve, rally forehand, slice forehand, forehand volley, rally backhand, slice backhand, backhand volley, smash or false positive) was associated with the corresponding IMU data for 28,582 shots. Six types of machine learning models were then constructed to classify true shot type from the IMU signals. Across 10-fold cross-validation, a cubic kernel support vector machine classified binned shots (overhead, forehand or backhand) with an accuracy of 97.4%. A second cubic kernel support vector machine achieved 93.2% accuracy when classifying all 9 shot types. With a view to monitoring external load, the combination of miniature inertial sensors and machine learning can offer a practical and automated method for quantifying shot counts and for discriminating shot types in elite tennis players.

  7. Condition Monitoring and Faults Diagnosis for Synchronous Generator Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Omer Elfaki Elbashir

    2013-09-01

    Full Text Available Early detection and diagnosis of incipient fault is desirable for on line condition assessment production quality assurance and improved operational efficiency of synchronous generator running of power supply. Artificial Intelligent techniques are increasly used for condition monitoring and fault diagnosis of machines. In this paper, Artificial Neural Network (ANN approach employed for fault diagnosis in the generator, based on monitoring generator currents to give indication of the winding faults. Feed-forward Network, error back propagation training algorithm are used to perform the generator faults diagnosis and their values. NN which has been trained for all possible operating condition of the machine used to classify the incoming data. The inputs of the NN are the stator and rotor currents, and the output represents the running condition of the generator. The training of the NN achieved by the data through a mathematical model based approach to simulate the generator faults at various degree of severity.This paper evaluates through simulation line currents magnitude of the generator .The final results have been represented on a monitoring unit, built using matlab program, to give early warning of the generator failure.

  8. COMORAN. Condition monitoring for railway applications

    Energy Technology Data Exchange (ETDEWEB)

    Herden, Marc-Oliver; Friesen, Ulf [Knorr-Bremse AG, Muenchen (Germany)

    2013-03-01

    It is becoming increasingly important to make sure that railway vehicles undergo maintenance as a function of their true condition. COMORAN is a system developed by Knorr-Bremse to achieve precisely that as far as bogie components are concerned. (orig.)

  9. 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.

  10. 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

    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...

  11. Condition monitoring of pump-turbines

    OpenAIRE

    Valero Ferrando, M.del Carmen; Egusquiza Estévez, Eduard

    2014-01-01

    At present, new renewables like wind, solar and marine energy are having a strong development. The generation of energy by renewables has the disadvantage that it depends on atmospheric conditions. It means that they can generate energy at any moment independently if this energy is required or not by the consumers. For the stability of the electrical grid, supply and demand of energy has to be matched. The surplus of energy produced when consumption is low has to be stored and del...

  12. 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.

  13. Forest machine contractors in Swedish industrial forestry: Significance and conditions during 1986-1993. Doctoral thesis

    Energy Technology Data Exchange (ETDEWEB)

    Liden, E.

    1995-07-01

    The aim of this review was to come to a general understanding of the phenomenon of contracting in forestry as an occupation and a life-style. This has been accomplished by studies on machine ownership, working conditions, and attrition from forestry, using quantitative methods in combination with qualitative ones, during the period 1986 to 1993. The studies pertained to industrial forestry in Sweden. In 1992/93 70% of all machines used in industrial forestry in Sweden were owned by contractors. Together these machines harvested 59% of the total quantity during the 1992/93 harvesting season. Three categories of contractors were recognized; the single contractor, the partner contractor, and the contractor with employees. It was concluded that being a contractor is more a life-style than an occupation. Very often the whole family is involved in the business. The contractors` willingness to work hard and to do a good job is an asset for forestry.

  14. System Reliability Analysis of Redundant Condition Monitoring Systems

    Institute of Scientific and Technical Information of China (English)

    YI Pengxing; HU Youming; YANG Shuzi; WU Bo; CUI Feng

    2006-01-01

    The development and application of new reliability models and methods are presented to analyze the system reliability of complex condition monitoring systems. The methods include a method analyzing failure modes of a type of redundant condition monitoring systems (RCMS) by invoking failure tree model, Markov modeling techniques for analyzing system reliability of RCMS, and methods for estimating Markov model parameters. Furthermore, a computing case is investigated and many conclusions upon this case are summarized. Results show that the method proposed here is practical and valuable for designing condition monitoring systems and their maintenance.

  15. Wind Turbine Drivetrain Condition Monitoring - An Overview (Presentation)

    Energy Technology Data Exchange (ETDEWEB)

    Sheng, S.; Yang, W.

    2013-07-01

    High operation and maintenance costs still hamper the development of the wind industry despite its quick growth worldwide. To reduce unscheduled downtime and avoid catastrophic failures of wind turbines and their components have been and will be crucial to further raise the competitiveness of wind power. Condition monitoring is one of the key tools for achieving such a goal. To enhance the research and development of advanced condition monitoring techniques dedicated to wind turbines, we present an overview of wind turbine condition monitoring, discuss current practices, point out existing challenges, and suggest possible solutions.

  16. Monitoring surface conditions of a Thoroughbred racetrack.

    Science.gov (United States)

    Clanton, C; Kobluk, C; Robinson, R A; Gordon, B

    1991-02-15

    During a pilot study at a Thoroughbred racetrack, information was collected to include weather conditions and track surface properties (moisture content, composition, strength, and coefficient of friction between surface and hoof). Measured weather variables did not correlate to any pattern of horse injuries of breakdowns. Surface moisture content was variable, whereas the moisture content of the compacted cushion was constant. Track surfaces around the starting chutes were more compacted than were other areas of the track. Next to the rail, track surface was softer than the surface toward the middle of the track. The coefficient of friction between a hoof and the surface was not affected by location or surface moisture content.

  17. 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.

  18. 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.

  19. Unified Multi-speed analysis (UMA) for the condition monitoring of aero-engines

    Science.gov (United States)

    Nembhard, Adrian D.; Sinha, Jyoti K.

    2015-12-01

    For rotating machinery in which speeds and dynamics constantly change, performing vibration-based condition monitoring can be challenging. Thus, an effort is made here to develop a Unified Multi-speed fault diagnosis technique that can exploit useful vibration information available at various speeds from a rotating machine in a single analysis. Commonly applied indicators are computed from data collected from a rig at different speeds for a baseline case and different faults. Four separate analyses are performed: single speed at a single bearing, integrated features from multiple speeds at a single bearing, single speed for integrated features from multiple bearings and the proposed Unified Multi-speed analysis. The Unified Multi-speed approach produces the most conspicuous separation and isolation among the conditions tested. Observations made here suggest integration of more dynamic features available at different speeds improves the learning process of the tool which could prove useful for aero-engine condition monitoring.

  20. 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.

  1. 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.

  2. INTELLIGENT TOOL CONDITION MONITORING IN HIGH-SPEED ...

    African Journals Online (AJOL)

    MR PRINCE

    work model has been developed for on-line condition monitoring of tool wear in high-speed ... degraded behaviours in wire electrical dis- ... mathematical models such as regression (Lin et ... an 11 kW Computer Numerical Controlled.

  3. Towards a protocol for community monitoring of caribou body condition

    OpenAIRE

    Gary Kofinas; Phil Lyver; Don Russell; Robert White; Augie Nelson; Nicholas Flanders

    2003-01-01

    Effective ecological monitoring is central to the sustainability of subsistence resources of indigenous communities. For caribou, Arctic indigenous people's most important terrestrial subsistence resource, body condition is a useful measure because it integrates many ecological factors that influence caribou productivity and is recognized by biologists and hunters as meaningful. We draw on experience working with indigenous communities to develop a body condition monitoring protocol for harve...

  4. Investigation of Various Wind Turbine Drivetrain Condition Monitoring Techniques (Presentation)

    Energy Technology Data Exchange (ETDEWEB)

    Sheng, S.

    2011-08-01

    This presentation was given at the 2011 Wind Turbine Reliability Workshop sponsored by Sandia National Laboratories in Albuquerque, NM on August 2-3, 2011. It discusses work for the Gearbox Reliability Collaborative including downtime caused by turbine subsystems, annual failure frequency of turbine subsystems, cost benefits of condition monitoring (CM), the Gearbox Reliability Collaborative's condition monitoring approach and rationale, test setup, and results and observations.

  5. Holistic data analysis at the condition monitoring of wind power plants; Gesamtheitliche Datenanalyse beim Condition Monitoring von Windenergieanlagen

    Energy Technology Data Exchange (ETDEWEB)

    Brenner, Daniel; Tilch, Dietmar [Bosch Rexroth Monitoring Systems GmbH, Dresden (Germany)

    2013-06-01

    First of all, the holistic condition monitoring implies a combination of various sources of information. Currently, there are different systems of condition monitoring of single components at wind power plants. Furthermore, there are various sensors being integrated into the SCADA system. This requires a handling with different solutions as well as control rooms and thus with increased training costs, enhanced personnel costs and enhanced system costs. DMT GmbH (Essen, Federal Republic of Germany) and Bosch Rexroth Monitoring Systems GmbH (Dresden, Federal Republic of Germany) show how a solution can be created by means of a rotor blade monitoring system BLADEcontrol as well as the power train monitoring system Windsafe. This solution enables a combined analysis and is open to other monitoring systems under a unified user interface.

  6. Some studies on condition monitoring techniques for on line condition monitoring and fault diagnosis of mine winder motor.

    Directory of Open Access Journals (Sweden)

    Tarun Kumar. Chatterjee

    2012-08-01

    Full Text Available Survey of existing literature reveals that no serious attempt has been made so far to monitor the health of mine winder motors. The electrical motors are the critical equipment of the mine winders which require constant condition monitoring for planning the right time for their maintenance and thus ensure maximum machineavailability. In this research work an online condition monitoring instrumentation system has been developed based on axial flux, current and vibration monitoring technique for mine winder motor. The online condition monitoring instrumentation system is noninvasive in nature and can be connected with mine winder motors which are in operation. The developed instrumentation system would be able to diagnose the health of mine winder motor and the motor fault of incipient nature can be pinpointed by the trend analysis of the frequency spectrum of time varying signal of axial flux, motor current and vibration.

  7. 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.

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

    Directory of Open Access Journals (Sweden)

    Fouzi Harrou

    2016-09-01

    Full Text Available 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 T2 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.

  9. 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.

  10. Automatic Optimization System of Cutting Condition forDifferent Types of Machining Processes

    Directory of Open Access Journals (Sweden)

    Huda Hatem

    2009-01-01

    Full Text Available This research aims at calculating the optimum cutting condition for various types of machining methods, assisted by computers, (the computer program in this research is designed to solve linear programs; the program is written in v. basic language. The program obtains the results automatically, this occur through entering the preliminary information about the work piece and the operating condition, the program makes the calculation actually by solving a group of experimental relations, depending on the type of machining method (turning, milling, drilling. The program was transferred to package and group of windows to facilitate the use; it will automatically print the initial input and optimal solution, and thus reduce the effort and time required for the calculations, that helps to find the optimum values for the cutting system. Optimum values improved mechanical properties (wear, fatigue, strength … and gave better productivity.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2001-06-01

    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.

  12. WAMS-based monitoring and control of Hopf bifurcations in multi-machine power systems

    Institute of Scientific and Technical Information of China (English)

    Shao-bu WANG; Quan-yuan JIANG; Yi-jia CAO

    2008-01-01

    A method is proposed to monitor and control Hopf bifurcations in multi-machine power systems using the information from wide area measurement systems (WAMSs). The power method (PM) is adopted to compute the pair of conjugate eigenvalues with the algebraically largest real part and the corresponding eigenvectors of the Jacobian matrix of a power system. The distance between the current equilibrium point and the Hopf bifurcation set can be monitored dynamically by computing the pair of conjugate eigenvalues. When the current equilibrium point is close to the Hopf bifurcation set, the approximate normal vector to the Hopf bifurcation set is computed and used as a direction to regulate control parameters to avoid a Hopf bifurcation in the power system described by differential algebraic equations (DAEs). The validity of the proposed method is demonstrated by regulating the reactive power loads in a 14-bus power system.

  13. 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.

  14. Fast beam conditions monitor (BCM1F) for CMS

    CERN Document Server

    Hall-Wilton, Richard; Macpherson, Alick; Ryjov, Vladimir; Stone, Robert L; 10.1109/NSSMIC.2008.4775050

    2009-01-01

    The CMS Beam Conditions and Radiation Monitoring System (BRM) [1] is composed of different subsystems that perform monitoring of, as well as providing the CMS detector protection from, adverse beam conditions inside and around the CMS experiment. This paper presents the Fast Beam Conditions Monitoring subsystem (BCM1F), which is designed for fast flux monitoring based on bunch by bunch measurements of both beam halo and collision product contributions from the LHC beam. The BCM1F is located inside the CMS pixel detector volume close to the beam-pipe and provides real-time information. The detector uses sCVD (single-crystal Chemical Vapor Deposition) diamond sensors and radiation hard front-end electronics, along with an analog optical readout of the signals.

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

    DEFF Research Database (Denmark)

    Schlechtingen, Meik; Santos, Ilmar

    2012-01-01

    in graphical and text format. Within the paper examples of real faults are provided, showing the capabilities of the method proposed. The method can be applied both to existing and new built turbines without the need of any additional hardware installation or manufacturers input.......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...

  16. Optimization of image capturing method of wear particles for condition diagnosis of machine parts

    Institute of Scientific and Technical Information of China (English)

    Yon-Sang CHO; Heung-Sik PARK

    2009-01-01

    Wear particles are inevitably occurred from moving parts, such as a piston-cylinder made from steel or hybrid materials. And a durability of these parts must be evaluated. The wear particle analysis has been known as a very effective method to foreknow and decide a moving situation and a damage of machine parts by using the digital computer image processing. But it is not laid down to calculate shape parameters of wear particle and wear volume. In order to apply image processing method in a durability evaluation of machine parts, it needs to verify the reliability of the calculated data by the image processing and to lay down the number of images and the amount of wear particles in one image. In this work, the lubricated friction experiment was carried out in order to establish the optimum image capture with the 1045 specimen under experiment condition. The wear particle data were calculated differently according to the number of image and the amount of wear particle in one image. The results show that capturing conditions need to he more than 140 wear particles in one image and over 40 images for the reliable data. Thus, the capturing method of wear particles images was optimized for condition diagnosis of machine moving parts.

  17. Spatial and temporal information fusion for crop condition monitoring

    Science.gov (United States)

    Crop growth condition information is critical for crop management and yield estimation. In order to monitor crop conditions from space, high spatial and temporal resolution remote sensing data are required. Data fusion approach provides a way to generate such data set from multiple remote sensing da...

  18. 40 CFR 141.625 - Conditions requiring increased monitoring.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 22 2010-07-01 2010-07-01 false Conditions requiring increased monitoring. 141.625 Section 141.625 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS Stage 2 Disinfection Byproducts Requirements § 141.625 Conditions...

  19. Fundamentals for remote condition monitoring of offshore wind turbine blades

    DEFF Research Database (Denmark)

    McGugan, Malcolm; Sørensen, Bent F.

    2007-01-01

    It is anticipated that the large offshore wind farms planed for the near future will require a level of sensor technology sufficient to monitor their general condition from on-shore stations. The continuous monitoring of operational condition and structural responses will give a higher level...... damage or failure in the Structural materials. The vision is of future blades containing sensors that give very early indications of any damage that is classed as critical or that is developing unacceptably rapidly. This early indication allows the option of changing operating conditions, and of a timely...

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Neural Operant Conditioning as a Core Mechanism of Brain-Machine Interface Control

    Directory of Open Access Journals (Sweden)

    Yoshio Sakurai

    2016-08-01

    Full Text Available The process of changing the neuronal activity of the brain to acquire rewards in a broad sense is essential for utilizing brain-machine interfaces (BMIs, which is essentially operant conditioning of neuronal activity. Currently, this is also known as neural biofeedback, and it is often referred to as neurofeedback when human brain activity is targeted. In this review, we first illustrate biofeedback and operant conditioning, which are methodological background elements in neural operant conditioning. Then, we introduce research models of neural operant conditioning in animal experiments and demonstrate that it is possible to change the firing frequency and synchronous firing of local neuronal populations in a short time period. We also debate the possibility of the application of neural operant conditioning and its contribution to BMIs.

  5. Sensorless speed control of a five-phase induction machine under open-phase condition

    Directory of Open Access Journals (Sweden)

    Ahmed S. Morsy

    2014-05-01

    Full Text Available Recently, multiphase machines have been promoted as competitors to their three-phase counterparts in high-power safety-critical drive applications. Among numerous advantages of multiphase induction machine (IM drives, self-starting and operation under open phase(s stand as the most salient features. With open phase(s, optimal current control provides disturbance- free operation given a set of objective functions. Although hysteresis current control was merely employed in the literature as it offers a simple controller structure to control the remaining healthy phases, it is not suitable for high-power applications. In the literature, multiple synchronous reference frame (dq control can be an alternative; however, it requires back and forth transformations with several calculations and additional sophistication. In this paper, a simple technique employing adaptive proportional resonant (PR current controllers is presented to control a five-phase IM under open-phase conditions. Results for both volt/hertz (V/f and field oriented control (FOC systems are presented. Moreover, sensorless operation under fault condition is also demonstrated by estimating the machine speed using a rotor flux-based model reference adaptive system (MRAS speed estimator. The proposed controllers are experimentally verified and compared. Although FOC provides better dynamic performance, V/f control offers a simpler control structure and a lower number of PR controllers.

  6. System and method for statistically monitoring and analyzing sensed conditions

    Science.gov (United States)

    Pebay, Philippe P.; Brandt, James M. , Gentile; Ann C. , Marzouk; Youssef M. , Hale; Darrian J. , Thompson; David C.

    2010-07-13

    A system and method of monitoring and analyzing a plurality of attributes for an alarm condition is disclosed. The attributes are processed and/or unprocessed values of sensed conditions of a collection of a statistically significant number of statistically similar components subjected to varying environmental conditions. The attribute values are used to compute the normal behaviors of some of the attributes and also used to infer parameters of a set of models. Relative probabilities of some attribute values are then computed and used along with the set of models to determine whether an alarm condition is met. The alarm conditions are used to prevent or reduce the impact of impending failure.

  7. Developmental condition and technical problems on electric insulation for super-conducting electric power machine

    Science.gov (United States)

    Motoyama, H.

    1989-05-01

    The present situations of superconducting electric power machines in the world and studied problems were investigated from viewpoint of the electric insulation. 50MVA generator (CRIE/Hitachi) or 120MVA generator (KWU/Siemens) where the dc superconducting technique was applied on field windings, are developed. As to Superconducting transformer, 220KVA transformer is trially manufactured and the conceptual design of 1,000MVA transformer is made by W.H. or Alstom. Future problems are the study of protecting method for the overvoltage to superconducting electric power machines and the study to prevent the quench for superconducting windings. The respective insulating characteristics of solid and liquid insulators become clear gradually under the cryogenic condition but a large part of insulating characteristics of composite insulator prepared by combination of both insulators are not clear, so that these problems must be clarified.

  8. Condition Monitoring of Helicopter Gearboxes by Embedded Sensing

    Science.gov (United States)

    Suryavanashi, Abhijit; Wang, Shengda; Gao, Robert; Danai, Kourosh; Lewicki, David G.

    2002-01-01

    Health of helicopter gearboxes is commonly assessed by monitoring the housing vibration, thus it is challenged by poor signal-to-noise ratio of the signal measured away from the source. It is hypothesized that vibration measurements from sensors placed inside the gearbox will be much clearer indicators of faults and will eliminate many of the difficulties faced by present condition monitoring systems. This paper outlines our approach to devising such a monitoring system. Several tasks have been outlined toward this objective and the strategy to address each has been described. Among the tasks are wireless sensor design, antenna design, and selection of sensor locations.

  9. 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.

  10. 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.

  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. An overview of crop growing condition monitoring in China agriculture remote sensing monitoring system

    Science.gov (United States)

    Huang, Qing; Zhou, Qing-bo; Zhang, Li

    2009-07-01

    China is a large agricultural country. To understand the agricultural production condition timely and accurately is related to government decision-making, agricultural production management and the general public concern. China Agriculture Remote Sensing Monitoring System (CHARMS) can monitor crop acreage changes, crop growing condition, agriculture disaster (drought, floods, frost damage, pest etc.) and predict crop yield etc. quickly and timely. The basic principles, methods and regular operation of crop growing condition monitoring in CHARMS are introduced in detail in the paper. CHARMS can monitor crop growing condition of wheat, corn, cotton, soybean and paddy rice with MODIS data. An improved NDVI difference model was used in crop growing condition monitoring in CHARMS. Firstly, MODIS data of every day were received and processed, and the max NDVI values of every fifteen days of main crop were generated, then, in order to assessment a certain crop growing condition in certain period (every fifteen days, mostly), the system compare the remote sensing index data (NDVI) of a certain period with the data of the period in the history (last five year, mostly), the difference between NDVI can indicate the spatial difference of crop growing condition at a certain period. Moreover, Meteorological data of temperature, precipitation and sunshine etc. as well as the field investigation data of 200 network counties were used to modify the models parameters. Last, crop growing condition was assessment at four different scales of counties, provinces, main producing areas and nation and spatial distribution maps of crop growing condition were also created.

  13. 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.

  14. Condition monitoring helps make the Space Shuttle Main Engine reusable

    Science.gov (United States)

    Lacroix, W. P.

    1973-01-01

    The Space Shuttle Main Engine (SSME) is a reusable, high-performance liquid-propellant rocket engine being developed for the Space Shuttle Orbiter Vehicle. The SSME has been designed for long life, rapid postflight maintenance, and a fast vehicle turnaround cycle of 160 hours. To meet the unique reusability requirements, the SSME considers maintainability and condition monitoring much as airlines do today. The condition monitoring capabilities designed into this engine are discussed with major emphasis on internal inspection and techniques which ensure the reusability of the SSME.

  15. Wire system ageing assessment and condition monitoring (WASCO)

    Energy Technology Data Exchange (ETDEWEB)

    Fantoni, P.F. (Institute for Energy Technology (IFE) (Norway))

    2009-07-15

    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 contains the results of experiments performed in collaboration with Tecnatom SA, Spain, to compare several cable condition monitoring techniques including LIRA (LIne Resonance Analysis) (au)

  16. Machine Learning and Data Mining for Comprehensive Test Ban Treaty Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Russell, S; Vaidya, S

    2009-07-30

    The Comprehensive Test Ban Treaty (CTBT) is gaining renewed attention in light of growing worldwide interest in mitigating risks of nuclear weapons proliferation and testing. Since the International Monitoring System (IMS) installed the first suite of sensors in the late 1990's, the IMS network has steadily progressed, providing valuable support for event diagnostics. This progress was highlighted at the recent International Scientific Studies (ISS) Conference in Vienna in June 2009, where scientists and domain experts met with policy makers to assess the current status of the CTBT Verification System. A strategic theme within the ISS Conference centered on exploring opportunities for further enhancing the detection and localization accuracy of low magnitude events by drawing upon modern tools and techniques for machine learning and large-scale data analysis. Several promising approaches for data exploitation were presented at the Conference. These are summarized in a companion report. In this paper, we introduce essential concepts in machine learning and assess techniques which could provide both incremental and comprehensive value for event discrimination by increasing the accuracy of the final data product, refining On-Site-Inspection (OSI) conclusions, and potentially reducing the cost of future network operations.

  17. Osiris: A Malware Behavior Capturing System Implemented at Virtual Machine Monitor Layer

    Directory of Open Access Journals (Sweden)

    Ying Cao

    2013-01-01

    Full Text Available To perform behavior based malware analysis, behavior capturing is an important prerequisite. In this paper, we present Osiris system which is a tool to capture behaviors of executable files in Windows system. It collects API calls invoked not only by main process of the analysis file, but also API calls invoked by child processes which are created by main process, injected processes if process injection happens, and service processes if the main process creates services. By modifying the source code of Qemu, Osiris is implemented at the virtual machine monitor layer and has the following advantages. First, it does not rewrite the binary code of analysis file or interfere with its normal execution, so that behavior data are obtained more stealthily and transparently. Second, it employs a multi-virtual machine framework to simulate the network environment for malware analysis, so that network behaviors of a malware are stimulated to a large extend. Third, besides network environment, it also simulates most common host events to stimulate potential malicious behaviors of a malware. The experimental results show that Osiris automates the malware analysis process and provides good behavior data for the following detection algorithm.

  18. 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.

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

    Science.gov (United States)

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

    2017-01-01

    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 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. PMID:28146106

  20. 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.

  1. A study on Geographic National (Urban) Conditions Monitoring of Beijing

    OpenAIRE

    Liu, Q.

    2014-01-01

    This article investigated and surveyed the current situation of the policy of Geographic National (Urban) Conditions Monitoring in Beijing based on the experimental unit over China carried out by National Administration of Surveying, Mapping and Geoinformation. Then analysed the guarantee of the implement considering the characteristics of programming and construction, policy and regulation in Beijing. Finally presented the frame system of Geographic National (Urban) Conditions Monit...

  2. 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.

  3. Towards a protocol for community monitoring of caribou body condition

    Directory of Open Access Journals (Sweden)

    Gary Kofinas

    2003-04-01

    Full Text Available Effective ecological monitoring is central to the sustainability of subsistence resources of indigenous communities. For caribou, Arctic indigenous people's most important terrestrial subsistence resource, body condition is a useful measure because it integrates many ecological factors that influence caribou productivity and is recognized by biologists and hunters as meaningful. We draw on experience working with indigenous communities to develop a body condition monitoring protocol for harvested animals. Local indigenous knowledge provides a broad set of caribou health indicators and explanations of how environmental conditions may affect body condition. Scientific research on caribou body condition provides a basis to develop a simple dichotomous key that includes back fat, intestinal fat, kidney fat and marrow¬fat, as measures of body fat, which in autumn to early winter correlates with the likelihood of pregnancy. The dichotomous key was formulated on "expert knowledge" and validated against field estimates of body composition. We compare local indigenous knowledge indicators with hunter documented data based on the dichotomous key. The potential con¬tribution of community body condition monitoring can be realized through the continued comparative analysis of datasets. Better communication among hunters and scientists, and refinement of data collection and analysis methods are recommended. Results suggest that specific local knowledge may become generalized and integrated between regions if the dichotomous key is used as a generalized (semi-quantitative index and complemented with other science and community-based assessments.

  4. 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.

  5. Distribution Characteristics of Wear Particles from Material of Machine Elements in Lubricant condition

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Yon Sang; Jun, Sung Jae; Kim, Young Hee; Park, Heung Sik [Donga Univ., Busan (Korea, Republic of)

    2007-07-01

    It necessarily follows that wear particles are generated through a friction and wear in a mechanical moving system. The wear particles are relative to the failure and the life of machine elements directly. To analyze the wear particle, its shape characteristics were calculated quantitative values such as diameter, roundness and fractal parameters by digital image processing. In this study, the histograms of shape parameters of wear particles were used for the purpose of analyzing the distribution of wear particles in various conditions. We consider that the histogram of shape parameter can be effectively represented to study a wear mechanism.

  6. Non-stationary condition monitoring through event alignment

    DEFF Research Database (Denmark)

    Pontoppidan, Niels Henrik; Larsen, Jan

    2004-01-01

    . 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...

  7. 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...

  8. 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 acco

  9. 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

  10. Reality Monitoring and Metamemory in Adults with Autism Spectrum Conditions

    Science.gov (United States)

    Cooper, Rose A.; Plaisted-Grant, Kate C.; Baron-Cohen, Simon; Simons, Jon S.

    2016-01-01

    Studies of reality monitoring (RM) often implicate medial prefrontal cortex (mPFC) in distinguishing internal and external information, a region linked to autism-related deficits in social and self-referential information processing, executive function, and memory. This study used two RM conditions (self-other; perceived-imagined) to investigate…

  11. 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

  12. 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

    2016-12-01

    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.

  13. Monitoring and Protection of Oil and Gas Condition in Industrial Using Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Y. Chalapathi Rao

    2012-09-01

    Full Text Available Wireless Sensor Networks (WSNs are one of the fastest growing and emerging technologies in the field of Wireless networking today. WSNs have a vast amount of applications including environmental monitoring, military, ecology, agriculture, inventory control, robotics and health care. This paper focuses on monitoring and protection of oil and gas operations using WSNs that are optimized to decrease installation, and maintenance cost, energy requirements, increase reliability and improve communication efficiency. In addition, simulation experiments using the proposed model are presented. Such models could provide new tools for research in predictive maintenance and condition-based monitoring of factory machinery in general and for open architecture machining systems in particular. Wireless sensing no longer needs to be relegated to locations where access is difficult or where cabling is not practical. Wireless condition monitoring systems can be cost effectively implemented in extensive applications that were historically handled by running routes with data collectors. The result would be a lower cost program with more frequent data collection, increased safety, and lower spare parts inventories. Facilities would be able to run leaner because they will have more confidence in their ability to avoid downtime

  14. Energy harvesting to power embedded condition monitoring hardware

    Science.gov (United States)

    Farinholt, Kevin; Brown, Nathan; Siegel, Jake; McQuown, Justin; Humphris, Robert

    2015-04-01

    The shift toward condition-based monitoring is a key area of research for many military, industrial, and commercial customers who want to lower the overall operating costs of capital equipment and general facilities. Assessing the health of rotating systems such as gearboxes, bearings, pumps and other actuation systems often rely on the need for continuous monitoring to capture transient signals that are evidence of events that could cause (i.e. cavitation), or be the result of (i.e. spalling), damage within a system. In some applications this can be accomplished using line powered analyzers, however for wide-spread monitoring, the use of small-scale embedded electronic systems are more desirable. In such cases the method for powering the electronics becomes a significant design factor. This work presents a multi-source energy harvesting approach meant to provide a robust power source for embedded electronics, capturing energy from vibration, thermal and light sources to operate a low-power sensor node. This paper presents the general design philosophy behind the multi-source harvesting circuit, and how it can be extended from powering electronics developed for periodic monitoring to sensing equipment capable of providing continuous condition-based monitoring.

  15. Condition Monitoring of Blade in Turbomachinery: A Review

    Directory of Open Access Journals (Sweden)

    Ahmed M. Abdelrhman

    2014-03-01

    Full Text Available Blade faults and blade failures are ranked among the most frequent causes of failures in turbomachinery. This paper provides a review on the condition monitoring techniques and the most suitable signal analysis methods to detect and diagnose the health condition of blades in turbomachinery. In this paper, blade faults are categorised into five types in accordance with their nature and characteristics, namely, blade rubbing, blade fatigue failure, blade deformations (twisting, creeping, corrosion, and erosion, blade fouling, and loose blade. Reviews on characteristics and the specific diagnostic methods to detect each type of blade faults are also presented. This paper also aims to provide a reference in selecting the most suitable approaches to monitor the health condition of blades in turbomachinery.

  16. Condition Monitoring of Turbines Using Nonlinear Mapping Method

    Institute of Scientific and Technical Information of China (English)

    Liao Guang-lan; Shi Tie-lin; Jiang Nan

    2004-01-01

    Aiming at the non-linear nature of the signals generated from turbines, curvilinear component analysis (CCA), a novel nonlinear projection method that favors local topology conservation is presented for turbines conditions monitoring. This is accomplished in two steps. Time domain features are extracted from raw vibration signals, and then they are projected into a two-dimensional output space by using CCA method and form regions indicative of specific conditions, which helps classify and identify turbine states visually. Therefore, the variation of turbine conditions can be observed clearly with the trajectory of image points for the feature data in the two-dimensional space, and the occurrence and development of failures can be monitored in time.

  17. 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.)

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

    CERN Document Server

    Leonard, Jessica Lynn; 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

    2014-01-01

    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.

  19. 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.

  20. Condition monitoring, diagnostic and controlling tool for boiler feed pump

    Energy Technology Data Exchange (ETDEWEB)

    Ahmed, Sohail [Siemens AG, Muelheim (Germany). Energy Sector; Leithner, Reinhard; Kosyna, Guenter [TU Braunschweig (Germany)

    2010-07-01

    The boiler feed pump is an important component of a thermal power generation cycle and demands high safety and unquestionable availability for flexible power plant operation. In this research paper, the methodology of a general purpose condition monitoring, diagnostic and controlling tool is presented, which can address the challenges of operational safety and availability as well as optimal operation of a boiler feed pump. This tool not only effectively records the life time consumption of both casings and rotors and monitors the small gaps between casings and rotors but also suggests appropriate actions in order to ensure that the pump operates within the allowable design limits. (orig.)

  1. 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.

  2. A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method.

    Science.gov (United States)

    Li, Wutao; Huang, Zhigang; Lang, Rongling; Qin, Honglei; Zhou, Kai; Cao, Yongbin

    2016-03-04

    Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications.

  3. A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method

    Directory of Open Access Journals (Sweden)

    Wutao Li

    2016-03-01

    Full Text Available Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms level and the monitoring time is on microsecond (μs level, which make the proposed approach usable in practical interference monitoring applications.

  4. A Framework for Intelligent Condition-based Maintenance of Rotating Equipment using Mechanical Condition Monitoring

    Directory of Open Access Journals (Sweden)

    Tahan B. Mohammadreza

    2014-07-01

    Full Text Available The ideal end result of maintenance strategy is to increase profitability, improve product quality and ensure safety conditions. In condition-based maintenance (CBM, asset health is monitored regularly to maximize reliability and availability by determining necessary maintenance at the right time. Review of recent studies shows most of developed approaches propose a standalone system for each stage of maintenance system. In order to standardize a generic architecture for machinery CBM, this paper attempts to introduce an intelligent framework consisting of several functional modules, starting from data acquisition and ending to advisory generation, with the emphasis on approaches of condition monitoring and maintenance decision-making.

  5. Envelope analysis of rotating machine vibrations in variable speed conditions: A comprehensive treatment

    Science.gov (United States)

    Abboud, D.; Antoni, J.; Sieg-Zieba, S.; Eltabach, M.

    2017-02-01

    Nowadays, the vibration analysis of rotating machine signals is a well-established methodology, rooted on powerful tools offered, in particular, by the theory of cyclostationary (CS) processes. Among them, the squared envelope spectrum (SES) is probably the most popular to detect random CS components which are typical symptoms, for instance, of rolling element bearing faults. Recent researches are shifted towards the extension of existing CS tools - originally devised in constant speed conditions - to the case of variable speed conditions. Many of these works combine the SES with computed order tracking after some preprocessing steps. The principal object of this paper is to organize these dispersed researches into a structured comprehensive framework. Three original features are furnished. First, a model of rotating machine signals is introduced which sheds light on the various components to be expected in the SES. Second, a critical comparison is made of three sophisticated methods, namely, the improved synchronous average, the cepstrum prewhitening, and the generalized synchronous average, used for suppressing the deterministic part. Also, a general envelope enhancement methodology which combines the latter two techniques with a time-domain filtering operation is revisited. All theoretical findings are experimentally validated on simulated and real-world vibration signals.

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

    CERN Document Server

    Zagozdzinska, Agnieszka Anna

    2015-01-01

    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...

  7. A REVIEW ON ARTIFICIAL INTELLIGENT SYSTEM FOR BEARING CONDITION MONITORING

    Directory of Open Access Journals (Sweden)

    PIYUSH M. PATEL,

    2011-02-01

    Full Text Available Artificial Intelligence (AI is an emerging technology. Research in AI is focused on developing computational approaches to intelligent behavior. The computer programs with which AI could be associated are primarily processes associated with complexity, ambiguity, ndecisiveness, and uncertainty. This present paper surveys the development of a condition monitoring procedure for different types ofbearings, which involves an artificial intelligence method as well as reviewed in order to examine the capability of AI methods and techniques to effectively address various hard-to-solve design tasks and issues relating different types of bearing fault. Although this review cannot be collectively exhaustive, it may be considered as a valuable guide for researchers who are interested in the domain of AI and wish to explore the opportunities offered by fuzzy logic, artificial neural networks and genetic algorithms for further improvement of conditioning monitoring for different types of bearing under different operating conditioning. Recent trends in research on conditioning monitoring using AI for different bearing have also been included.

  8. 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...

  9. 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.

  10. 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.

  11. Some aspects of AE application in tool condition monitoring

    Science.gov (United States)

    Jemielniak

    2000-03-01

    Acoustic emission (AE) is rather a well-known form of non-destructive testing. In the last few years the technology of the AE measurement has been expanded to cover the area of tool condition monitoring. The paper presents some experience of Warsaw University of Technology (WUT) in such applications of AE. It provides an interpretation of common AE signal distortions and possible solutions to avoid them. Furthermore, a characteristic study of several different AE and ultrasonic sensors being used in WUT is furnished. Evaluation of the applicability of some basic measures of acoustic emission for tool condition monitoring is also presented in the paper. Finally paper presents a method of the catastrophic tool failure detection in turning, which uses symptoms other than the direct magnitude AERMS signal. The method is based on the statistical analysis of the distributions of the AERMS signal.

  12. Model-based condition monitoring for lithium-ion batteries

    Science.gov (United States)

    Kim, Taesic; Wang, Yebin; Fang, Huazhen; Sahinoglu, Zafer; Wada, Toshihiro; Hara, Satoshi; Qiao, Wei

    2015-11-01

    Condition monitoring for batteries involves tracking changes in physical parameters and operational states such as state of health (SOH) and state of charge (SOC), and is fundamentally important for building high-performance and safety-critical battery systems. A model-based condition monitoring strategy is developed in this paper for Lithium-ion batteries on the basis of an electrical circuit model incorporating hysteresis effect. It systematically integrates 1) a fast upper-triangular and diagonal recursive least squares algorithm for parameter identification of the battery model, 2) a smooth variable structure filter for the SOC estimation, and 3) a recursive total least squares algorithm for estimating the maximum capacity, which indicates the SOH. The proposed solution enjoys advantages including high accuracy, low computational cost, and simple implementation, and therefore is suitable for deployment and use in real-time embedded battery management systems (BMSs). Simulations and experiments validate effectiveness of the proposed strategy.

  13. Development of a beam condition monitor for use in experiments at the CERN Large Hadron Collider using synthetic diamond

    CERN Document Server

    Fernández-Hernando, L; Ilgner, C; MacPherson, A; Oh, A; Pernegger, H; Pritchard, T; Stone, R; Worm, S

    2004-01-01

    The CERN Large Hadron Collider (LHC) will collide two counter rotating proton beams, each with a store energy about 350MJ; enough to melt 550kg of copper. If there is failure in an element of the accelerator, the resulting beam losses could cause damage not only to the machine but also to the experiments. A Beam Condition Monitor (BCM) is foreseen to monitor last increments of particle flux near the interaction point and if necessary, to generate an abort signal to the LHC accelerator control, to dump the beams. Due to its radiation hardness and minimal services requirements, synthetic CVD diamond is being considered as BCM sensor option. (12 refs).

  14. 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.

  15. Predictive Condition Monitoring of Induction Motor Bearing Using Fuzzy Logic

    OpenAIRE

    2012-01-01

    Induction motor is critical component in industrial processes and is frequently integrated in commercially available equipment. Safety, reliability, efficiency and performance are the major concerns of induction motor applications. Due to high reliability requirements and cost of breakdown, condition monitoring, diagnosis and Protection increasing importance. Protection of an induction motor (IM) against possible problems, such as stator faults, rotor faults and mechanical faults, occurring i...

  16. 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...

  17. The Beam Conditions Monitor of the LHCb Experiment

    CERN Document Server

    Ilgner, Ch; Lieng, M; Nedos, M; Sauerbrey, J; Schleich, S; Spaan, B; Warda, K; Wishahi, J

    2010-01-01

    The LHCb experiment at the European Organization for Nuclear Research (CERN) is dedicated to precision measurements of CP violation and rare decays of B hadrons. Its most sensitive components are protected by means of a Beam Conditions Monitor (BCM), based on polycrystalline CVD diamond sensors. Its configuration, operation and decision logics to issue or remove the beam permit signal for the Large Hadron Collider (LHC) are described in this paper.

  18. 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.

  19. 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.

  20. Condition monitoring for a neutral beam injector cryopumping system

    Energy Technology Data Exchange (ETDEWEB)

    Wright, N., E-mail: n.wright@lboro.ac.uk [School of Electronic and Electrical Engineering, Loughborough University, Loughborough LE11 3TU (United Kingdom); Dixon, R., E-mail: r.dixon@lboro.ac.uk [School of Electronic and Electrical Engineering, Loughborough University, Loughborough LE11 3TU (United Kingdom); Verhoeven, R., E-mail: roel.verhoeven@ccfe.ac.uk [JET-EFDA, Culham Science Centre, Abingdon OX14 3DB (United Kingdom)

    2013-10-15

    Highlights: ► The development of a cryopumping condition monitoring scheme is presented. ► A residual generation scheme is used to detect two faults. ► Kalman filtering is used to generate the residuals. ► A filtering and voting arrangement is used to evaluate the residuals. ► A non-linear simulation model is used to verify the scheme. -- Abstract: For neutral beam injection systems, the maintenance of a vacuum inside the injector box is essential for normal operation. Cryogenic pumping systems are often used to create and maintain this vacuum. Cryogenic pumping systems have been deployed on the neutral beam heating systems supporting the Joint European Torus. With these as a target application, the development of a condition monitoring scheme is presented. The scheme uses a residual generation approach. A bank of Kalman filters is used to estimate measured process variables. A residual evaluator is used to map residual signals onto a set of faults. Two example faults are simulated to demonstrate the response of the scheme. This paper contributes to the wider fusion development programme by demonstrating how a contemporary condition monitoring technique can be applied to a fusion support system, in order to improve its availability.

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

    Science.gov (United States)

    Hohmann, Siegfried; Kögel, Svea; Brunner, Yvonne; Schmieg, Barbara; Ewald, Christina; Kirschhöfer, Frank; Brenner-Weiß, Gerald; Länge, Kerstin

    2015-05-21

    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.

  2. 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.

  3. An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring

    Directory of Open Access Journals (Sweden)

    Tien-Kan Chung

    2016-02-01

    Full Text Available An attachable electromagnetic-energy-harvester driven wireless vibration-sensing system for monitoring milling-processes and cutter-wear/breakage-conditions is demonstrated. The system includes an electromagnetic energy harvester, three single-axis Micro Electro-Mechanical Systems (MEMS accelerometers, a wireless chip module, and corresponding circuits. The harvester consisting of magnets with a coil uses electromagnetic induction to harness mechanical energy produced by the rotating spindle in milling processes and consequently convert the harnessed energy to electrical output. The electrical output is rectified by the rectification circuit to power the accelerometers and wireless chip module. The harvester, circuits, accelerometer, and wireless chip are integrated as an energy-harvester driven wireless vibration-sensing system. Therefore, this completes a self-powered wireless vibration sensing system. For system testing, a numerical-controlled machining tool with various milling processes is used. According to the test results, the system is fully self-powered and able to successfully sense vibration in the milling processes. Furthermore, by analyzing the vibration signals (i.e., through analyzing the electrical outputs of the accelerometers, criteria are successfully established for the system for real-time accurate simulations of the milling-processes and cutter-conditions (such as cutter-wear conditions and cutter-breaking occurrence. Due to these results, our approach can be applied to most milling and other machining machines in factories to realize more smart machining technologies.

  4. An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring.

    Science.gov (United States)

    Chung, Tien-Kan; Yeh, Po-Chen; Lee, Hao; Lin, Cheng-Mao; Tseng, Chia-Yung; Lo, Wen-Tuan; Wang, Chieh-Min; Wang, Wen-Chin; Tu, Chi-Jen; Tasi, Pei-Yuan; Chang, Jui-Wen

    2016-02-23

    An attachable electromagnetic-energy-harvester driven wireless vibration-sensing system for monitoring milling-processes and cutter-wear/breakage-conditions is demonstrated. The system includes an electromagnetic energy harvester, three single-axis Micro Electro-Mechanical Systems (MEMS) accelerometers, a wireless chip module, and corresponding circuits. The harvester consisting of magnets with a coil uses electromagnetic induction to harness mechanical energy produced by the rotating spindle in milling processes and consequently convert the harnessed energy to electrical output. The electrical output is rectified by the rectification circuit to power the accelerometers and wireless chip module. The harvester, circuits, accelerometer, and wireless chip are integrated as an energy-harvester driven wireless vibration-sensing system. Therefore, this completes a self-powered wireless vibration sensing system. For system testing, a numerical-controlled machining tool with various milling processes is used. According to the test results, the system is fully self-powered and able to successfully sense vibration in the milling processes. Furthermore, by analyzing the vibration signals (i.e., through analyzing the electrical outputs of the accelerometers), criteria are successfully established for the system for real-time accurate simulations of the milling-processes and cutter-conditions (such as cutter-wear conditions and cutter-breaking occurrence). Due to these results, our approach can be applied to most milling and other machining machines in factories to realize more smart machining technologies.

  5. Rotor speed estimation of induction machines by monitoring the stator voltages and currents

    Energy Technology Data Exchange (ETDEWEB)

    Ho, S.Y.S.; Langman, R.A. [Tasmania Univ., Hobart, TAS (Australia)

    1995-12-31

    Accurate measurement of induction motor speed is routinely obtained by using a transducer coupled on the shaft. In many industrial situations, this is not acceptable as there may be no room for a suitable transducer, or else the motor environment may be too unpleasant. It is in theory possible to calculate the speed by monitoring the terminal voltages and currents (plus knowing the angular synchronous speed) and then applying these to the differential equations of motor. Two rotor speed algorithms were investigated. Unsatisfactory results were obtained with an algorithm based on the machine equations in a stationary reference frame because at some stage the algorithm divides zero by zero. To avoid these problems the time varying stator voltages and currents were further transformed into the synchronous reference frame so that they end up with dc electrical quantities. This algorithm of obtaining the tangent of the phase angle, for the determination of the rotor speed, was discussed and tested. The analysis presented in this paper points out that the speed of induction motor may be estimated at about +- 0.1 percent uncertainty from measurement of the stator voltage and current. (author). 5 figs., 5 refs.

  6. Factorial switching linear dynamical systems applied to physiological condition monitoring.

    Science.gov (United States)

    Quinn, John A; Williams, Christopher K I; McIntosh, Neil

    2009-09-01

    Condition monitoring often involves the analysis of systems with hidden factors that switch between different modes of operation in some way. Given a sequence of observations, the task is to infer the filtering distribution of the switch setting at each time step. In this paper, we present factorial switching linear dynamical systems as a general framework for handling such problems. We show how domain knowledge and learning can be successfully combined in this framework, and introduce a new factor (the "X-factor") for dealing with unmodeled variation. We demonstrate the flexibility of this type of model by applying it to the problem of monitoring the condition of a premature baby receiving intensive care. The state of health of a baby cannot be observed directly, but different underlying factors are associated with particular patterns of physiological measurements and artifacts. We have explicit knowledge of common factors and use the X-factor to model novel patterns which are clinically significant but have unknown cause. Experimental results are given which show the developed methods to be effective on typical intensive care unit monitoring data.

  7. Effect of wire EDM conditions on generation of residual stresses in machining of aluminum 2014 T6 alloy

    Directory of Open Access Journals (Sweden)

    Pujari Srinivasa Rao

    2016-06-01

    Full Text Available Wire electrical discharge machining (EDM possesses many advantages over the conventional manufacturing process. Hence, this process was used for machining of all conductive materials; especially, nowadays this is the most common process for machining of aerospace aluminum alloys. This process produces complex shapes in aluminum alloys with extremely tight tolerances in a single setup. But, for good surface integrity and longer service life, the residual stresses generated on the components should be as low as possible and it depends on the setting of process parameters and the material to be machined. In wire EDM, much of the work was concentrated on Titanium alloys, Inconel alloys and various types of steels and partly on aluminum alloys. The present investigation was a parametric analysis of wire EDM parameters on residual stresses in the machining of aluminum alloy using Taguchi method. The results obtained had shown a wide range of residual stresses from 8.2 to 405.6 MPa. It also influenced the formation of various intermetallics such as AlCu and AlCu3. Microscopic examination revealed absence of surface cracks on aluminum surface at all the machining conditions. Here, an attempt was made to compare the results of aluminum alloy with the available machined data for other metals.

  8. Global monitoring of the condition of large hydraulic excavators from the viewpoint of an OEM; Globale Zustandsueberwachung von Grosshydraulikbaggern aus Sicht eines OEM

    Energy Technology Data Exchange (ETDEWEB)

    Brueck, Peter; Zimmermann, Erik [Komatsu Mining Germany GmbH, Duesseldorf (Germany)

    2011-01-15

    The contribution describes the monitoring of the condition of large hydraulic excavators with the Komatsu solutions as an example. The solutions of machine data logging, global data transmission and analytical tools are considered and explained. In addition the advantages of the different solutions for the user and manufacturer are listed and described with the aid of an example. (orig.)

  9. A global condition monitoring system for wind turbines

    DEFF Research Database (Denmark)

    Schlechtingen, Meik

    , which bear the potential to support plant owners reducing turbine downtime and lowering costs. In this research a global condition monitoring system is proposed, which provides a platform to take advantage of the different information sources available to operators. One of the most common sources...... 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...... with false brinelling. Finally, a global fuzzy expert system is developed giving the possibility of linking all available information in terms of fuzzy logic rules. It is important to highlight that this research is based on real measured data coming from two wind power plants with turbines of a different...

  10. Fault diagnosis and condition monitoring of wind turbines

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad; Mirzaei, Mahmood

    2017-01-01

    standard sensors on modern wind turbines, including moment sensors and rotor angle sensors. This approach will allow the method to be applied to existing wind turbines without any modifications. The method is based on the detection of asymmetries in the rotor system caused by changes or faults in the rotor......This paper describes a model-free method for the fault diagnosis and condition monitoring of rotor systems in wind turbines. Both fault diagnosis and monitoring can be achieved without using a model for the wind turbine, applied controller, or wind profiles. The method is based on measurements from...... and phase information of the modulation signals. It is possible to detect and isolate which blade is faulty or has been changed based on these signatures. Furthermore, the faulty component can be isolated, ie, the actuator, sensor or blade, and the type of fault can be determined. The method can be used...

  11. Transmission path phase compensation for gear monitoring under fluctuating load conditions

    Science.gov (United States)

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

    2006-10-01

    Vibration can be monitored under fluctuating load conditions if provision is made for taking into account the fluctuation in machine speed, the response amplitude modulation caused by the change in input force, and the amplitude and phase effects on the measured response from the transmission path. Methodologies have been developed to compensate for the effects of fluctuating speed and amplitude modulation. However, this article investigates the effect of the transmission path phase. This is discussed in terms of the effect this phase has on synchronous averaging. A new approach is presented to resolve the influence that the transmission path phase has on synchronous averaging. The approach is used for the experimental data measured on a helical gear test rig. A significant improvement in the rate of convergence was obtained by adopting the new approach which compensates for the phase shifting in the measured structural response. This contrasts with conventional synchronous averaging with order tracking which does not compensate for structural response phase shifting.

  12. 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.

  13. 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.

  14. 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).

  15. 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.

  16. 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

  17. 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.

  18. 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

  19. 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.

  20. 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...

  1. Condition monitoring of reciprocating seal based on FBG sensors

    Science.gov (United States)

    Zhao, Xiuxu; Zhang, Shuanshuan; Wen, Pengfei; Zhen, Wenhan; Ke, Wei

    2016-07-01

    The failure of hydraulic reciprocating seals will seriously affect the normal operation of hydraulic reciprocating machinery, so the potential fault condition monitoring of reciprocating seals is very important. However, it is extremely difficult because of the limitation of reciprocating motion and the structure constraints of seal groove. In this study, an approach using fiber Bragg grating (FBG) sensors is presented. Experimental results show that the contact strain changes of a reciprocating seal can be detected by FBG sensors in the operation process of the hydraulic cylinders. The failure condition of the reciprocating seal can be identified by wavelet packet energy entropy, and the center frequency of power spectrum analysis. It can provide an effective solution for the fault prevention and health management of reciprocating hydraulic rod seals.

  2. The CMS fast beams condition monitor back-end electronics based on MicroTCA technology: status and development

    Science.gov (United States)

    Zagozdzinska, Agnieszka A.; Dabrowski, Anne E.; Pozniak, Krzysztof T.

    2015-09-01

    The Fast Beams Condition Monitor (BCM1F), upgraded for LHC Run II, is used to measure the online luminosity and machine induced background for the CMS experiment. The detector consists of 24 single-crystal CVD diamond sensors that are read out with a custom fast front-end chip fabricated in 130 nm CMOS technology. Since the signals from the sensors are used for real time monitoring of the LHC conditions they are processed by dedicated 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. In operational modes of high rates, consecutive events, spaced in time by less than 12.5 ns, may cause partially overlapping events. Hence, novel signal processing techniques are deployed to resolve overlapping peaks. The high accuracy qualification of the signals is crucial to determine the luminosity and the machine induced background rates for the CMS experiment and the LHC.

  3. Test report on the machine-mounted continuous respirable dust monitor

    Energy Technology Data Exchange (ETDEWEB)

    Kissell, F.N.; Thimons, E.D. [Pittsburgh Research Laboratory, Pittsburgh, PA (USA). National Institute for Occupational Safety and Health

    2001-07-01

    The machine-mounted continuous respirable dust monitor (MMCRDM) is a fixed-location area sampling device developed for possible use at the working face of an underground coal mine. This device, based on proprietary technology known as the tapered element oscillating microbalance, has evolved over the past eight years through a cooperative effort of the former Bureau of Mines, MSHA, and the Rupprecht & Patashnick Company in Albany, NY. The capability to measure respirable coal mine dust levels on a continuous basis, rather than depending solely on periodic samples obtained from the traditional coal mine dust samplers, has been a goal in the mining industry for nearly two decades. Recently, an extensive series of laboratory and underground tests was conducted by NIOSH with the cooperation of MSHA and coal operators to test the performance of the MMCRDM. In preliminary laboratory testing, the MMCRDM seemed to work well. However, in every underground test, when compared to reference samplers placed close to the inlet, the MMCRDM failed to meet the 25% accuracy criterion specified in the contract under which it was developed. Two reasons explain this failure: first, in most tests the bias (the relative discrepancy between the average MMCRDM concentration and the average reference sampler concentration) was too great. Second, the variability of the samplers used for reference comparison was too large. Finally, the underground testing of the MMCRDMs showed that they are quite unreliable at this stage of development. In the majority of mine tests, no more than 10 shifts of data were taken before the MMCRDM failed to function properly. Major breakdowns, requiring the return of the MMCRDM to the factory for repairs, occurred on average every 28 days. To be considered mine-worthy, MMCRDM reliability must be substantially improved. 6 refs., 7 figs.

  4. 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)

  5. Real time video analysis to monitor neonatal medical condition

    Science.gov (United States)

    Shirvaikar, Mukul; Paydarfar, David; Indic, Premananda

    2017-05-01

    One in eight live births in the United States is premature and these infants have complications leading to life threatening events such as apnea (pauses in breathing), bradycardia (slowness of heart) and hypoxia (oxygen desaturation). Infant movement pattern has been hypothesized as an important predictive marker for these life threatening events. Thus estimation of movement along with behavioral states, as a precursor of life threatening events, can be useful for risk stratification of infants as well as for effective management of disease state. However, more important and challenging is the determination of the behavioral state of the infant. This information includes important cues such as sleep position and the status of the eyes, which are important markers for neonatal neurodevelopment state. This paper explores the feasibility of using real time video analysis to monitor the condition of premature infants. The image of the infant can be segmented into regions to localize and focus on specific areas of interest. Analysis of the segmented regions can be performed to identify different parts of the body including the face, arms, legs and torso. This is necessary due to real-time processing speed considerations. Such a monitoring system would be of great benefit as an aide to medical staff in neonatal hospital settings requiring constant surveillance. Any such system would have to satisfy extremely stringent reliability and accuracy requirements, before it can be deployed in a hospital care unit, due to obvious reasons. The effect of lighting conditions and interference will have to be mitigated to achieve such performance.

  6. Structural health condition monitoring of rails using acoustic emission techniques

    Science.gov (United States)

    Yilmazer, Pinar

    In-service rails can develop several types of structural defects due to fatigue and wear caused by rolling stock passing over them. Most rail defects will develop gradually over time thus permitting inspection engineers to detect them in time before final failure occurs. In the UK, certain types of severe rail defects such as tache ovales, require the fitting of emergency clamps and the imposing of an Emergency Speed Restriction (ESR) until the defects are removed. Acoustic emission (AE) techniques can be applied for the detection and continuous monitoring of defect growth therefore removing the need of imposing strict ESRs. The work reported herewith aims to develop a sound methodology for the application of AE in order to detect and subsequently monitor damage evolution in rails. To validate the potential of the AE technique, tests have been carried out under laboratory conditions on three and four-point bending samples manufactured from 260 grade rail steel. Further tests, simulating the background noise conditions caused by passing rolling stock have been carried out using special experimental setups. The crack growth events have been simulated using a pencil tip break..

  7. The Performance of the Beam Conditions and Radiation Monitoring System of CMS

    CERN Document Server

    Dabrowski, Anne

    2011-01-01

    The Beam Conditions and Radiation Monitoring System (BRM), is installed in CMS to protect the CMS detector from high beam losses and to provide feedback to the LHC and CMS on the beam conditions. The primary detector sub-systems are based on either single crystal diamond sensors (BCM1F) for particle counting with nanosecond resolution or on polycrystalline diamonds (BCM2; BCM1L) for integrated signal current measurements. Beam scintillation counters (BSC) are also used during low luminosity running. The detectors have radiation hard front-end electronics and are read out independently of the CMS central data acquisition and are online whenever there is beam in the LHC machine. The various sub-systems exploit different time resolutions and position locations to be able to monitor the beam induced backgrounds and the flux of particles produced during collisions. This paper describes the CMS BRM system and the complementary aspects of the installed BRM sub-detectors to measure both single particle count rates a...

  8. SSME Condition Monitoring Using Neural Networks and Plume Spectral Signatures

    Science.gov (United States)

    Hopkins, Randall; Benzing, Daniel

    1996-01-01

    For a variety of reasons, condition monitoring of the Space Shuttle Main Engine (SSME) has become an important concern for both ground tests and in-flight operation. The complexities of the SSME suggest that active, real-time condition monitoring should be performed to avoid large-scale or catastrophic failure of the engine. In 1986, the SSME became the subject of a plume emission spectroscopy project at NASA's Marshall Space Flight Center (MSFC). Since then, plume emission spectroscopy has recorded many nominal tests and the qualitative spectral features of the SSME plume are now well established. Significant discoveries made with both wide-band and narrow-band plume emission spectroscopy systems led MSFC to develop the Optical Plume Anomaly Detection (OPAD) system. The OPAD system is designed to provide condition monitoring of the SSME during ground-level testing. The operational health of the engine is achieved through the acquisition of spectrally resolved plume emissions and the subsequent identification of abnormal emission levels in the plume indicative of engine erosion or component failure. Eventually, OPAD, or a derivative of the technology, could find its way on to an actual space vehicle and provide in-flight engine condition monitoring. This technology step, however, will require miniaturized hardware capable of processing plume spectral data in real-time. An objective of OPAD condition monitoring is to determine how much of an element is present in the SSME plume. The basic premise is that by knowing the element and its concentration, this could be related back to the health of components within the engine. For example, an abnormal amount of silver in the plume might signify increased wear or deterioration of a particular bearing in the engine. Once an anomaly is identified, the engine could be shut down before catastrophic failure occurs. Currently, element concentrations in the plume are determined iteratively with the help of a non-linear computer

  9. Machining accuracy of crowns by CAD/CAM system using TCP/IP: influence of restorative material and scanning condition.

    Science.gov (United States)

    Tomita, Sachiko; Shin-ya, Akiyoshi; Gomi, Harunori; Shin-ya, Akikazu; Yokoyama, Daiichiro

    2007-07-01

    The purpose of this study was to determine the optimal condition for fabricating accurate crowns efficiently using an internet-based CAD/CAM system. The influences of three different CAD/CAM restorative materials (titanium, porcelain, and composite resin) and three different step-over scanning distances (0.01 mm, 0.11 mm, and 0.21 mm) were evaluated, and their interactive effects were carefully examined. Several points on the inner and outer surfaces of machined crowns - as well as height - were measured. These measurements were then compared with the original models, from which machining accuracy was obtained. At all measuring points, the inner surface of all crowns was machined larger than the die model, whereas the cervical area of porcelain crown was machined smaller than the crown model. Results of this study revealed that a step-over distance of 0.11 mm was an optimal scanning condition, taking into consideration the interactive effects of scanning time required, data volume, and machining accuracy.

  10. 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....

  11. Condition Based Monitoring of Gas Turbine Combustion Components

    Energy Technology Data Exchange (ETDEWEB)

    Ulerich, Nancy; Kidane, Getnet; Spiegelberg, Christine; Tevs, Nikolai

    2012-09-30

    The objective of this program is to develop sensors that allow condition based monitoring of critical combustion parts of gas turbines. Siemens teamed with innovative, small companies that were developing sensor concepts that could monitor wearing and cracking of hot turbine parts. A magnetic crack monitoring sensor concept developed by JENTEK Sensors, Inc. was evaluated in laboratory tests. Designs for engine application were evaluated. The inability to develop a robust lead wire to transmit the signal long distances resulted in a discontinuation of this concept. An optical wear sensor concept proposed by K Sciences GP, LLC was tested in proof-of concept testing. The sensor concept depended, however, on optical fiber tips wearing with the loaded part. The fiber tip wear resulted in too much optical input variability; the sensor could not provide adequate stability for measurement. Siemens developed an alternative optical wear sensor approach that used a commercial PHILTEC, Inc. optical gap sensor with an optical spacer to remove fibers from the wearing surface. The gap sensor measured the length of the wearing spacer to follow loaded part wear. This optical wear sensor was developed to a Technology Readiness Level (TRL) of 5. It was validated in lab tests and installed on a floating transition seal in an F-Class gas turbine. Laboratory tests indicate that the concept can measure wear on loaded parts at temperatures up to 800{degrees}C with uncertainty of < 0.3 mm. Testing in an F-Class engine installation showed that the optical spacer wore with the wearing part. The electro-optics box located outside the engine enclosure survived the engine enclosure environment. The fiber optic cable and the optical spacer, however, both degraded after about 100 operating hours, impacting the signal analysis.

  12. Embedded strain gauges for condition monitoring of silicone gaskets.

    Science.gov (United States)

    Schotzko, Timo; Lang, Walter

    2014-07-10

    A miniaturized strain gauge with a thickness of 5 µm is molded into a silicone O-ring. This is a first step toward embedding sensors in gaskets for structural health monitoring. The signal of the integrated sensor exhibits a linear correlation with the contact pressure of the O-ring. This affords the opportunity to monitor the gasket condition during installation. Thus, damages caused by faulty assembly can be detected instantly, and early failures, with their associated consequences, can be prevented. Through the embedded strain gauge, the contact pressure applied to the gasket can be directly measured. Excessive pressure and incorrect positioning of the gasket can cause structural damage to the material of the gasket, which can lead to an early outage. A platinum strain gauge is fabricated on a thin polyimide layer and is contacted through gold connections. The measured resistance pressure response exhibits hysteresis for the first few strain cycles, followed by a linear behavior. The short-term impact of the embedded sensor on the stability of the gasket is investigated. Pull-tests with O-rings and test specimens have indicated that the integration of the miniaturized sensors has no negative impact on the stability in the short term.

  13. Embedded Strain Gauges for Condition Monitoring of Silicone Gaskets

    Directory of Open Access Journals (Sweden)

    Timo Schotzko

    2014-07-01

    Full Text Available A miniaturized strain gauge with a thickness of 5 µm is molded into a silicone O-ring. This is a first step toward embedding sensors in gaskets for structural health monitoring. The signal of the integrated sensor exhibits a linear correlation with the contact pressure of the O-ring. This affords the opportunity to monitor the gasket condition during installation. Thus, damages caused by faulty assembly can be detected instantly, and early failures, with their associated consequences, can be prevented. Through the embedded strain gauge, the contact pressure applied to the gasket can be directly measured. Excessive pressure and incorrect positioning of the gasket can cause structural damage to the material of the gasket, which can lead to an early outage. A platinum strain gauge is fabricated on a thin polyimide layer and is contacted through gold connections. The measured resistance pressure response exhibits hysteresis for the first few strain cycles, followed by a linear behavior. The short-term impact of the embedded sensor on the stability of the gasket is investigated. Pull-tests with O-rings and test specimens have indicated that the integration of the miniaturized sensors has no negative impact on the stability in the short term.

  14. 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.

  15. 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

    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...... with simulated data and as the obtained results are promising, further work will be on a validation of the method using real wind turbine data....

  16. 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

    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......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...... with simulated data and as the obtained results are promising, further work will be on a validation of the method using real wind turbine data....

  17. New methods for the condition monitoring of level crossings

    Science.gov (United States)

    García Márquez, Fausto Pedro; Pedregal, Diego J.; Roberts, Clive

    2015-04-01

    Level crossings represent a high risk for railway systems. This paper demonstrates the potential to improve maintenance management through the use of intelligent condition monitoring coupled with reliability centred maintenance (RCM). RCM combines advanced electronics, control, computing and communication technologies to address the multiple objectives of cost effectiveness, improved quality, reliability and services. RCM collects digital and analogue signals utilising distributed transducers connected to either point-to-point or digital bus communication links. Assets in many industries use data logging capable of providing post-failure diagnostic support, but to date little use has been made of combined qualitative and quantitative fault detection techniques. The research takes the hydraulic railway level crossing barrier (LCB) system as a case study and develops a generic strategy for failure analysis, data acquisition and incipient fault detection. For each barrier the hydraulic characteristics, the motor's current and voltage, hydraulic pressure and the barrier's position are acquired. In order to acquire the data at a central point efficiently, without errors, a distributed single-cable Fieldbus is utilised. This allows the connection of all sensors through the project's proprietary communication nodes to a high-speed bus. The system developed in this paper for the condition monitoring described above detects faults by means of comparing what can be considered a 'normal' or 'expected' shape of a signal with respect to the actual shape observed as new data become available. ARIMA (autoregressive integrated moving average) models were employed for detecting faults. The statistical tests known as Jarque-Bera and Ljung-Box have been considered for testing the model.

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

    Science.gov (United States)

    Zhang, Cunji; Yao, Xifan; Zhang, Jianming; Jin, Hong

    2016-05-31

    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.

  19. Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

    Science.gov (United States)

    Chen, Lujie; Dubrawski, Artur; Wang, Donghan; Fiterau, Madalina; Guillame-Bert, Mathieu; Bose, Eliezer; Kaynar, Ata M; Wallace, David J; Guttendorf, Jane; Clermont, Gilles; Pinsky, Michael R; Hravnak, Marilyn

    2016-07-01

    The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability. Observational cohort study. Twenty-four-bed trauma step-down unit. Two thousand one hundred fifty-three patients. Noninvasive vital sign monitoring data (heart rate, respiratory rate, peripheral oximetry) recorded on all admissions at 1/20 Hz, and noninvasive blood pressure less frequently, and partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were vital sign deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained machine-learning algorithms. The best model was evaluated on test set alerts to enact online alert classification over time. The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve performance of 0.79 (95% CI, 0.67-0.93) for peripheral oximetry at the instant the vital sign first crossed threshold and increased to 0.87 (95% CI, 0.71-0.95) at 3 minutes into the alerting period. Blood pressure area under the curve started at 0.77 (95% CI, 0.64-0.95) and increased to 0.87 (95% CI, 0.71-0.98), whereas respiratory rate area under the curve started at 0.85 (95% CI, 0.77-0.95) and increased to 0.97 (95% CI, 0.94-1.00). Heart rate alerts were too few for model development. Machine-learning models can discern clinically relevant peripheral oximetry, blood pressure, and respiratory rate alerts from artifacts in an online monitoring dataset (area under the curve > 0.87).

  20. 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.

  1. 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.

  2. Comparative study on discharge conditions in micro-hole electrical discharge machining of tungsten carbide (WC-Co) material

    Institute of Scientific and Technical Information of China (English)

    Hyun-Seok TAK; Chang-Seung HA; Dong-Hyun KIM; Ho-Jun LEE; Hae-June LEE; Myung-Chang KANG

    2009-01-01

    WC-Co is used widely in die and mold industries due to its unique combination of hardness, strength and wear-resistance. For machining difficult-to-cut materials, such as tungsten carbide, micro-electrical discharge machining(EDM) is one of the most effective methods for making holes because the hardness is not a dominant parameter in EDM. This paper describes the characteristics of the discharge conditions for micro-hole EDM of tungsten carbide with a WC grain size of 0.5μm and Co content of 12%. The EDM process was conducted by varying the condenser and resistance values. A R-C discharge EDM device using arc erosion for micro-hole machining was suggested. Furthermore, the characteristics of the developed micro-EDM were analyzed in terms of the electro-optical observation using an oscilloscope and field emission scanning electron microscope.

  3. Entropy Assessment on Environmental Influence of Condense Heat in Recovery System in Air-Conditioning Refrigerating Machine

    Institute of Scientific and Technical Information of China (English)

    TANG Wen-wu; WANG Han-qing

    2009-01-01

    This paper presented an entropy evaluation method for the influences of condense heat recovery system on the environment.Aiming at the damage of the condense heat to the environment,an entropy of re-source loss and an emission entropy from the condense heat recovery system in the air conditioning refrigerating machine were introduced.For the evaluation of the entropies,we developed a new algorithm for the parameter i-dentifieation.called the composite influence coefficient,based on the Least Squares Support Vector Machine method.By simulation,the numerical experiments shows that the Least Squares Support Vector Machine method is one of the powerful methods for the parameter identification to compute the damage entropy of the condense heat,with the largest training error being-0.025(the relative error being-3.56%),and the biggest test error being 0.015(the relative error being 2.5%).

  4. Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process.Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study.Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

  5. 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.

  6. Development of a CVD diamond Beam Condition Monitor for CMS at the Large Hadron Collider

    CERN Document Server

    Fernández-Hernando, L; Gray, R; Ilgner, C; MacPherson, A; Oh, A; Pritchard, T; Stone, R; Worm, S

    2005-01-01

    The CERN Large Hadron Collider (LHC) will store 2808 bunches per colliding beam, with each bunch consisting of 1011 protons at an energy of 7 TeV. If there is a failure in an element of the accelerator, the resulting beam losses could cause damage not only to the machine but also to the experiments. A Beam Condition Monitor (BCM) is foreseen to monitor fast increments of particle fluxes near the interaction point and, if necessary, to generate an abort signal to the LHC accelerator control to dump the beams. The system is being developed initially for the CMS experiment but it is sufficiently general to find potential applications elsewhere. Due to its high radiation hardness, CVD diamond was chosen for investigation as the BCM sensor. Various samples of CVD diamond have been characterized extensively with both a 90Sr source and in high-intensity test beams in order to assess the capabilities of such sensors and to study whether this detector technology is suitable for a BCM system. A selection of results fro...

  7. Comparison of output-only methods for condition monitoring of industrials systems

    Energy Technology Data Exchange (ETDEWEB)

    Rutten, C; Nguyen, V-H; Golinval, J-C, E-mail: Christophe.Rutten@ulg.ac.be, E-mail: VH.nguven@doct.ulg.ac.be, E-mail: JC.Golinval@ulg.ac.be [University of Liege, Department of Mechanical Engineering, LTAS-Vibrations and Identification of Structures Chemin des Chevreuils 1, 4000 Liege (Belgium)

    2011-07-19

    In the field of structural health monitoring or machine condition monitoring, the activation of nonlinear dynamic behavior complicates the procedure of damage or fault detection. Blind source separation (BSS) techniques are known as efficient methods for damage diagnosis. However, most of BSS techniques repose on the assumption of the linearity of the system and the need of many sensors. This article presents some possible extensions of those techniques that may improve the damage detection, e.g. Enhanced-Principal Component Analysis (EPCA), Kernel PCA (KPCA) and Blind Modal Identification (BMID). The advantages of EPCA rely on its rapidity of use and its reliability. The KPCA method, through the use of nonlinear kernel functions, allows to introduce nonlinear dependences between variables. BMID is adequate to identify and to detect damage for generally damped systems. In this paper, damage is firstly examined by Stochastic Subspace Identification (SSI); then the detection is achieved by comparing subspace features between the reference and a current state through statistics and the concept of subspace angle. Industrial data are used as illustration of the methods.

  8. Condition Monitoring for wind power plants. Structure monitoring and lifetime monitoring of wind power plants (SCMS and LCMS); Condition Monitoring fuer Windenergieanlagen. Strukturmonitoring and Lebensdauerueberwachung von Windenergieanlagen (SCMS and LCMS)

    Energy Technology Data Exchange (ETDEWEB)

    Lange, Holger [P.E. Concepts GmbH, Essen (Germany)

    2010-07-01

    Knowledge about the condition and the remaining lifetime of the structural components of WEPs provides considerable advantages for the manufacturers, owners and insurers. To gain this knowledge, two monitoring systems have been developed, one for the structural condition monitoring and one for the lifetime condition monitoring. Both systems need only little additional measuring expense or none at all, the main part is in the software evaluating the measurement results and parts of the wind and control data. The results of the verification at multi-megawatt wind turbines show that the systems work satisfactorily and that even a sensor-free lifetime monitoring is possible. (orig.)

  9. Noninvasive health condition monitoring device for workers at high altitudes conditions.

    Science.gov (United States)

    Aqueveque, Pablo; Gutierrez, Cristopher; Saavedra, Francisco; Pino, Esteban J

    2016-08-01

    This work presents the design and implementation of a continuous monitoring device to control the health state of workers, for instance miners, at high altitudes. The extreme ambient conditions are harmful for peoples' health; therefore a continuous control of the workers' vital signs is necessary. The developed system includes physiological variables: electrocardiogram (ECG), respiratory activity and body temperature (BT), and ambient variables: ambient temperature (AT) and relative humidity (RH). The noninvasive sensors are incorporated in a t-shirt to deliver a functional device, and maximum comfort to the users. The device is able to continuously calculate heart rate (HR) and respiration rate (RR), and establish a wireless data transmission to a central monitoring station.

  10. 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.

  11. 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.)

  12. Two simple multivariate procedures for monitoring planetary gearboxes in non-stationary operating conditions

    Science.gov (United States)

    Zimroz, Radoslaw; Bartkowiak, Anna

    2013-07-01

    This paper deals with the diagnostics of planetary gearboxes under nonstationary operating conditions. In most diagnostics applications, energy of vibration signals (calculated directly from time series or extracted from spectral representation of signal) is used. Unfortunately energy based features are sensitive to load conditions and it makes diagnostics difficult. In this paper we used energy based 15D data vectors (namely spectral amplitudes of planetary mesh frequency and its harmonics) in order to investigate if it is possible to improve diagnostics efficiency in comparison to previous, one dimensional, approaches proposed for the same problem. Two multivariate methods, Principal Component Analysis (PCA) and Canonical Discriminant Analysis (CDA), were used as techniques for data analysis. We used these techniques in order to investigate dimensionality of the data and to visualize data in 3D and 2D spaces in order to understand data behavior and assess classification ability. As a case study the data from two planetary gearboxes used in complex mining machines (one in bad condition and the other in good condition) were analyzed. For these two machines more than 2000 15D vectors were acquired. It should be noted that due to non-stationarity of loading conditions, previous diagnostics results obtained using other techniques were moderately good (ca. 80% recognition efficiency); however there is still some need to improve diagnostics classification ability. After application of the proposed approaches it was found that the entire data could be reduced to 2 dimensions whereby data instances became visible and a good discriminant function (characterized by a misclassification rate of .0023, i.e. only 5 erroneous classifications for a total of 2183 instances) could be derived. This paper suggests a novel way for condition monitoring of planetary gearboxes based on multivariate statistics. The emphasis is put on the algebraic and geometric interpretations of the PCA

  13. On the Detection of Gap-volt Conditions of Cavity Electric Machining Process%型腔电火花加工间隙电压状态的检测分析

    Institute of Scientific and Technical Information of China (English)

    连芩; 唐一平; 卢秉恒

    2001-01-01

    现代工业控制已进入智能控制阶段,它要求更先进的检测技术作为其支撑技术。本文就型腔电火花加工(EDM)智能控制的检测环节,应用新的检测间隙电压的方法,分析加工过程中间隙电压变化的特征。%Modern industrial control need more advanced monitoring technology, such as intelligent control, for cavity electric machining. We apply the new monitoring technology to the monitoring of the gap conditions of cavity electric machining by analyses of the gap-volt wave feature.

  14. 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.

  15. Operation reliability assessment for cutting tools by applying a proportional covariate model to condition monitoring information.

    Science.gov (United States)

    Cai, Gaigai; Chen, Xuefeng; Li, Bing; Chen, Baojia; He, Zhengjia

    2012-09-25

    The reliability of cutting tools is critical to machining precision and production efficiency. The conventional statistic-based reliability assessment method aims at providing a general and overall estimation of reliability for a large population of identical units under given and fixed conditions. However, it has limited effectiveness in depicting the operational characteristics of a cutting tool. To overcome this limitation, this paper proposes an approach to assess the operation reliability of cutting tools. A proportional covariate model is introduced to construct the relationship between operation reliability and condition monitoring information. The wavelet packet transform and an improved distance evaluation technique are used to extract sensitive features from vibration signals, and a covariate function is constructed based on the proportional covariate model. Ultimately, the failure rate function of the cutting tool being assessed is calculated using the baseline covariate function obtained from a small sample of historical data. Experimental results and a comparative study show that the proposed method is effective for assessing the operation reliability of cutting tools.

  16. Energy autonomous sensor systems for automotive condition monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Fraeulin, Christian [A. RAYMOND GmbH und Co. KG, Weil am Rhein (Germany); Nurnus, Joachim; Punt, Wladimir [Micropelt GmbH, Freiburg (Germany)

    2011-07-01

    With the number of automotive sensors increasing, the effort for connecting all these sensors becomes more and more of an issue. A possible way to overcome these issues is to use energy-autonomous sensors that, besides the basic sensor function, include means to transmit the measurement data wirelessly as well as to generate the electrical energy they need to operate. Generating the electrical energy can be done by harvesting energy from ambient sources that are available in abundance, among others these can be heat and vibration. Although these principles are not new, so far little attempts have been made to incorporate these technologies into cost-sensitive segments like the automotive market. In this paper we present two energy-autonomous sensor demonstrators for automotive applications: a temperature sensor powered with a thermoelectric harvester, thus using a tiny amount of the physical property it wants to measure, and a pressure sensor powered by vibration energy. For both applications, managing the limited amount of available energy is one of the mayor tasks in developing this kind of systems. Therefore both systems use special means in hard- and software to cope with that task. While the automotive market is a very interesting one for energy-autonomous sensors, many other possible applications can be considered, among them the solar market and industrial condition monitoring. (orig.)

  17. 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...

  18. 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.

  19. 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.

  20. 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.

  1. Wear Characterization of Cemented Carbides (WC–CoNi Processed by Laser Surface Texturing under Abrasive Machining Conditions

    Directory of Open Access Journals (Sweden)

    Shiqi Fang

    2017-06-01

    Full Text Available Cemented carbides are outstanding engineering materials widely used in quite demanding material removal applications. In this study, laser surface texturing is implemented for enhancing, at the surface level, the intrinsic bulk-like tribological performance of these materials. In this regard, hexagonal pyramids patterned on the cutting surface of a tungsten cemented carbide grade (WC–CoNi have been successfully introduced by means of laser surface texturing. It simulates the surface topography of conventional honing stones for abrasive application. The laser-produced structure has been tested under abrasive machining conditions with full lubrication. Wear of the structure has been characterized and compared, before and after the abrasive machining test, in terms of changes in geometry aspect and surface integrity. It is found that surface roughness of the machined workpiece was improved by the laser-produced structure. Wear characterization shows that laser treatment did not induce any significant damage to the cemented carbide. During the abrasive machining test, the structure exhibited a high wear resistance. Damage features were only discerned at the contacting surface, whereas geometrical shape of pyramids remained unchanged.

  2. 10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

    Science.gov (United States)

    2010-01-01

    ... 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 PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal...

  3. Investigation of Various Condition Monitoring Techniques Based on a Damaged Wind Turbine Gearbox

    Energy Technology Data Exchange (ETDEWEB)

    Sheng, S.

    2011-10-01

    This paper is a continuation of a 2009 paper presented at the 7th International Workshop on Structural Health Monitoring that described various wind turbine condition-monitoring techniques. This paper presents the results obtained by various condition- monitoring techniques from a damaged Gearbox Reliability Collaborative test gearbox.

  4. 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...

  5. 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 sett...

  6. Monitoring the Monitors: EU Enlargement Conditionality and Minority Protection in the CEECs

    Directory of Open Access Journals (Sweden)

    Gwendolyn Sasse

    2003-04-01

    Full Text Available The issue of minority protection is an extreme case for analyzing the problem of linkage between EU membership conditionality and compliance by candidate countries. while EU law is virtually non-existent, EU practice is divergent, and international standards are ambiguous, the issue has been given high rhetorical prominence by the EU during enlargement. The analysis in this article follows a tracking approach to study the relationship of EU conditionality to changes in minority rights protection in the CEECs. The authors examine how the EU's monitoring process has operated, what its benchmarks have been, how the EU process has interacted with those of other international organizations, such as the Council of Europe and OSCE, and evaluate what its impact has been on the candidate countries. In conclusion, the authors find that EU conditionality is not closely temporally correlated with the emergence of new strategies and laws on minority protection in the CEECs. Instead, the EU's main instrument for accession and convergence, the Regular Reports, have been characterized by ad hocism, inconsistency, and a stress on formal measures rather than substantive evaluation of implementation.

  7. 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.

  8. Quality Monitoring for Laser Welding Based on High-Speed Photography and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Teng Wang

    2017-03-01

    Full Text Available In order to improve the prediction ability of welding quality during high-power disk laser welding, a new approach was proposed and applied in the classification of the dynamic features of metal vapor plume. Six features were extracted through the color image processing method. Three features, including the area of plume, number of spatters, and horizontal coordinate of plume centroid, were selected based on the classification accuracy rates and Pearson product-moment correlation coefficients. A support vector machine model was adopted to classify the welding quality status into two categories, good or poor. The results demonstrated that the support vector machine model established according to the selected features had satisfactory prediction and generalization ability. The classification accuracy rate was higher than 90%, and the model could be applied in the prediction of welding quality during high-power disk laser welding.

  9. Actuation, monitoring and closed-loop control of sewing machine presser foot

    OpenAIRE

    Silva, Luís F.; Lima, Mário; Carvalho, Helder; Rocha, A.M.; Ferreira, Fernando; Monteiro, João L.; Couto, Carlos

    2003-01-01

    Sewing is one of the most important processes in the apparel industry for the production of high-quality garments. Although some research and improvements have been carried out in this area, the sewing process has remained almost unchanged throughout the years, staying largely dependent on the operator skills to set up sewing parameters and to handle the fabrics being sewn. Slight changes in sewing machine settings can influence the quality of seams, as well as sewing operation time. To avoid...

  10. The use of acoustic emission for bearing condition monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Lees, A W; Quiney, Z [Swansea University, Singleton Park, Swansea, SA2 8PP (United Kingdom); Ganji, A; Murray, B, E-mail: a.w.lees@swansea.ac.uk, E-mail: z.quiney.294103@swansea.ac.uk, E-mail: ali.ganji@skf.com [SKF Engineering and Research Centre, Kelvinbaan 16, 3439 MT Nieuwegein (Netherlands)

    2011-07-19

    This paper reports research currently in progress at Swansea University in collaboration with SKF Engineering and Research Centre as part of a continuing investigation into high frequency Acoustic Emission. The primary concerns are experimentally producing subsurface cracks, the type of which would occur in a service failure of a ball bearing, within a steel ball and to closely monitor the properties of this AE from crack initiation to the formation of a ball on the ball surface. It is worth noting that there is evidence that the frequency content of the AE changes during this period, although this has yet to be proved consistent or even fully explained. Conclusive evidence could lead to a system which detects such cracks in a bearing operating in real life conditions, advantageous for many reasons including safety, downtime and maintenance and associated costs. The results from two experimental procedures are presented, one of which loads a single ball held stationary in a test rig to induce subsurface cracks, which are in turn detected by a pair of broadband AE sensors and recorded via a Labview based software system. This approach not only allows detailed analysis of the AE waveforms but also approximate AE source location from the time difference between two sensors. The second experimental procedure details an adaptation of a four-ball lubricant tester in an attempt to produce naturally occurring subsurface cracks from rolling contact whilst minimising the AE arising from surface wear. This thought behind this experiment is reinforced with 3D computational modelling of the rotating system.

  11. Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms

    Science.gov (United States)

    Gangsar, Purushottam; Tiwari, Rajiv

    2017-09-01

    This paper presents an investigation of vibration and current monitoring for effective fault prediction in induction motor (IM) by using multiclass support vector machine (MSVM) algorithms. Failures of IM may occur due to propagation of a mechanical or electrical fault. Hence, for timely detection of these faults, the vibration as well as current signals was acquired after multiple experiments of varying speeds and external torques from an experimental test rig. Here, total ten different fault conditions that frequently encountered in IM (four mechanical fault, five electrical fault conditions and one no defect condition) have been considered. In the case of stator winding fault, and phase unbalance and single phasing fault, different level of severity were also considered for the prediction. In this study, the identification has been performed of the mechanical and electrical faults, individually and collectively. Fault predictions have been performed using vibration signal alone, current signal alone and vibration-current signal concurrently. The one-versus-one MSVM has been trained at various operating conditions of IM using the radial basis function (RBF) kernel and tested for same conditions, which gives the result in the form of percentage fault prediction. The prediction performance is investigated for the wide range of RBF kernel parameter, i.e. gamma, and selected the best result for one optimal value of gamma for each case. Fault predictions has been performed and investigated for the wide range of operational speeds of the IM as well as external torques on the IM.

  12. 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

  13. 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

  14. 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

  15. BIRD COMMUNITIES AND HABITAT AS ECOLOGICAL INDICATORS OF FOREST CONDITION IN REGIONAL MONITORING

    Science.gov (United States)

    Ecological indicators for long-term monitoring programs are needed to detect and assess changing environmental conditions, We developed and tested community-level environmental indicators for monitoring forest bird populations and associated habitat. We surveyed 197 sampling plo...

  16. Different Condition Monitoring Approaches for Main Shafts of Offshore Wind Turbines

    DEFF Research Database (Denmark)

    Ambühl, Simon; Sørensen, John Dalsgaard

    the applicability of different condition monitoring techniques like performance monitoring, strain gauge results and vibration analysis for crack detection on the low speed shaft. Different signal processing methods like descriptive statistics, Fourier Transforms, Wavelet transforms, Modal Assurance Criteria...

  17. 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.

  18. 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.

  19. Monitoring the condition of natural resources in US national parks.

    Science.gov (United States)

    Fancy, S G; Gross, J E; Carter, S L

    2009-04-01

    The National Park Service has developed a long-term ecological monitoring program for 32 ecoregional networks containing more than 270 parks with significant natural resources. The monitoring program assists park managers in developing a broad-based understanding of the status and trends of park resources as a basis for making decisions and working with other agencies and the public for the long-term protection of park ecosystems. We found that the basic steps involved in planning and designing a long-term ecological monitoring program were the same for a range of ecological systems including coral reefs, deserts, arctic tundra, prairie grasslands, caves, and tropical rainforests. These steps involve (1) clearly defining goals and objectives, (2) compiling and summarizing existing information, (3) developing conceptual models, (4) prioritizing and selecting indicators, (5) developing an overall sampling design, (6) developing monitoring protocols, and (7) establishing data management, analysis, and reporting procedures. The broad-based, scientifically sound information obtained through this systems-based monitoring program will have multiple applications for management decision-making, research, education, and promoting public understanding of park resources. When combined with an effective education program, monitoring results can contribute not only to park issues, but also to larger quality-of-life issues that affect surrounding communities and can contribute significantly to the environmental health of the nation.

  20. Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms.

    Science.gov (United States)

    Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly A

    2013-02-15

    High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation.

  1. Heat production in the windings of the stators of electric machines under stationary condition

    Science.gov (United States)

    Alebouyeh Samami, Behzad; Pieper, Martin; Breitbach, Gerd; Hodapp, Josef

    2014-12-01

    In electric machines due to high currents and resistive losses (joule heating) heat is produced. To avoid damages by overheating the design of effective cooling systems is required. Therefore the knowledge of heat sources and heat transfer processes is necessary. The purpose of this paper is to illustrate a good and effective calculation method for the temperature analysis based on homogenization techniques. These methods have been applied for the stator windings in a slot of an electric machine consisting of copper wires and resin. The key quantity here is an effective thermal conductivity, which characterizes the heterogeneous wire resin-arrangement inside the stator slot. To illustrate the applicability of the method, the analysis of a simplified, homogenized model is compared with the detailed analysis of temperature behavior inside a slot of an electric machine according to the heat generation. We considered here only the stationary situation. The achieved numerical results are accurate and show that the applied homogenization technique works in practice. Finally the results of simulations for the two cases, the original model of the slot and the homogenized model chosen for the slot (unit cell), are compared to experimental results.

  2. Research on Network-based Integrated Condition Monitoring Unit for Rotating Machinery

    Institute of Scientific and Technical Information of China (English)

    XI Xiao-peng; ZHANG Wen-rui; XI Shuan-min; JING Min-qing; YU Lie

    2004-01-01

    In this paper, a network-based monitoring unit for condition monitoring and fault diagnosis of rotating machinery is designed and implemented. With the technology of DSP( Digital signal processing), TCP/IP, and simultaneous acquisition, a mechanism of multi-process and inter-process communication, the integrating problem of signal acquisition, the data dynamic management and network-based configuration in the embedded condition monitoring system is solved. It offers the input function of monitoring information for network-based condition monitoring and a fault diagnosis system.

  3. 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.

  4. Design of a Human Machine Interface for a Reliability Monitoring System of Nuclear Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Yuxin; Yang, Ming; Yan, Shengyuan [Harbin Engineering University, Harbin (Switzerland)

    2014-08-15

    This paper presents the design of a Reliability Monitoring System (RMS) for nuclear power plant which was newly developed by authors. The RMS, combining a dynamic reliability analysis system with an online fault management system, can assist technical personnel with their daily tasks such as system configuration management, maintenance plan making and state monitoring from a point of view that degradation of equipment performance, bad plant configurations and incorrect actions will decrease the probability of systems performing designed functions. For utilizing the RMS, technical personnel will monitor the plant operational state, conform the fault diagnosis, input the intended operating or maintenance actions, and monitor the probability changes of systems through a graphical HMI. A HMI evaluation experiment was conducted using an eye tracking system and a full scale simulator of PWR NPP. Visual data was recorded and analyzed after the experiment. The experiment results are presented and the HMI design problems revealed by the evaluation experiment are discussed.

  5. LMD method and multi-class RWSVM of fault diagnosis for rotating machinery using condition monitoring information.

    Science.gov (United States)

    Liu, Zhiwen; Chen, Xuefeng; He, Zhengjia; Shen, Zhongjie

    2013-07-05

    Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. First, the sensor-based vibration signals measured from the rotating machinery are preprocessed by the LMD method and product functions (PFs) are produced. Second, statistic features are extracted to acquire more fault characteristic information from the sensitive PF. Finally, these features are fed into a multi-class RWSVM to identify the rotating machinery health conditions. The experimental results validate the effectiveness of the proposed RWSVM method in identifying rotating machinery fault patterns accurately and effectively and its superiority over that based on the general SVM.

  6. LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information

    Directory of Open Access Journals (Sweden)

    Zhongjie Shen

    2013-07-01

    Full Text Available Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD and multi-class reproducing wavelet support vector machines (RWSVM, which is applied to diagnose rotating machinery faults. First, the sensor-based vibration signals measured from the rotating machinery are preprocessed by the LMD method and product functions (PFs are produced. Second, statistic features are extracted to acquire more fault characteristic information from the sensitive PF. Finally, these features are fed into a multi-class RWSVM to identify the rotating machinery health conditions. The experimental results validate the effectiveness of the proposed RWSVM method in identifying rotating machinery fault patterns accurately and effectively and its superiority over that based on the general SVM.

  7. 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.

  8. [Monitoring and conditioning in plastic and reconstructive ENT-surgery].

    Science.gov (United States)

    Dacho, A; Dietz, A

    2006-11-01

    Plastic and reconstructive ENT surgery serves for reconstruction of form and function. Frequent indications in ENT surgery are the covering of large tissue defects after tumor operations, firing and/or explosion injuries, accidents, burns or massive infections. A high revision rate of up to 20 % in selective patient groups show that more knowledge of both monitoring and ischemia-/reperfusion mechanisms is necessary. Besides improved monitor proceedings biochemical cell procedures in pedicled and free flaps are getting more focused. In the last years certain physical and medical factors appear, which have influence on the long-term surviving of a pedicled or free flap, e. g. pre- and/or postconditioning. The increasing knowledge of changes in perfusion and oxygenation, which prevail in the flap, as well as different options of physical and pharmacological therapies permit a promising view into the future, in order to achieve an improved surviving of a pedicled or free flap in combination with improved monitor proceedings.

  9. 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.

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

    Directory of Open Access Journals (Sweden)

    Yoshio eSakurai

    2014-02-01

    Full Text Available 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.

  11. 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.

  12. MONITORING CONDITION OF ECONOMIC SECURITY OF REPUBLIC MOLDOVA

    Directory of Open Access Journals (Sweden)

    E.V. Bicova

    2009-12-01

    Full Text Available The system of indicators of the economic security is described at research of questions of energy security of Republic Moldova in the article. Results of monitoring of indicators and also a final estimation of the level of economic security (preliminary are resulted.

  13. Robot dispatching Scenario for Accident Condition Monitoring of NPP

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jongseog [Central Research Institute of Korea Hydro and Nuclear Power Co., Daejeon (Korea, Republic of)

    2013-05-15

    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.

  14. 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.

  15. A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals.

    Science.gov (United States)

    Zhang, Qingxue; Zhou, Dian; Zeng, Xuan

    2016-11-01

    This paper proposes a novel machine learning-enabled framework to robustly monitor the instantaneous heart rate (IHR) from wrist-electrocardiography (ECG) signals continuously and heavily corrupted by random motion artifacts in wearable applications. The framework includes two stages, i.e. heartbeat identification and refinement, respectively. In the first stage, an adaptive threshold-based auto-segmentation approach is proposed to select out heartbeat candidates, including the real heartbeats and large amounts of motion-artifact-induced interferential spikes. Then twenty-six features are extracted for each candidate in time, spatial, frequency and statistical domains, and evaluated by a spare support vector machine (SVM) to select out ten critical features which can effectively reveal residual heartbeat information. Afterwards, an SVM model, created on the training data using the selected feature set, is applied to find high confident heartbeats from a large number of candidates in the testing data. In the second stage, the SVM classification results are further refined by two steps: (1) a rule-based classifier with two attributes named 'continuity check' and 'locality check' for outlier (false positives) removal, and (2) a heartbeat interpolation strategy for missing-heartbeat (false negatives) recovery. The framework is evaluated on a wrist-ECG dataset acquired by a semi-customized platform and also a public dataset. When the signal-to-noise ratio is as low as  -7 dB, the mean absolute error of the estimated IHR is 1.4 beats per minute (BPM) and the root mean square error is 6.5 BPM. The proposed framework greatly outperforms well-established approaches, demonstrating that it can effectively identify the heartbeats from ECG signals continuously corrupted by intense motion artifacts and robustly estimate the IHR. This study is expected to contribute to robust long-term wearable IHR monitoring for pervasive heart health and fitness management.

  16. 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...

  17. 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.

  18. 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 f...

  19. Wastewater quality monitoring system using sensor fusion and machine learning techniques.

    Science.gov (United States)

    Qin, Xusong; Gao, Furong; Chen, Guohua

    2012-03-15

    A multi-sensor water quality monitoring system incorporating an UV/Vis spectrometer and a turbidimeter was used to monitor the Chemical Oxygen Demand (COD), Total Suspended Solids (TSS) and Oil & Grease (O&G) concentrations of the effluents from the Chinese restaurant on campus and an electrocoagulation-electroflotation (EC-EF) pilot plant. In order to handle the noise and information unbalance in the fused UV/Vis spectra and turbidity measurements during the calibration model building, an improved boosting method, Boosting-Iterative Predictor Weighting-Partial Least Squares (Boosting-IPW-PLS), was developed in the present study. The Boosting-IPW-PLS method incorporates IPW into boosting scheme to suppress the quality-irrelevant variables by assigning small weights, and builds up the models for the wastewater quality predictions based on the weighted variables. The monitoring system was tested in the field with satisfactory results, underlying the potential of this technique for the online monitoring of water quality. Copyright © 2011 Elsevier Ltd. All rights reserved.

  20. 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.

  1. Putting Man in the Machine: Exploiting Expertise to Enhance Multiobjective Design of Water Supply Monitoring Network

    Science.gov (United States)

    Bode, F.; Nowak, W.; Reed, P. M.; Reuschen, S.

    2016-12-01

    Drinking-water well catchments need effective early-warning monitoring networks. Groundwater water supply wells in complex urban environments are in close proximity to a myriad of potential industrial pollutant sources that could irreversibly damage their source aquifers. These urban environments pose fiscal and physical challenges to designing monitoring networks. Ideal early-warning monitoring networks would satisfy three objectives: to detect (1) all potential contaminations within the catchment (2) as early as possible before they reach the pumping wells, (3) while minimizing costs. Obviously, the ideal case is nonexistent, so we search for tradeoffs using multiobjective optimization. The challenge of this optimization problem is the high number of potential monitoring-well positions (the search space) and the non-linearity of the underlying groundwater flow-and-transport problem. This study evaluates (1) different ways to effectively restrict the search space in an efficient way, with and without expert knowledge, (2) different methods to represent the search space during the optimization and (3) the influence of incremental increases in uncertainty in the system. Conductivity, regional flow direction and potential source locations are explored as key uncertainties. We show the need and the benefit of our methods by comparing optimized monitoring networks for different uncertainty levels with networks that seek to effectively exploit expert knowledge. The study's main contributions are the different approaches restricting and representing the search space. The restriction algorithms are based on a point-wise comparison of decision elements of the search space. The representation of the search space can be either binary or continuous. For both cases, the search space must be adjusted properly. Our results show the benefits and drawbacks of binary versus continuous search space representations and the high potential of automated search space restriction

  2. 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.

  3. 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.

  4. 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...

  5. INFORMATION SYSTEMS AND PROCESSES OF MONITORING POWER TRANSFORMERS CONDITION

    Directory of Open Access Journals (Sweden)

    Litvinov V. N.

    2016-02-01

    Full Text Available To solve the problem of reducing the power supply system’s reliability a prompt full-scale diagnostics based on modern methods can help. Inculcation of information systems for the operational diagnostics implementation allows providing the operating personnel with information that enables to predict possible infringements in power transformers work and to prepare in advance an action plan to address them. The paper presents fragments of the developed monitoring system of power transformer using programmable logic controllers. Within the work of the system there were marked such groups of controlled parameters as information about temperature and the cooling system work; magnitude of windings voltage per phase; the windings current values per phase; information about being transmitted and transmitted power; information about the insulation state. There is designed a functional scheme of the system for monitoring the state of the power transformer. There is described a general algorithm of system functioning. There is developed graphical operator interface that allows to monitor the object state and to manage the system state. Using XML markup language there was designed format of data packets. Designed hardware and software package can be used in the educational process, as it allows to improve the quality of students training, to bring them closer to the realities of modern professional activities; in operational activities as complying with the approved domestic calculating methods replacement of foreign software; in science in solving problems of analysis and optimization of operating parameters of power transformers

  6. Monitoring Re-execution Condition of Continuous Action Step in Computerized Procedure System

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Yun Goo; Lee, Sung Jin [KHNP Co., Daejeon (Korea, Republic of)

    2010-05-15

    The APR1400 digital main control room (MCR) has many advanced features of computerized control room. One of the most important improvements is the Computerized Procedure System (CPS). Emergency operating procedure (EOP) in the Nuclear Power Plant (NPP) provides a series of instructions to MCR operators to cope with design base events. Computerized EOP supports the operator in terms of plant monitoring, decision making, and control access. Continuous Action Step (CAS) in EOP should be monitored through the entire procedure execution when plant processes are disturbed under emergency conditions. CPS can monitor CAS re-execution condition during EOP execution. CPS has functions to monitor CAS re-execution condition

  7. Different Condition Monitoring Approaches for Main Shafts of Offshore Wind Turbines

    DEFF Research Database (Denmark)

    Ambühl, Simon; Sørensen, John Dalsgaard

    Condition monitoring can be used to detect faults and failures at an early stage. Thus it decreases the overall maintenance expenses. This report gives an example of condition monitoring with focus on early crack detection in the main shaft of an offshore wind turbine. This article discusses...... the applicability of different condition monitoring techniques like performance monitoring, strain gauge results and vibration analysis for crack detection on the low speed shaft. Different signal processing methods like descriptive statistics, Fourier Transforms, Wavelet transforms, Modal Assurance Criteria...

  8. 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...

  9. APPLICATION OF INFORMATION TECHNOLOGIES FOR MONITORING BRIDGEWORK CONDITIONS

    Directory of Open Access Journals (Sweden)

    D. E. Gusev

    2008-01-01

    Full Text Available The paper considers а variant of database technologies’ application in transport communication sphere, particularly, for introduction of integrated methodology for evaluation of technical and operational conditions of bridgeworks on motor roads of general usage. Information technologies’ application helps to prevent emergency and pre-emergency conditions of bridgeworks and provides optimal investment allocation in the sphere of transport communication. 

  10. Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multi-signal Vital Sign Monitoring Data

    Science.gov (United States)

    Chen, Lujie; Dubrawski, Artur; Wang, Donghan; Fiterau, Madalina; Guillame-Bert, Mathieu; Bose, Eliezer; Kaynar, Ata M.; Wallace, David J.; Guttendorf, Jane; Clermont, Gilles; Pinsky, Michael R.; Hravnak, Marilyn

    2015-01-01

    OBJECTIVE Use machine-learning (ML) algorithms to classify alerts as real or artifacts in online noninvasive vital sign (VS) data streams to reduce alarm fatigue and missed true instability. METHODS Using a 24-bed trauma step-down unit’s non-invasive VS monitoring data (heart rate [HR], respiratory rate [RR], peripheral oximetry [SpO2]) recorded at 1/20Hz, and noninvasive oscillometric blood pressure [BP] less frequently, we partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were VS deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained ML algorithms. The best model was evaluated on alerts in the test set to enact online alert classification as signals evolve over time. MAIN RESULTS The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve (AUC) performance of 0.79 (95% CI 0.67-0.93) for SpO2 at the instant the VS first crossed threshold and increased to 0.87 (95% CI 0.71-0.95) at 3 minutes into the alerting period. BP AUC started at 0.77 (95%CI 0.64-0.95) and increased to 0.87 (95% CI 0.71-0.98), while RR AUC started at 0.85 (95%CI 0.77-0.95) and increased to 0.97 (95% CI 0.94–1.00). HR alerts were too few for model development. CONCLUSIONS ML models can discern clinically relevant SpO2, BP and RR alerts from artifacts in an online monitoring dataset (AUC>0.87). PMID:26992068

  11. Reliability Improvement of Power Converters by Means of Condition Monitoring of IGBT Modules

    DEFF Research Database (Denmark)

    Choi, Ui Min; Blaabjerg, Frede; Jørgensen, Søren

    2017-01-01

    proposes a condition monitoring method of insulated-gate bipolar transistor (IGBT) modules. In the first section of this paper, a structure of a conventional IGBT module and a related parameter for the condition monitoring are explained. Then, a proposed real-time on-state collector-emitter voltage...

  12. A Comparison of Advanced Techniques for Monitoring the Condition of Machinery

    NARCIS (Netherlands)

    Maas, H.L.M.M.; Meiler, P.P.; Grimmelius, H.T.

    1998-01-01

    Within the project described in this paper, the objective of condition monitoring is to detect (upcoming) failures as early as possible, to minimise damage to the machinery. The involvement of humans within the condition monitoring process is being reduced by incorporation of advanced and intelligen

  13. 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.

  14. Did Nongovernmental Monitoring improve Working Conditions in the case of Nike and the Footwear Industry?

    OpenAIRE

    FERDOUS AHAMED, Ph.D

    2013-01-01

    This article examines working conditions in the RMG sector of Bangladesh could improve through effective monitoring system. In a significant case Nike suggested that working conditions and labour rights can be improved through a systematic approach and a comprehensive and transparent monitoring system. External pressure from NGOs and other advocacy groups motivated Nike to introduce a Code of Conduct and a monitoring system. The process is discussed in this section. Conclusion: In conclusion,...

  15. 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.

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

    Science.gov (United States)

    2017-07-18

    AMD X86 c. ARM v9. The Visual Management Interface ( VMI ) provided by x can differ. Some possibilities are: a. x provides a security management... VMI that allows security administrator to author security policy, manage network security and connectivity, and monitor VM resource consumption. b...The x security management VMI provides default security policy and templates with example configurations. c. There is no VMI ; the x security policy

  17. 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

    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...... 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...

  18. 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

  19. 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.

  20. Multi-component machine monitoring and fault diagnosis using blind source separation and advanced vibration analysis

    Science.gov (United States)

    Mahvash Mohammadi, Ali

    In this dissertation, two approaches are studied for the case of bearing anomaly detection. One approach is to regard it as a blind source separation (cocktail party) problem and take advantage of statistical and mathematical methods developed for this purpose, primarily independent component analysis (ICA), to separate signals coming from different sources. The other approach is to avoid making the effort to 'separate' the signals and relate them to different components (sources) and instead make use of the specification and characteristics of vibration signals produced by the different components in normal and faulty conditions. In the first approach, a common difficulty with applying blind source separation techniques (or, in general any mathematical methods) to separation of vibration sources is that no standard measure exists to assess the quality of separation and validate the results. In fact, for an ideal assessment the true original signals produced by each component must be available as a prerequisite. This requires gathering signals from each component in strict isolation during operation in a lab environment which, if not impossible, is very costly and difficult. To alleviate this difficulty, a novel method is developed that presents the distribution of vibration energy with regard to the respective locations of vibration sources and sensors, and takes into consideration the mechanical attributes of the structure. This method uses some key concepts from statistical energy analysis (SEA) to support the fact that each sensor collects a different version of the oscillations produced in the system with respect to its location in the system. Therefore, by comparing the spectral signature of the vibration signals and making use of a priori knowledge of the spatial distribution of sensors and components, a schematic representation of the spectral signature of the vibration sources are obtained. This method is verified using a series of experiments with

  1. Optoelectronic methods in potential application in monitoring of environmental conditions

    Science.gov (United States)

    Mularczyk-Oliwa, Monika; Bombalska, Aneta; Kwaśny, Mirosław; Kopczyński, Krzysztof; Włodarski, Maksymilian; Kaliszewski, Miron; Kostecki, Jerzy

    2016-12-01

    Allergic rhinitis, also known as hay fever is a type of inflammation which occurs when the immune system overreacts to allergens in the air. It became the most common disease among people. It became important to monitor air content for the presence of a particular type of allergen. For the purposes of environmental monitoring there is a need to widen the group of traditional methods of identification of pollen for faster and more accurate research systems. The aim of the work was the characterization and classification of certain types of plant pollens by using laser optical methods, which were supported by the chemmometrics. Several species of pollen were examined, for which a database of spectral characteristics was created, using LIF, Raman scattering and FTIR methods. Spectral database contains characteristics of both common allergens and pollen of minor importance. Based on registered spectra, statistical analysis was made, which allows the classification of the tested pollen species. For the study of the emission spectra Nd:YAG laser was used with the fourth harmonic generation (266 nm) and GaN diode laser (375 nm). For Raman scattering spectra spectrometer Nicolet IS-50 with a excitation wavelength of 1064 nm was used. The FTIR spectra, recorded in the mid infrared1 range (4000-650 cm-1) were collected with use of transmission mode (KBr pellet), ATR and DRIFT.

  2. Engine Oil Condition Monitoring Using High Temperature Integrated Ultrasonic Transducers

    Directory of Open Access Journals (Sweden)

    Jeff Bird

    2011-01-01

    Full Text Available The present work contains two parts. In the first part, high temperature integrated ultrasonic transducers (IUTs made of thick piezoelectric composite films, were coated directly onto lubricant oil supply and sump lines of a modified CF700 turbojet engine. These piezoelectric films were fabricated using a sol-gel spray technology. By operating these IUTs in transmission mode, the amplitude and velocity of transmitted ultrasonic waves across the flow channel of the lubricant oil in supply and sump lines were measured during engine operation. Results have shown that the amplitude of the ultrasonic waves is sensitive to the presence of air bubbles in the oil and that the ultrasound velocity is linearly dependent on oil temperature. In the second part of the work, the sensitivity of ultrasound to engine lubricant oil degradation was investigated by using an ultrasonically equipped and thermally-controlled laboratory testing cell and lubricant oils of different grades. The results have shown that at a given temperature, ultrasound velocity decreases with a decrease in oil viscosity. Based on the results obtained in both parts of the study, ultrasound velocity measurement is proposed for monitoring oil degradation and transient oil temperature variation, whereas ultrasound amplitude measurement is proposed for monitoring air bubble content.

  3. A Development of Empirical Models for Equipment Condition Monitoring System

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Song Kyu; Baik, Se Jin [KEPCO Engineering and Construction Company, Daejeon (Korea, Republic of); An, Sang Ha [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of)

    2010-10-15

    A great deal of effort is recently put into on-line monitoring (OLM), specially using empirical model to detect earlier the fault of components or the calibration reduction/extension of instrument. The empirical model is constructed with historical data obtained during operation and it mainly relies on regression techniques. Various models are used in OLM and the role of models is to describe the relation among signals that have been collected. Ultimate goal of empirical models is to best estimate parameter as soon as possible close to actual value. Typically some of the historical data are used for model training, and some data are used for verification and assessment of model performance. Several different models for OLM of nuclear power systems are currently being used. Examples include the ANL Multivariate State Estimation Techniques (MSET) used in EPI center of SmartSignal, the expert state estimation engine (ESEE) used in SureSense software of Expert Microsystems, Process Evaluation and Analysis by Neural Operators (PEANO) OECD of Halden Reactor Project and linear regression model used in RCP seal integrity monitoring system (SIMON) of KEPCO E and C

  4. High temperature integrated ultrasonic transducers for engine condition monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Kobayashi, M.; Jen, C.K. [National Research Council of Canada, Boucherville, PQ (Canada). Industrial Materials Inst.; Wu, K.T. [McGill Univ., Montreal, PQ (Canada). Dept. of Electrical and Computer Engineering; Bird, J.; Galeote, B. [National Research Council of Canada, Ottawa, ON (Canada). Inst. for Aerospace Research; Mrad, N. [Department of National Defence, Ottawa, ON (Canada). Air Vehicles Research Station

    2009-07-01

    Piezoelectric ultrasonic transducers (UTs) are used for real-time, in-situ or off-line nondestructive evaluation (NDE) of large metallic structures such as airplanes, automobiles, ships, pressure vessels and pipelines because of their subsurface inspection capability, fast inspection speed, simplicity and cost-effectiveness. The objective of this study was to develop and evaluate effective integrated ultrasonic transducers (IUT) technology to perform non-intrusive engine NDE and structural health monitoring (SHM). High temperature IUTs made of bismuth titanate piezoelectric film greater than 50 {mu}m in thickness were coated directly onto a modified CF700 turbojet engine outer casing, oil sump and supply lines and gaskets using sol-gel spray technology. The assessment was limited to temperatures up to 500 degrees C. The center frequencies of the IUTs were approximately 10 to 17 MHz. Ultrasonic signals obtained in pulse/echo measurements were excellent. High temperature ultrasonic performance will likely be obtained in the transmission mode as well. The potential applications of the developed IUTs include non-intrusive real-time temperature, lubricant oil quality and metal debris monitoring within a turbojet engine environment. 9 refs., 13 figs.

  5. 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

  6. 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.

  7. Measurements of the luminosity and normalised beam-induced background using the CMS Fast Beam Condition Monitor

    CERN Document Server

    Odell, Nathaniel Jay

    2012-01-01

    The CMS Beam Conditions and Radiation Monitoring system (BRM) is installed to protect the CMS detector from high beam losses and to provide feedback to the LHC and CMS on the beam conditions. The Fast Beam Condition Monitor (BCM1F), one of the sub-detectors in the BRM system, is installed inside the pixel volume close to the beam pipe and consists of two planes of 4 modules each located 1.8 m away from the IP, on both ends. It uses single-crystal CVD diamond sensors, radiation hard front-end electronics and an optical transmission of the signal. It is designed for single particle rate measurements, detecting both machine induced beam background and collision products on a bunch-by-bunch basis. Presented is the implementation of the normalized online beam-induced background measurement and the online instantaneous luminosity measurement. The method for determining the luminosity from the measured rates, including the absolute calibration using the Van der Meer scan, and the measurement performance will be d...

  8. Structural Health Monitoring in Changing Operational Conditions Using Tranmissibility Measurements

    Directory of Open Access Journals (Sweden)

    Christof Devriendt

    2010-01-01

    Full Text Available This article uses frequency domain transmissibility functions for detecting and locating damage in operational conditions. In recent articles numerical and experimental examples were presented and the possibility to use the transmissibility concept for damage detection seemed quite promising. In the work discussed so far, it was assumed that the operational conditions were constant, the structure was excited by a single input in a fixed location. Transmissibility functions, defined as a simple ratio between two measured responses, do depend on the amplitudes or locations of the operational forces. The current techniques fail in the case of changing operational conditions. A suitable operational damage detection method should however be able to detect damage in a very early stage even in the case of changing operational conditions. It will be demonstrated in this paper that, by using only a small frequency band around the resonance frequencies of the structure, the existing methods can still be used in a more robust way. The idea is based on the specific property that the transmissibility functions become independent of the loading condition in the system poles. A numerical and experimental validation will be given.

  9. A high sensitivity wear debris sensor using ferrite cores for online oil condition monitoring

    Science.gov (United States)

    Zhu, Xiaoliang; Zhong, Chong; Zhe, Jiang

    2017-07-01

    Detecting wear debris and measuring the increasing number of wear debris in lubrication oil can indicate abnormal machine wear well ahead of machine failure, and thus are indispensable for online machine health monitoring. A portable wear debris sensor with ferrite cores for online monitoring is presented. The sensor detects wear debris by measuring the inductance change of two planar coils wound around a pair of ferrite cores that make the magnetic flux denser and more uniform in the sensing channel, thereby improving the sensitivity of the sensor. Static testing results showed this wear debris sensor is capable of detecting 11 µm and 50 µm ferrous debris in 1 mm and 7 mm diameter fluidic pipes, respectively; such a high sensitivity has not been achieved before. Furthermore, a synchronized sampling method was also applied to reduce the data size and realize real-time data processing. Dynamic testing results demonstrated that the sensor is capable of detecting wear debris in real time with a high throughput of 750 ml min-1 the measured debris concentration is in good agreement with the actual concentration.

  10. Machine Learning

    CERN Document Server

    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.

  11. 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.

  12. Supervised and unsupervised condition monitoring of non-stationary acoustic emission signals

    DEFF Research Database (Denmark)

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

    2005-01-01

    We are pursuing a system that monitors the engine condition under multiple load settings, i.e. under non-stationary operating conditions. The running speed when data acquired under simulated marine conditions (different load settings on the propeller curve) was in the range from approximately 70...... approaches perform well, which indicates that unsupervised models, modelled without faulty data, may be used for accurate condition monitoring....... condition changes across load changes. In this paper we approach this load interpolation problem with supervised and unsupervised learning, i.e. model with normal and fault examples and normal examples only, respectively. We apply non-linear methods for the learning of engine condition changes. Both...

  13. Fundamentals for remote condition monitoring of offshore wind turbines

    DEFF Research Database (Denmark)

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

    mobile sensors), fibre optics (including a new microbend transducer design and various Bragg-grating based applications), wireless approaches involving both battery and energy harvesting options, and inertia sensor based system identification approaches able to deal with linear periodic systems......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...

  14. GROUNDWATER MONITORING: Statistical Methods for Testing Special Background Conditions

    Energy Technology Data Exchange (ETDEWEB)

    Chou, Charissa J.

    2004-04-28

    This chapter illustrates application of a powerful intra-well testing method referred as the combined Shewhart-CUSUM control chart approach, which can detect abrupt and gradual changes in groundwater parameter concentrations. This method is broadly applicable to groundwater monitoring situations where there is no clearly defined upgradient well or wells, where spatial variability exists in parameter concentrations, or when groundwater flow rate is extremely slow. Procedures for determining the minimum time needed to acquire independent groundwater samples and useful transformations for obtaining normally distributed data are also provided. The control chart method will be insensitive to detect real changes if a preexisting trend is observed in the background data set. A method and a case study describing how a trend observed in a background data set can be removed using a transformation suggested by Gibbons (1994) are presented to illustrate treatment of a preexisting trend.

  15. 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.

  16. MERGING OPTIMALITY CONDITIONS WITH GENETIC ALGORITHM OPERATORS TO SOLVE SINGLE MACHINE TOTAL WEIGHTED TARDINESS PROBLEM

    Institute of Scientific and Technical Information of China (English)

    Ibrahim M.AL-HARKAN

    2005-01-01

    In this paper, a constrained genetic algorithm (CGA) is proposed to solve the single machine total weighted tardiness problem. The proposed CGA incorporates dominance rules for the problem under consideration into the GA operators. This incorporation should enable the proposed CGA to obtain close to optimal solutions with much less deviation and much less computational effort than the conventional GA (UGA). Several experiments were performed to compare the quality of solutions obtained by the three versions of both the CGA and the UGA with the results obtained by a dynamic programming approach. The computational results showed that the CGA was better than the UGA in both quality of solutions obtained and the CPU time needed to obtain the close to optimal solutions.The three versions of the CGA reduced the percentage deviation by 15.6%, 61.95%, and 25% respectively and obtained close to optimal solutions with 59% lower CPU time than what the three versions of the UGA demanded. The CGA performed better than the UGA in terms of quality of solutions and computational effort when the population size and the number of generations are smaller.

  17. Theoretical analysis of the conditions for increasing the accuracy of the axial hole machining multiple tip tools

    Directory of Open Access Journals (Sweden)

    Т. М. Брижан

    2015-03-01

    Full Text Available This paper deals with the development of a mathematical model for determining the value of the elastic displacement that occurs when reaming and deployment of holes. The lack of theoretical studies of the analytical representation of the cutting force limits the study of conditions to improve the accuracy of hole machining. A promising direction should be considered a theoretical approach, which allows to determine the magnitude of the elastic displacement occurring in the technological system when drilling and thus to estimate the error handling holes. A further development of this approach is a theoretical analysis of the magnitude of the elastic displacement that occurs when processing multiple tip tools axial holes (core drills and reamers. In this paper, based on the analytic representation of cutting forces in machining holes axial multiple tip tools have received a new theoretical solution of the nature of changes in the magnitude of the elastic displacement occurring in the technological system and determining error processing holes. Calculations revealed, that in the case of misalignment of the hole axis tool with four or more blades magnitude of the elastic displacement remains constant, regardless of the position of the blades. However, the torque varies cyclically, giving rise to a torsional oscillation process system. Proved, that with increasing amounts of the variable part of the tool blades torque decreases. It follows from this promising application of axial multiple tip tools (reamers, reamers with inclined blades, so as to avoid the time variation of torque and thus eliminate torsional vibrations in the technological system, that is essential for improving the accuracy of hole machining and surface finish class

  18. Intelligent pump drives. Simulation, condition monitoring, fault diagnosis and energy efficiency; Intelligente Pumpenantriebe. Simulation, Condition Monitoring, Fehlerdiagnose und Energieeffizienz

    Energy Technology Data Exchange (ETDEWEB)

    Kleinmann, Stefan [Allweiler AG, Radolfzell (Germany); Leonardo, Domenico; Koller-Hodac, Agathe [Hochschule fuer Technik Rapperswil (Switzerland)

    2011-07-01

    The authors of the contribution under consideration report on an implementation of a simulation environment and a fault diagnostic system for an oil burner application. Using a modification of the application hardware, an additional increase in efficiency in an advanced control of pump drives is achieved. The properties of the combustion process are not affected adversely. All changes to the system can be investigated in simulations for feasibility and impact. Using the simulation model, a diagnostic system is brought up enabling a remote monitoring for example.

  19. A remote condition monitoring system for wind-turbine based DG systems

    Science.gov (United States)

    Ma, X.; Wang, G.; Cross, P.; Zhang, X.

    2012-05-01

    In this paper, a remote condition monitoring system is proposed, which fundamentally consists of real-time monitoring modules on the plant side, a remote support centre and the communications between them. The paper addresses some of the key issues related on the monitoring system, including i) the implementation and configuration of a VPN connection, ii) an effective database system to be able to handle huge amount of monitoring data, and iii) efficient data mining techniques to convert raw data into useful information for plant assessment. The preliminary results have demonstrated that the proposed system is practically feasible and can be deployed to monitor the emerging new energy generation systems.

  20. Planetary gearbox condition monitoring of ship-based satellite communication antennas using ensemble multiwavelet analysis method

    Science.gov (United States)

    Chen, Jinglong; Zhang, Chunlin; Zhang, Xiaoyan; Zi, Yanyang; He, Shuilong; Yang, Zhe

    2015-03-01

    Satellite communication antennas are key devices of a measurement ship to support voice, data, fax and video integration services. Condition monitoring of mechanical equipment from the vibration measurement data is significant for guaranteeing safe operation and avoiding the unscheduled breakdown. So, condition monitoring system for ship-based satellite communication antennas is designed and developed. Planetary gearboxes play an important role in the transmission train of satellite communication antenna. However, condition monitoring of planetary gearbox still faces challenges due to complexity and weak condition feature. This paper provides a possibility for planetary gearbox condition monitoring by proposing ensemble a multiwavelet analysis method. Benefit from the property on multi-resolution analysis and the multiple wavelet basis functions, multiwavelet has the advantage over characterizing the non-stationary signal. In order to realize the accurate detection of the condition feature and multi-resolution analysis in the whole frequency band, adaptive multiwavelet basis function is constructed via increasing multiplicity and then vibration signal is processed by the ensemble multiwavelet transform. Finally, normalized ensemble multiwavelet transform information entropy is computed to describe the condition of planetary gearbox. The effectiveness of proposed method is first validated through condition monitoring of experimental planetary gearbox. Then this method is used for planetary gearbox condition monitoring of ship-based satellite communication antennas and the results support its feasibility.

  1. Disposable indicators for monitoring lighting conditions in museums.

    Science.gov (United States)

    Bacci, Mauro; Cucci, Costanza; Dupont, Anne-Laurence; Lavédrine, Bertrand; Picollo, Marcello; Porcinai, Simone

    2003-12-15

    Photoinduced alterations of light-sensitive artifacts represent one of the main problems that conservators and curators have to face for environmental control in museums and galleries. Therefore, increasing attention has been recently devoted to developing strategies of indoor light monitoring, especially aimed at minimizing the cumulated light exposure for the objects on exhibit. In this work a prototype of a light dosimeter, constituted by a photosensitive dyes/polymer mixture applied on a paper substrate, is presented. This indicator, specially designed for a preventive assessment of the risk of damage for highly light-sensitive objects, undergoes a progressive color variation as its exposure to the light increases. Different, easily distinguishable color steps are exhibited depending on the light dose received, so that the dosimeter can be used straightforwardly to have a first, instrumentation-free estimation of the total light exposure. A reflectance spectroscopy study in the 350-860 nm range was carried out on prototype dosimeters exposed to light emitted from a tungsten-halogen lamp to investigate the response of the dosimeter to the light and to study the fading mechanism. Two different approaches were evaluated for the calibration of the prototype: colorimetry and principal component analysis of the reflectance spectra. The usefulness of the two methods in providing a quantitative indication of the light dose received was evaluated.

  2. Review of Physical Based Monitoring Techniques for Condition Assessment of Corrosion in Reinforced Concrete

    Directory of Open Access Journals (Sweden)

    Ying Lei

    2013-01-01

    Full Text Available Monitoring the condition of steel corrosion in reinforced concrete (RC is imperative for structural durability. In the past decades, many electrochemistry based techniques have been developed for monitoring steel corrosion. However, these electrochemistry techniques can only assess steel corrosion through monitoring the surrounding concrete medium. As alternative tools, some physical based techniques have been proposed for accurate condition assessment of steel corrosion through direct measurements on embedded steels. In this paper, some physical based monitoring techniques developed in the last decade for condition assessment of steel corrosion in RC are reviewed. In particular, techniques based on ultrasonic guided wave (UGW and Fiber Bragg grating (FBG are emphasized. UGW based technique is first reviewed, including important characters of UGW, corrosion monitoring mechanism and feature extraction, monitoring corrosion induced deboning, pitting, interface roughness, and influence factors. Subsequently, FBG for monitoring corrosion in RC is reviewed. The studies and application of the FBG based corrosion sensor developed by the authors are presented. Other physical techniques for monitoring corrosion in RC are also introduced. Finally, the challenges and future trends in the development of physical based monitoring techniques for condition assessment of steel corrosion in RC are put forward.

  3. Analysis of plastic properties of titanium alloys under severe deformation conditions in machining

    Directory of Open Access Journals (Sweden)

    Alexander I. Khaimovich

    2014-10-01

    Full Text Available The present paper presents a method of analysis of titanium alloys plastic properties under severe deformation conditions during milling with registration of the cutting force components Fx, Fy, Fz in real time using a special stand. The obtained constitutive relations in the form the Johnson-Cook law for stresses and dependence for a friction coefficient describing the titanium alloy VT9 plastic properties under simulate operating conditions.

  4. Health Monitoring and Management for Manufacturing Workers in Adverse Working Conditions.

    Science.gov (United States)

    Xu, Xiaoya; Zhong, Miao; Wan, Jiafu; Yi, Minglun; Gao, Tiancheng

    2016-10-01

    In adverse working conditions, environmental parameters such as metallic dust, noise, and environmental temperature, directly affect the health condition of manufacturing workers. It is therefore important to implement health monitoring and management based on important physiological parameters (e.g., heart rate, blood pressure, and body temperature). In recent years, new technologies, such as body area networks, cloud computing, and smart clothing, have allowed the improvement of the quality of services. In this article, we first give five-layer architecture for health monitoring and management of manufacturing workers. Then, we analyze the system implementation process, including environmental data processing, physical condition monitoring and system services and management, and present the corresponding algorithms. Finally, we carry out an evaluation and analysis from the perspective of insurance and compensation for manufacturing workers in adverse working conditions. The proposed scheme will contribute to the improvement of workplace conditions, realize health monitoring and management, and protect the interests of manufacturing workers.

  5. 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.

  6. Condition monitoring of industrial infrastructures using distributed fibre optic acoustic sensors

    Science.gov (United States)

    Hicke, Konstantin; Hussels, Maria-Teresa; Eisermann, René; Chruscicki, Sebastian; Krebber, Katerina

    2017-04-01

    Distributed fibre optic acoustic sensing (DAS) can serve as an excellent tool for real-time condition monitoring of a variety of industrial and civil infrastructures. In this paper, we portray a subset of our current research activities investigating the usability of DAS based on coherent optical time-domain reflectometry (C-OTDR) for innovative and demanding condition monitoring applications. Specifically, our application-oriented research presented here aims at acoustic and vibrational condition monitoring of pipelines and piping systems, of rollers in industrial heavy-duty conveyor belt systems and of extensive submarine power cable installations, respectively.

  7. Use of Advanced Machine-Learning Techniques for Non-Invasive Monitoring of Hemorrhage

    Science.gov (United States)

    2010-04-01

    551-556, 1986. [5] Convertino VA. Endurance exercise training : conditions of enhanced hemodynamic responses and tolerance to LBNP. Med Sci Sports... Muscle sympathetic nerve activity during intense lower body negative pressure to syncope in humans. J Physiol 587:4987-4999, 2009. [17] Deterling...Rickards CA, Lurie KG, and Convertino VA. Breathing through an inspiratory threshold device improves stroke volume during central hypovolemia in humans. J

  8. Diamond Pixel Modules and the ATLAS Beam Conditions Monitor

    CERN Document Server

    Dobos, D

    2011-01-01

    The ATLAS Beam Conditions Monitor’s (BCM) main purpose is to protect the experiments silicon tracker from beam incidents. In total 16 1x1 cm^2 500 um thick diamond pCVD sensors are used in eight positions around the LHC interaction point. They perform time difference measurements with sub nanosecond resolution to distinguish between particles from a collision and spray particles from a beam incident; an abundance of the latter can lead the BCM to provoke an abort of LHC beam. The BCM diamond detector modules, their readout system and the algorithms used to detect beam incidents are described. Results of the BCM operation with circulating LHC beams and it’s commissioning with first LHC collisions are reported.

  9. Monitoring protocol for field testing. Monitoring of heating techniques under practical conditions; Monitoringsprotocol voor veldtesten. Monitoring van warmtetechnieken onder praktijkomstandigheden

    Energy Technology Data Exchange (ETDEWEB)

    Fennema, E.; Jansen, C.

    2009-12-15

    Incentivisation of renewable energy requires large-scale implementation of technologies such as heat-cold storage, heat pumps, cogeneration, solar boilers and waste heat utilization. In practice, the performances of such systems often turn out to deviate from the manufacturer's specifications. Therefore it is important to obtain objective data from practice to gain insight in the differences between theoretical and practical performances and items for improvement of various technologies. The aim of monitoring practice is formulated as: 'gaining insight in the energetic performances of heating techniques under practical circumstances by means of monitoring'. Large-scale measuring in a uniform manner requires a monitoring protocol. Such a protocol safeguards the quality, objectivity, uniformity and hence the reliability of the measuring data. [Dutch] Stimulering van duurzame energie vraagt om grootschalige toepassingen van technologieen zoals warmte-koude opslag, warmtepompen, warmtekracht, zonneboilers en restwarmtebenutting. Het blijkt dat de prestaties van dergelijke systemen in de praktijk vaak afwijken van de fabrikantspecificaties. Daarom is het van belang om objectieve praktijkgegevens te verkrijgen waarmee inzicht wordt verkregen in het verschil tussen theoretische en praktische prestaties, en de verbeterpunten van verschillende technologieen. Het doel van praktijkmonitoring is als volgt geformuleerd: via monitoring het inzicht te verkrijgen in de energetische prestaties van warmtetechnieken onder praktijkomstandigheden. Het uitvoeren van grootschalige metingen op een uniforme wijze vereist een monitoring protocol. Zo'n protocol waarborgt de kwaliteit, objectiviteit, uniformiteit en daarmee de betrouwbaarheid van de meetdata.

  10. The rotating wall machine: a device to study ideal and resistive magnetohydrodynamic stability under variable boundary conditions.

    Science.gov (United States)

    Paz-Soldan, C; Bergerson, W F; Brookhart, M I; Hannum, D A; Kendrick, R; Fiksel, G; Forest, C B

    2010-12-01

    The rotating wall machine, a basic plasma physics experimental facility, has been constructed to study the role of electromagnetic boundary conditions on current-driven ideal and resistive magnetohydrodynamic instabilities, including differentially rotating conducting walls. The device, a screw pinch magnetic geometry with line-tied ends, is described. The plasma is generated by an array of 19 plasma guns that not only produce high density plasmas but can also be independently biased to allow spatial and temporal control of the current profile. The design and mechanical performance of the rotating wall as well as diagnostic capabilities and internal probes are discussed. Measurements from typical quiescent discharges show the plasma to be high β (≤p>2μ(0)/B(z)(2)), flowing, and well collimated. Internal probe measurements show that the plasma current profile can be controlled by the plasma gun array.

  11. Condition Monitoring with WinTControl {sup trademark} in variable-speed wind power systems; Condition Monitoring mit WinTControl {sup trademark} an drehzahlveraenderlichen Windenergieanlagen

    Energy Technology Data Exchange (ETDEWEB)

    Becker; Dahlhaus, N. [Flender Service GmbH, Herne (Germany). Abt. Condition Monitoring

    2003-07-01

    In 2002, German insurance company had to pay the all-time high of about 40 million Euro for damage in wind power systems. In consequence, a servicing and maintenance clause is now included in the insurance contracts, and requirements on condition monitoring for wind power systems were defined. Condition monitoring is based on an analysis of vibrations of components that are subject to wear, i.e. gears, generators, toothing, roller bearings, rotors, and electric components. All of these have typical and significant vibration patterns which can be measured and compared in order to assess the status of a plant by high-sensitivity spectral analyses. The contribution presents the examples of reversible, constant-speed asynchronous generators with gears and of double-fed, variable-speed asynchronous generators with gears. (orig.) [German] Im Jahre 2002 haben die deutschen Versicherer fuer Schaeden an Windenergieanlagen (WEA) den Spitzenwert von rund 40 Mio. Euro bezahlt, was unter anderem dazu fuehrte, dass eine Wartungs- und Instandhaltungsklausel in die Vertraege aufgenommen und Anforderungen an ein geeignetes Condition Monitoring fuer Windenergieanlagen definiert wurden. Basis des Condition Monitoring sind 'Soll/Ist-Vergleiche' des Schwingungsverhaltens verschleissbehafteter Komponenten. Getriebe, Generatoren, Verzahnungen, Waelzlager, Rotoren und E-Technikkomponenten haben typische und signifikante Schwingungsbilder, welche bei Messung unter vergleichbaren Betriebsbedingungen eine einfache Beurteilung der Zustandveraenderung erlauben. Zustandsveraenderungen dieser Komponenten lassen sich ueber Spektralanalysen hochempfindlich verfolgen. Wie sich das Condition Monitoring an WEA dennoch realisieren laesst, wird nachfolgend an Triebstraengen mit polumschaltbaren, drehzahlstarren Asynchrongeneratoren mit Getrieben und an doppeltgespeisten, drehzahlveraenderlichen Asynchrongeneratoren mit Getrieben beschrieben. (orig.)

  12. 7 CFR 623.16 - Monitoring and enforcement of easement terms and conditions.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 6 2010-01-01 2010-01-01 false Monitoring and enforcement of easement terms and conditions. 623.16 Section 623.16 Agriculture Regulations of the Department of Agriculture (Continued) NATURAL RESOURCES CONSERVATION SERVICE, DEPARTMENT OF AGRICULTURE WATER RESOURCES EMERGENCY WETLANDS RESERVE PROGRAM § 623.16 Monitoring and...

  13. Application of the quantitative oil monitoring to analysing the operating condition of marine machinery

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Based on the data of Fourier transform infra-red (FTIR) spectroscopy, spectrometric oilanalysis (SOA) and other routine methods, the experiment of oil monitoring for steering propellersis discussed. The experiment demonstrates the FTIR spectroscopy can rapidly and easily obtainthe results in laboratory analysis, and combine with spectrometer oil analysis, complementary in-formation is most effective to condition monitoring of marine machinery.

  14. 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

  15. 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

  16. Operational-Condition-Independent Criteria Dedicated to Monitoring Wind Turbine Generators

    Directory of Open Access Journals (Sweden)

    Richard Court

    2013-01-01

    Full Text Available Condition monitoring is beneficial to the wind industry for both onshore and offshore plants. However, due to the variations in operational conditions, its potential has not been fully explored. There is a need to develop an operational-condition-independent condition monitoring technique, which has motivated the research presented here. In this paper, three operational-condition-independent criteria are developed. The criteria accomplish the condition monitoring by analyzing the wind turbine electrical signals in the time domain. Therefore, they are simple to calculate and ideal for online use. All proposed criteria were tested through both simulated and practical experiments. The experiments have shown that these criteria not only provide a solution for detecting both mechanical and electrical faults that occur in wind turbine generators, but provide a potential tool for diagnosing generator winding faults.

  17. Monitoring pasture damage in subarid conditions in south of Spain.

    Science.gov (United States)

    Díaz, Felix; Saa-Requejo, Antonio; Martín-Sotoca, Juan J.; Dalezios, Nicolas; Tarquis, Ana M.

    2016-04-01

    This work analyzes four areas in Murcia region (Spain) to study the application of the indexed pastures insurances in arid and subarid conditions. For this purpose four zones of 2,5 km have been selected, all of them close to meteorological stations, with records covering the period since 2001 to 2012 and with compound MODIS images of 500 m x 500 m from eight days intervals on that period. In addition to obtain historical series of the Normalized Difference Vegetation Index (NDVI), other indices (NDWI, NDDI and NDWU) have been computed. The results of this study show that NDWU provides additional information to that in the NDVI. In fact, according to our results, NDDI does not provide accurate information for the regions analyzed in this particular case study. In an attempt to relate precipitancy indices and drought situations in the four areas selected, we have showed that Standardized Precipitation Index (SPI) cannot be used accurately for drought intensity assessment. Then new indices have been formulated based on Markov chains: PI5mm and PI10mm.These indices can assess on isolated droughts which are missed by using indexed insurances. Nonetheless, it has also been observed that abnormal droppings in the NDWI index often coincide with drought lapses well established by indexed insurances. Acknowledgements First author acknowledges the Research Grant obtained from CEIGRAM in 2015

  18. Monitoring network-design influence on assessment of ecological condition in wadeable streams

    Science.gov (United States)

    We investigated outcomes of three monitoring networks for assessing ecological character and condition of wadeable streams in the Waikato region, New Zealand. Sites were selected 1) based on a professional judgment network, 2) within categories of stream and watershed characteris...

  19. Photovoltaic Array Condition Monitoring Based on Online Regression of Performance Model

    DEFF Research Database (Denmark)

    Spataru, Sergiu; Sera, Dezso; Kerekes, Tamas

    2013-01-01

    automatic supervision and condition monitoring of the PV system components, especially for small PV installations, where no specialized personnel is present at the site. This work proposes a PV array condition monitoring system based on a PV array performance model. The system is parameterized online, using...... 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...... the performance model is used to predict the power output of the PV array. Utilizing the predicted and measured PV array output power values, the condition monitoring system is able to detect power losses above 5%, occurring in the PV array....

  20. 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...

  1. 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...

  2. 77 FR 24228 - Condition Monitoring Techniques for Electric Cables Used in Nuclear Power Plants

    Science.gov (United States)

    2012-04-23

    ...The U.S. Nuclear Regulatory Commission (NRC or the Commission) is issuing a new guide regulatory guide, (RG) 1.218, ``Condition Monitoring Techniques for Electric Cables Used in Nuclear Power Plants.'' This guide describes techniques that the staff of the NRC considers acceptable for condition monitoring of electric cables for nuclear power plants. RG 1.218 is not intended to be prescriptive,......

  3. Operational-Condition-Independent Criteria Dedicated to Monitoring Wind Turbine Generators: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Yang, W.; Sheng, S.; Court, R.

    2012-08-01

    To date the existing wind turbine condition monitoring technologies and commercially available systems have not been fully accepted for improving wind turbine availability and reducing their operation and maintenance costs. One of the main reasons is that wind turbines are subject to constantly varying loads and operate at variable rotational speeds. As a consequence, the influences of turbine faults and the effects of varying load and speed are coupled together in wind turbine condition monitoring signals. So, there is an urgent need to either introduce some operational condition de-coupling procedures into the current wind turbine condition monitoring techniques or develop a new operational condition independent wind turbine condition monitoring technique to maintain high turbine availability and achieve the expected economic benefits from wind. The purpose of this paper is to develop such a technique. In the paper, three operational condition independent criteria are developed dedicated for monitoring the operation and health condition of wind turbine generators. All proposed criteria have been tested through both simulated and practical experiments. The experiments have shown that these criteria provide a solution for detecting both mechanical and electrical faults occurring in wind turbine generators.

  4. Condition monitoring of planetary gearbox by hardware implementation of artificial neural networks

    DEFF Research Database (Denmark)

    Dabrowski, Dariusz

    2016-01-01

    -stationary conditions and are exposed to extreme events. Also bucket-wheel excavators are equipped with high-power gearboxes that are exposed to shocks. Continuous monitoring of their condition is crucial in view of early failures, and to ensure safety of exploitation. Artificial neural networks allow for a quick...... environmental conditions. In this paper, a hardware implementation of an artificial neural network designed for condition monitoring of a planetary gearbox is presented. The implementation was done on a Field Programmable Gate Array (FPGA). It is characterized by much higher efficiency and stability than...

  5. Machine vision process monitoring on a poultry processing kill line: results from an implementation

    Science.gov (United States)

    Usher, Colin; Britton, Dougl; Daley, Wayne; Stewart, John

    2005-11-01

    Researchers at the Georgia Tech Research Institute designed a vision inspection system for poultry kill line sorting with the potential for process control at various points throughout a processing facility. This system has been successfully operating in a plant for over two and a half years and has been shown to provide multiple benefits. With the introduction of HACCP-Based Inspection Models (HIMP), the opportunity for automated inspection systems to emerge as viable alternatives to human screening is promising. As more plants move to HIMP, these systems have the great potential for augmenting a processing facilities visual inspection process. This will help to maintain a more consistent and potentially higher throughput while helping the plant remain within the HIMP performance standards. In recent years, several vision systems have been designed to analyze the exterior of a chicken and are capable of identifying Food Safety 1 (FS1) type defects under HIMP regulatory specifications. This means that a reliable vision system can be used in a processing facility as a carcass sorter to automatically detect and divert product that is not suitable for further processing. This improves the evisceration line efficiency by creating a smaller set of features that human screeners are required to identify. This can reduce the required number of screeners or allow for faster processing line speeds. In addition to identifying FS1 category defects, the Georgia Tech vision system can also identify multiple "Other Consumer Protection" (OCP) category defects such as skin tears, bruises, broken wings, and cadavers. Monitoring this data in an almost real-time system allows the processing facility to address anomalies as soon as they occur. The Georgia Tech vision system can record minute-by-minute averages of the following defects: Septicemia Toxemia, cadaver, over-scald, bruises, skin tears, and broken wings. In addition to these defects, the system also records the length and

  6. Thyroid Cells Exposed to Simulated Microgravity Conditions - Comparison of the Fast Rotating Clinostat and the Random Positioning Machine

    Science.gov (United States)

    Warnke, Elisabeth; Kopp, Sascha; Wehland, Markus; Hemmersbach, Ruth; Bauer, Johann; Pietsch, Jessica; Infanger, Manfred; Grimm, Daniela

    2016-06-01

    The ground-based facilities 2D clinostat (CN) and Random Positioning Machine (RPM) were designed to simulate microgravity conditions on Earth. With support of the CORA-ESA-GBF program we could use both facilities to investigate the impact of simulated microgravity on normal and malignant thyroid cells. In this review we report about the current knowledge of thyroid cancer cells and normal thyrocytes grown under altered gravity conditions with a special focus on growth behaviour, changes in the gene expression pattern and protein content, as well as on altered secretion behaviour of the cells. We reviewed data obtained from normal thyrocytes and cell lines (two poorly differentiated follicular thyroid cancer cell lines FTC-133 and ML-1, as well as the normal thyroid cell lines Nthy-ori 3-1 and HTU-5). Thyroid cells cultured under conditions of simulated microgravity (RPM and CN) and in Space showed similar changes with respect to spheroid formation. In static 1 g control cultures no spheroids were detectable. Changes in the regulation of cytokines are discussed to be involved in MCS (multicellular spheroids) formation. The ESA-GBF program helps the scientists to prepare future spaceflight experiments and furthermore, it might help to identify targets for drug therapy against thyroid cancer.

  7. Application of Condition-Based Monitoring Techniques for Remote Monitoring of a Simulated Gas Centrifuge Enrichment Plant

    Energy Technology Data Exchange (ETDEWEB)

    Hooper, David A [ORNL; Henkel, James J [ORNL; Whitaker, Michael [ORNL

    2012-01-01

    This paper presents research into the adaptation of monitoring techniques from maintainability and reliability (M&R) engineering for remote unattended monitoring of gas centrifuge enrichment plants (GCEPs) for international safeguards. Two categories of techniques are discussed: the sequential probability ratio test (SPRT) for diagnostic monitoring, and sequential Monte Carlo (SMC or, more commonly, particle filtering ) for prognostic monitoring. Development and testing of the application of condition-based monitoring (CBM) techniques was performed on the Oak Ridge Mock Feed and Withdrawal (F&W) facility as a proof of principle. CBM techniques have been extensively developed for M&R assessment of physical processes, such as manufacturing and power plants. These techniques are normally used to locate and diagnose the effects of mechanical degradation of equipment to aid in planning of maintenance and repair cycles. In a safeguards environment, however, the goal is not to identify mechanical deterioration, but to detect and diagnose (and potentially predict) attempts to circumvent normal, declared facility operations, such as through protracted diversion of enriched material. The CBM techniques are first explained from the traditional perspective of maintenance and reliability engineering. The adaptation of CBM techniques to inspector monitoring is then discussed, focusing on the unique challenges of decision-based effects rather than equipment degradation effects. These techniques are then applied to the Oak Ridge Mock F&W facility a water-based physical simulation of a material feed and withdrawal process used at enrichment plants that is used to develop and test online monitoring techniques for fully information-driven safeguards of GCEPs. Advantages and limitations of the CBM approach to online monitoring are discussed, as well as the potential challenges of adapting CBM concepts to safeguards applications.

  8. 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 the set up ANFIS models for anomaly detection is proved by the achieved performance of the models. In combination with the FIS the prediction errors can provide information about the condition of the monitored components. In this paper the condition monitoring system is described. Part two...... 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...

  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. Development of a Beam Condition Monitor System for the Experimental Areas of the LHC Using CVD Diamond

    CERN Document Server

    Fernández-Hernando, L

    2004-01-01

    The CERN Large Hadron Collider (LHC) will store 2808 bunches per colliding beam, each bunch consisting of 10^11 protons at an energy of 7 TeV. If there is a failure in an element of the accelerator, the resulting beam losses could cause damages not only to the machine but also to the experiments. A Beam Condition Monitor (BCM) is foreseen to monitor fast increments of particle fluxes near the interaction point and, if necessary, to generate an abort signal to the LHC accelerator control to dump the beams. The system is being developed initially for the CMS experiment but is sufficiently general to find potential applications elsewhere. Due to its high radiation hardness, CVD diamond has been studied for use as the BCM sensor. Various samples of CVD diamond have been characterized extensively with a Sr-90 source and high intensity test beams in order to assess the capabilities of such sensors and to study whether this detector technology is suitable for a BCM system. The results from these investigations are p...

  11. 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.

  12. Surface roughness and cutting forces modeling for optimization of machining condition in finish hard turning of AISI 52100 steel

    Energy Technology Data Exchange (ETDEWEB)

    Azizi, Mohamed Walid; Belhadi, Salim; Yallese, Mohamed Athmane [Univ. of Guelma, Guelma (Algeria); Mabrouki, Tarek; Rigal, Jean Francois [Univ. of Lyon, Lyon (France)

    2012-12-15

    An experimental investigation was conducted to analyze the effect of cutting parameters (cutting speed, feed rate and depth of cut) and workpiece hardness on surface roughness and cutting force components. The finish hard turning of AISI 52100 steel with coated Al2O3 + TiC mixed ceramic cutting tools was studied. The planning of experiment were based on Taguchi's L27 orthogonal array. The response table and analysis of variance (ANOVA) have allowed to check the validity of linear regression model and to determine the significant parameters affecting the surface roughness and cutting forces. The statistical analysis reveals that the feed rate, workpiece hardness and cutting speed have significant effects in reducing the surface roughness; whereas the depth of cut, workpiece hardness and feed rate are observed to have a statistically significant impact on the cutting force components than the cutting speed. Consequently, empirical models were developed to correlate the cutting parameters and workpiece hardness with surface roughness and cutting forces. The optimum machining conditions to produce the lowest surface roughness with minimal cutting force components under these experimental conditions were searched using desirability function approach for multiple response factors optimization. Finally, confirmation experiments were performed to verify the pertinence of the developed empirical models.

  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. 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 halted before a breakdown occurs. In the case of photovoltaic (PV) power plants, the system can be simplified into two distinct blocks: the solar panels/modules and the power inverter. A breakdown in either of these blocks can cause significant downtime in the system. Nevertheless, multiple solar module...

  15. Quantum heat current under non-perturbative and non-Markovian conditions: Applications to heat machines

    Science.gov (United States)

    Kato, Akihito; Tanimura, Yoshitaka

    2016-12-01

    We consider a quantum system strongly coupled to multiple heat baths at different temperatures. Quantum heat transport phenomena in this system are investigated using two definitions of the heat current: one in terms of the system energy and the other in terms of the bath energy. When we consider correlations among system-bath interactions (CASBIs)—which have a purely quantum mechanical origin—the definition in terms of the bath energy becomes different. We found that CASBIs are necessary to maintain the consistency of the heat current with thermodynamic laws in the case of strong system-bath coupling. However, within the context of the quantum master equation approach, both of these definitions are identical. Through a numerical investigation, we demonstrate this point for a non-equilibrium spin-boson model and a three-level heat engine model using the reduced hierarchal equations of motion approach under the strongly coupled and non-Markovian conditions. We observe the cyclic behavior of the heat currents and the work performed by the heat engine, and we find that their phases depend on the system-bath coupling strength. Through consideration of the bath heat current, we show that the efficiency of the heat engine decreases as the strength of the system-bath coupling increases, due to the CASBI contribution. In the case of a large system-bath coupling, the efficiency decreases further if the bath temperature is increased, even if the ratio of the bath temperatures is fixed, due to the discretized nature of energy eigenstates. This is also considered to be a unique feature of quantum heat engines.

  16. [A portable impedance meter for monitoring liquid compartments of human body under space flight conditions].

    Science.gov (United States)

    Noskov, V B; Nikolaev, D V; Tuĭkin, S A; Kozharinov, V I; Grachev, V A

    2007-01-01

    A portable two-frequency tetrapolar impedance meter was developed to study the state of liquid compartments of human body under zero-gravity conditions. The portable impedance meter makes it possible to monitor the hydration state of human body under conditions of long-term space flight on board international space station.

  17. Research progress and prospects on machinery monitoring under varying working condition

    Institute of Scientific and Technical Information of China (English)

    Lin Jing; Zhao Ming

    2013-01-01

    A general review is given about the research progress of the rotating machinery condition monitoring under varying working condition.The major typical methods for analyzing are reviewed,including their progress,deficiencies and capabilities.Some prospects are given finally.

  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. Unraveling fabrication and calibration of wearable gas monitor for use under free-living conditions.

    Science.gov (United States)

    Yue Deng; Cheng Chen; Tsow, Francis; Xiaojun Xian; Forzani, Erica

    2016-08-01

    Volatile organic compounds (VOC) are organic chemicals that have high vapor pressure at regular conditions. Some VOC could be dangerous to human health, therefore it is important to determine real-time indoor and outdoor personal exposures to VOC. To achieve this goal, our group has developed a wearable gas monitor with a complete sensor fabrication and calibration protocol for free-living conditions. Correction factors for calibrating the sensors, including sensitivity, aging effect, and temperature effect are implemented into a Quick Response Code (QR code), so that the pre-calibrated quartz tuning fork (QTF) sensor can be used with the wearable monitor under free-living conditions.

  20. 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...... on the angular location of residual energy. Also, the framework can be extended, for instance by post modeling of repeated faults. Furthermore, we have investigated the problem of non-stationary condition monitoring when operational changes induce angular timing changes in the observed signals. Our contribution......, 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-...

  1. Using the Sandia Z Machine to Probe Water at Planetary Conditions: Redefining the Properties of Water in the Ice Giants

    Science.gov (United States)

    Knudson, M. D.; Desjarlais, M.; Lemke, R.; Mattsson, T.; French, M.; Nettelmann, N.; Redmer, R.

    2012-12-01

    Recently, there has been a tremendous increase in the number of identified extrasolar planetary systems. Our understanding of their formation is tied to exoplanet internal structure models, which rely upon equation of state (EOS) models of light elements and compounds such as water at multi-Mbar pressure conditions. For the past decade, a large, interdisciplinary team at Sandia National Laboratories has been refining the Z Machine (20+ MA and 10+ MGauss) into a mature, robust, and precise platform for material dynamics experiments in the multi-Mbar pressure regime. In particular, significant effort has gone into effectively coupling condensed matter theory, magneto-hydrodynamic simulation, and electromagnetic modeling to produce a fully self-consistent simulation capability able to very accurately predict the performance of the Z machine and various experimental load configurations. This capability has been instrumental in the ability to develop experimental platforms to routinely perform magnetic ramp compression experiments to over 4 Mbar, and magnetically accelerate flyer plates to over 40 km/s, creating over 20 Mbar impact pressures. Furthermore, a strong tie has been developed between the condensed matter theory and the experimental program. This coupling has been proven time and again to be extremely fruitful, with the capability of both theory and experiment being challenged and advanced through this close interrelationship. This presentation will provide a short overview of the material dynamics platform and discuss in more detail the use of Z to perform extreme material dynamics studies with unprecedented accuracy on water in support of basic science, planetary astrophysics, and the emerging field of high energy density laboratory physics. It was found that widely used EOSs for water are much too compressible (up to 30 percent) at pressures and temperatures relevant to planetary interiors. Furthermore, it is shown that the behavior of water at these

  2. Lubrication Oil Condition Monitoring and Remaining Useful Life Prediction With Particle Filtering

    Directory of Open Access Journals (Sweden)

    Yongzhi Qu

    2013-01-01

    Full Text Available In order to reduce the costs of wind energy, it is necessary to improve the wind turbine availability and reduce the operational and maintenance costs. The reliability and availability of a functioning wind turbine depend largely on the protective properties of the lubrication oil for its drive train subassemblies such as gearbox and means for lubrication oil condition monitoring and degradation detection. The wind industry currently uses lubrication oil analysis for detecting gearbox and bearing wear but cannot detect the functional failures of the lubrication oils. The main purpose of lubrication oil condition monitoring and degradation detection is to determine whether the oils have deteriorated to such a degree that they no longer fulfill their functions. This paper describes a research on developing online lubrication oil health condition monitoring and remaining useful life prediction with particle filtering technique using commercially available online sensors. The paper first presents a survey on current state-of-the-art online lubrication oil condition monitoring solutions and their characteristics along with the classification and evaluation of each technique. It is then followed by an investigation on wind turbine gearbox lubrication oil health condition monitoring and degradation detection using online viscosity and dielectric constant sensors. In particular, the lubricant performance evaluation and remaining useful life prediction of degraded lubrication oil with viscosity and dielectric constant data using particle filtering are presented. A simulation case study is provided to demonstrate the effectiveness of the developed technique.

  3. 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....

  4. Fast Beam Conditions Monitor BCM1F for the CMS Experiment

    CERN Document Server

    Bell, A; Hall-Wilton, R; Lange, W; Lohmann, W; Macpherson, A; Ohlerich, M; Rodriguez, N; Ryjov, V; Schmidt, R S; Stone, R L

    2010-01-01

    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 be ams of the LHC are described.

  5. Wind turbine condition monitoring based on SCADA data using normal behavior models

    DEFF Research Database (Denmark)

    Schlechtingen, Meik; Santos, Ilmar

    2014-01-01

    This paper is part two of a two part series. The originality of part one was the proposal of a novelty approach for wind turbine supervisory control and data acquisition (SCADA) data mining for condition monitoring purposes. The novelty concerned the usage of adaptive neuro-fuzzy interference......) proposed the prediction errors provide information about the condition of the monitored components.Part two presents application examples illustrating the efficiency of the proposed method. The work is based on continuously measured wind turbine SCADA data from 18 modern type pitch regulated wind turbines...... of the 2 MW class covering a period of 35 months. Several real life faults and issues in this data are analyzed and evaluated by the condition monitoring system (CMS) and the results presented. It is shown that SCADA data contain crucial information for wind turbine operators worth extracting. Using full...

  6. 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.)

  7. Conditional cooperation and costly monitoring explain success in forest commons management.

    Science.gov (United States)

    Rustagi, Devesh; Engel, Stefanie; Kosfeld, Michael

    2010-11-12

    Recent evidence suggests that prosocial behaviors like conditional cooperation and costly norm enforcement can stabilize large-scale cooperation for commons management. However, field evidence on the extent to which variation in these behaviors among actual commons users accounts for natural commons outcomes is altogether missing. Here, we combine experimental measures of conditional cooperation and survey measures on costly monitoring among 49 forest user groups in Ethiopia with measures of natural forest commons outcomes to show that (i) groups vary in conditional cooperator share, (ii) groups with larger conditional cooperator share are more successful in forest commons management, and (iii) costly monitoring is a key instrument with which conditional cooperators enforce cooperation. Our findings are consistent with models of gene-culture coevolution on human cooperation and provide external validity to laboratory experiments on social dilemmas.

  8. Applying support vector machine on hybrid fNIRS/EEG signal to classify driver's conditions (Conference Presentation)

    Science.gov (United States)

    Nguyen, Thien; Ahn, Sangtae; Jang, Hyojung; Jun, Sung C.; Kim, Jae G.

    2016-03-01

    Driver's condition plays a critical role in driving safety. The fact that about 20 percent of automobile accidents occurred due to driver fatigue leads to a demand for developing a method to monitor driver's status. In this study, we acquired brain signals such as oxy- and deoxyhemoglobin and neuronal electrical activity by a hybrid fNIRS/EEG system. Experiments were conducted with 11 subjects under two conditions: Normal condition, when subjects had enough sleep, and sleep deprivation condition, when subject did not sleep previous night. During experiment, subject performed a driving task with a car simulation system for 30 minutes. After experiment, oxy-hemoglobin and deoxy-hemoglobin changes were derived from fNIRS data, while beta and alpha band relative power were calculated from EEG data. Decrement of oxy-hemoglobin, beta band power, and increment of alpha band power were found in sleep deprivation condition compare to normal condition. These features were then applied to classify two conditions by Fisher's linear discriminant analysis (FLDA). The ratio of alpha-beta relative power showed classification accuracy with a range between 62% and 99% depending on a subject. However, utilization of both EEG and fNIRS features increased accuracy in the range between 68% and 100%. The highest increase of accuracy is from 63% using EEG to 99% using both EEG and fNIRS features. In conclusion, the enhancement of classification accuracy is shown by adding a feature from fNIRS to the feature from EEG using FLDA which provides the need of developing a hybrid fNIRS/EEG system.

  9. A time-frequency analysis approach for condition monitoring of a wind turbine gearbox under varying load conditions

    Science.gov (United States)

    Antoniadou, I.; Manson, G.; Staszewski, W. J.; Barszcz, T.; Worden, K.

    2015-12-01

    This paper deals with the condition monitoring of wind turbine gearboxes under varying operating conditions. Generally, gearbox systems include nonlinearities so a simplified nonlinear gear model is developed, on which the time-frequency analysis method proposed is first applied for the easiest understanding of the challenges faced. The effect of varying loads is examined in the simulations and later on in real wind turbine gearbox experimental data. The Empirical Mode Decomposition (EMD) method is used to decompose the vibration signals into meaningful signal components associated with specific frequency bands of the signal. The mode mixing problem of the EMD is examined in the simulation part and the results in that part of the paper suggest that further research might be of interest in condition monitoring terms. For the amplitude-frequency demodulation of the signal components produced, the Hilbert Transform (HT) is used as a standard method. In addition, the Teager-Kaiser energy operator (TKEO), combined with an energy separation algorithm, is a recent alternative method, the performance of which is tested in the paper too. The results show that the TKEO approach is a promising alternative to the HT, since it can improve the estimation of the instantaneous spectral characteristics of the vibration data under certain conditions.

  10. A hybrid fiber-optic sensor system for condition monitoring of large scale wind turbine blades

    Science.gov (United States)

    Kim, Dae-gil; Kim, Hyunjin; Sampath, Umesh; Song, Minho

    2015-07-01

    A hybrid fiber-optic sensor system which combines fiber Bragg grating (FBG) sensors and a Michelson interferometer is suggested for condition monitoring uses of large scale wind turbine blades. The system uses single broadband light source to address both sensors, which simplifies the optical setup and enhances the cost-effectiveness of condition monitoring system. An athermal-packaged FBG is used to supply quasi-coherent light for the Michelson interferometer demodulation. For the feasibility test, different profiles of test strain, temperature and vibration have been applied to test structures, and successfully reconstructed with the proposed sensor system.

  11. A Web-based Condition Monitoring and Diagnostic System of Rolling Mill

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    A web-based condition monitoring and fault diagnosis system (CMAFDS) for the F2 finishing mill of the 2050 Hot Strip Mill was developed at a steel works. The features of the condition monitoring and fault diagnosis system based on the Web are analyzed in this paper. This paper also describes the main frame of the hardware and the software in the system and emphatically points out the function of the database management system(DBMS) based on the Web. It is proved that the web-based CMAFDS is practical in technology and much superior to the CMAFDS based on other network technology in functions.

  12. Artificial Neural Network and Rough Set for HV Bushings Condition Monitoring

    CERN Document Server

    Mpanza, LJ

    2011-01-01

    Most transformer failures are attributed to bushings failures. Hence it is necessary to monitor the condition of bushings. In this paper three methods are developed to monitor the condition of oil filled bushing. Multi-layer perceptron (MLP), Radial basis function (RBF) and Rough Set (RS) models are developed and combined through majority voting to form a committee. The MLP performs better that the RBF and the RS is terms of classification accuracy. The RBF is the fasted to train. The committee performs better than the individual models. The diversity of models is measured to evaluate their similarity when used in the committee.

  13. 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

    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...

  14. In-situ stress measurements and stress change monitoring to monitor overburden caving behaviour and hydraulic fracture pre-conditioning

    Institute of Scientific and Technical Information of China (English)

    Puller Jesse W.; Mills Ken W.; Jeffrey Rob G.; Walker Rick J.

    2016-01-01

    A coal mine in New South Wales is longwall mining 300 m wide panels at a depth of 160–180 m directly below a 16–20 m thick conglomerate strata. As part of a strategy to use hydraulic fracturing to manage potential windblast and periodic caving hazards associated with these conglomerate strata, the in-situ stresses in the conglomerate were measured using ANZI strain cells and the overcoring method of stress relief. Changes in stress associated with abutment loading and placement of hydraulic fractures were also measured using ANZI strain cells installed from the surface and from underground. Overcore stress mea-surements have indicated that the vertical stress is the lowest principal stress so that hydraulic fractures placed ahead of mining form horizontally and so provide effective pre-conditioning to promote caving of the conglomerate strata. Monitoring of stress changes in the overburden strata during longwall retreat was undertaken at two different locations at the mine. The monitoring indicated stress changes were evi-dent 150 m ahead of the longwall face and abutment loading reached a maximum increase of about 7.5 MPa. The stresses ahead of mining change gradually with distance to the approaching longwall and in a direction consistent with the horizontal in-situ stresses. There was no evidence in the stress change monitoring results to indicate significant cyclical forward abutment loading ahead of the face. The for-ward abutment load determined from the stress change monitoring is consistent with the weight of over-burden strata overhanging the goaf indicated by subsidence monitoring.

  15. [Research on the inner wall condition monitoring method of ring forgings based on infrared spectra].

    Science.gov (United States)

    Fu, Xian-bin; Liu, Bin; Wei, Bin; Zhang, Yu-cun; Liu, Zhao-lun

    2015-01-01

    In order to grasp the inner wall condition of ring forgings, an inner wall condition monitoring method based on infrared spectra for ring forgings is proposed in the present paper. Firstly, using infrared spectroscopy the forgings temperature measurement system was built based on the three-level FP-cavity LCTF. The two single radiation spectra from the forgings' surface were got using the three-level FP-cavity LCTF. And the temperature measuring of the surface forgings was achieved according to the infrared double-color temperature measuring principle. The measuring accuracy can be greatly improved by this temperature measurement method. Secondly, on the basis of the Laplace heat conduction differential equation the inner wall condition monitoring model was established by the method of separating variables. The inner wall condition monitoring of ring forgings was realized via combining the temperature data and the forgings own parameter information. Finally, this method is feasible according to the simulation experiment. The inner wall condition monitoring method can provide the theoretical basis for the normal operating of the ring forgings.

  16. Forecasting Urban Water Demand via Machine Learning Methods Coupled with a Bootstrap Rank-Ordered Conditional Mutual Information Input Variable Selection Method

    Science.gov (United States)

    Adamowski, J. F.; Quilty, J.; Khalil, B.; Rathinasamy, M.

    2014-12-01

    This paper explores forecasting short-term urban water demand (UWD) (using only historical records) through a variety of machine learning techniques coupled with a novel input variable selection (IVS) procedure. The proposed IVS technique termed, bootstrap rank-ordered conditional mutual information for real-valued signals (brCMIr), is multivariate, nonlinear, nonparametric, and probabilistic. The brCMIr method was tested in a case study using water demand time series for two urban water supply system pressure zones in Ottawa, Canada to select the most important historical records for use with each machine learning technique in order to generate forecasts of average and peak UWD for the respective pressure zones at lead times of 1, 3, and 7 days ahead. All lead time forecasts are computed using Artificial Neural Networks (ANN) as the base model, and are compared with Least Squares Support Vector Regression (LSSVR), as well as a novel machine learning method for UWD forecasting: the Extreme Learning Machine (ELM). Results from one-way analysis of variance (ANOVA) and Tukey Honesty Significance Difference (HSD) tests indicate that the LSSVR and ELM models are the best machine learning techniques to pair with brCMIr. However, ELM has significant computational advantages over LSSVR (and ANN) and provides a new and promising technique to explore in UWD forecasting.

  17. Controller integrated condition monitoring systems (CMS) for wind energy converters in on- and offshore environment; Steuerungsintegrierte Condition Monitoring Systeme (CMS) fuer Windenergieanlagen im On- und Offshoreumfeld

    Energy Technology Data Exchange (ETDEWEB)

    Hoering, Bernd [8.2 Monitoring GmbH, Hamburg (Germany)

    2013-11-01

    Twelve years of application experience with vibration-based Condition Monitoring System (CMS) on wind energy show success. However, CMS is not considered a core competence. Currently the market of CMS suppliers is radically changing. In future, operators with different CMS in use or wind turbine manufacturers, who use different control systems with integrated CMS, can serve their different CMS hardware with a uniform software. The software was specially designed for the Chinese market and is already in operation in the on- and offshore environment. (orig.)

  18. 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.

  19. 一种可抗TCPFlooding攻击的网络流量监测机制①%Network Monitoring Machine Against TCP Flooding Attacks

    Institute of Scientific and Technical Information of China (English)

    2013-01-01

      DDoS攻击是互联网的主要安全威胁之一,而大部分DDoS攻击工具都使用TCP Flooding攻击方式,基于大量研究相关技术的基础上,提出了一种可用于局域网的网络流量监测机制,可以有效的检测出TCP Flooding攻击,解决当前各种网络安全设备在此方面存在的问题。%DDoS attacks are a major threat to internet and almost all of DDoS attacker use TCP Flooding attacks. Based on lots of studying, a network monitoring machine is presented. The machine can detect TCP Flooding attacks for local area network and solve problems of other security production.

  20. Study of Fuzzy Neural Networks Model for System Condition Monitoring of AUV

    Institute of Scientific and Technical Information of China (English)

    WANG Yu-jia; ZHANG Ming-jun

    2002-01-01

    A structure equivalent model of fuzzy-neural networks for system condition monitoring is proposed, whose outputs are the condition or the degree of fault occurring in some parts of the system. This network is composed of six layers of neurons,which represent the membership functions, fuzzy rules and outputs respectively. The structure parameters and weights are obtained by processing off-line learning, and the fuzzy rules are derived from the experience. The results of the computer simulation for the autonomous underwater vehicle condition monitoring based on this fuzzy-neural networks show that the network is efficient and feasible in gaining the condition information or the degree of fault of the two main propellers.

  1. New Fast Beam Conditions Monitoring (BCM1F) system for CMS

    CERN Document Server

    AUTHOR|(CDS)2083575; 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 sub-bunch structure.

  2. 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.

  3. Multifunctional ultrasonic sensor for on-line tool condition monitoring in turning operations

    Energy Technology Data Exchange (ETDEWEB)

    Nayfeh, T.H.; Abu-Zahra, N.H. [Cleveland State Univ., OH (United States). Industrial Engineering Dept.

    1998-03-01

    Machining operations in automated manufacturing centers are, in general, under-performing by 20--80 percent. Optimizing these machining operations requires on-line knowledge of the cutting tool`s condition and the process state. Currently, this information is either not reliable or not available in a timely manner. This in part is due to the lack of suitable sensors which are able to measure on-line directly and accurately one or more of the relevant tool and process variables. A direct, active, ultrasonic method for on-line sensing of the tool condition and the process state in turning operations was developed in this work. Sensing is achieved by using an ultrasonic transducer operating at 10 MHz in a pulse-echo mode to send pulses through the cutting tool. The amplitude and propagation time of the reflected pulses are modulated by the tool nose, flank, temperature, and by the material in contact with the tools. This method has the potential to measure on-line several relevant process and cutting tool parameters directly and accurately through the use of a single sensor. These parameters are tool-workpiece contact, tool gradual wear, tool chipping and tool chatter.

  4. ANN based Performance Evaluation of BDI for Condition Monitoring of Induction Motor Bearings

    Science.gov (United States)

    Patel, Raj Kumar; Giri, V. K.

    2016-07-01

    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.

  5. 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

  6. Railway track component condition monitoring using optical fibre Bragg grating sensors

    Science.gov (United States)

    Buggy, S. J.; James, S. W.; Staines, S.; Carroll, R.; Kitson, P.; Farrington, D.; Drewett, L.; Jaiswal, J.; Tatam, R. P.

    2016-05-01

    The use of optical fibre Bragg grating (FBG) strain sensors to monitor the condition of safety critical rail components is investigated. Fishplates, switchblades and stretcher bars on the Stagecoach Supertram tramway in Sheffield in the UK have been instrumented with arrays of FBG sensors. The dynamic strain signatures induced by the passage of a tram over the instrumented components have been analysed to identify features indicative of changes in the condition of the components.

  7. Principles for the monitoring and evaluation of wetland extent, condition and function in Australia.

    Science.gov (United States)

    Saintilan, Neil; Imgraben, Sarah

    2012-01-01

    The monitoring of resource condition is receiving renewed attention across several levels of government in Australia. This interest is linked to substantial investment in environmental remediation and aquatic ecosystem restoration in particular. In this context, it is timely to consider principles which ought to guide the development and implementation of monitoring programmes for wetland ecosystems. A framework is established which places monitoring in the context of the strategic adaptive management of wetlands. This framework requires there has to be clear goals for the extent and condition of the resource, with these goals being defined within thresholds of acceptable variability. Qualitative and, where possible, quantitative conceptual models linking management interventions to management goals should be the basis of indicator selection and assessment. The intensity of sampling ought to be informed by pilot surveys of statistical power in relation to the thresholds of acceptable variability identified within the management plan.

  8. A Recursive Multiscale Correlation-Averaging Algorithm for an Automated Distributed Road Condition Monitoring System

    Energy Technology Data Exchange (ETDEWEB)

    Ndoye, Mandoye [Lawrence Livermore National Laboratory (LLNL); Barker, Alan M [ORNL; Krogmeier, James [Purdue University; Bullock, Darcy [Purdue University

    2011-01-01

    A signal processing approach is proposed to jointly filter and fuse spatially indexed measurements captured from many vehicles. It is assumed that these measurements are influenced by both sensor noise and measurement indexing uncertainties. Measurements from low-cost vehicle-mounted sensors (e.g., accelerometers and Global Positioning System (GPS) receivers) are properly combined to produce higher quality road roughness data for cost-effective road surface condition monitoring. The proposed algorithms are recursively implemented and thus require only moderate computational power and memory space. These algorithms are important for future road management systems, which will use on-road vehicles as a distributed network of sensing probes gathering spatially indexed measurements for condition monitoring, in addition to other applications, such as environmental sensing and/or traffic monitoring. Our method and the related signal processing algorithms have been successfully tested using field data.

  9. A brief status on condition monitoring and fault diagnosis in wind energy conversion systems

    Energy Technology Data Exchange (ETDEWEB)

    Amirat, Y. [University of Brest, EA 4325 LBMS, 29238 Brest (France); University of Annaba, Electrical Engineering Department, 23000 Annaba (Algeria); Benbouzid, M.E.H.; Turri, S. [University of Brest, EA 4325 LBMS, 29238 Brest (France); Al-Ahmar, E. [University of Brest, EA 4325 LBMS, 29238 Brest (France); Holy Spirit University of Kaslik, Faculty of Sciences and Computer Engineering, BP 446 Jounieh (Lebanon); Bensaker, B. [University of Annaba, Electrical Engineering Department, 23000 Annaba (Algeria)

    2009-12-15

    There is a constant need for the reduction of operational and maintenance costs of Wind Energy Conversion Systems (WECSs). The most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early detection of the degeneration of the generator health, facilitating a proactive response, minimizing downtime, and maximizing productivity. Wind generators are also inaccessible since they are situated on extremely high towers, which are normally 20 m or more in height. There are also plans to increase the number of offshore sites increasing the need for a remote means of WECS monitoring that eliminates some of the difficulties faced due to accessibility problems. Therefore and due to the importance of condition monitoring and fault diagnosis in WECS (blades, drive trains, and generators), and keeping in mind the need for future research, this paper is intended as a brief status describing different types of faults, their generated signatures, and their diagnostic schemes. (author)

  10. Structural condition assessment of long-span suspension bridges using long-term monitoring data

    Science.gov (United States)

    Yang, Deng; Youliang, Ding; Aiqun, Li

    2010-03-01

    This paper focuses on developing an online structural condition assessment technique using long-term monitoring data measured by a structural health monitoring system. The seasonal correlations of frequency-temperature and beam-end displacement-temperature for the Runyang Suspension Bridge are performed, first. Then, a statistical modeling technique using a six-order polynomial is further applied to formulate the correlations of frequency-temperature and displacement-temperature, from which abnormal changes of measured frequencies and displacements are detected using the mean value control chart. Analysis results show that modal frequencies of higher vibration modes and displacements have remarkable seasonal correlations with the environmental temperature and the proposed method exhibits a good capability for detecting the micro damage-induced changes of modal frequencies and displacements. The results demonstrate that the proposed method can effectively eliminate temperature complications from frequency and displacement time series and is well suited for online condition monitoring of long-span suspension bridges.

  11. 基于机床做功量的监控系统开发%Development of Monitoring System Based on the Work Load of Machine Tool

    Institute of Scientific and Technical Information of China (English)

    李平; 黄泽森

    2013-01-01

    在分析机床维护保养不良是影响机床可靠性的关键因素的基础上,将一种新的可靠性思想——用户监控维护方法引入数控机床可靠性研究领域中.提出了机床定功维护的概念,并以丝杆螺母副维护保养为例,建立了丝杆螺母副与电机做功关系的数学模型.给出了主轴电机功率的获取方法,并通过对数控系统西门子840D进行二次编程,开发了基于用户角度来提高机床可靠性的用户监控维护系统.该系统在数控机床上的成功应用为机床可靠性的研究提供了一种新的思路和方法.%On the basis of poor maintenance of NC machine tool is the key factor of influencing the machine tool reliability, a new method which called User Monitor and Maintenance was introduced, and a creational concept of maintenance based on work to the field of NC machine reliability research was put forward. By the example of maintenance of ball screw and nut, the math model between the ball screw and nut, and the work of electromotor was established. The method of obtaining power of main spindle motor was given, and the System of User Monitor and Maintenance based on secondary development of NC Simens 840D system by users to improve the reliability of NC machine tool was developed. The successful application of the system on the NC machine tool provides a new method and idea for the research of NC machine tool's reliability.

  12. 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.

  13. 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

  14. Summer student report - Upgrade work for the Fast Beam Condition Monitor at CMS

    CERN Document Server

    Tsrunchev, Peter

    2016-01-01

    Report on summer student internship at CERN. Describes work done towards the replacement of the Fast Beam Conditions Monitor (BCM1F) - activities related to the test beam conducted by the BRIL (Background Radiation Instrumentation and Luminosity) experiment in July 2016, analog opto-hybrids testing and XDAQ development for the uTCA readout system currently under development.

  15. Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras

    NARCIS (Netherlands)

    Nijland, W.; de Jong, R.; de Jong, S.M.; Wulder, M.A.; Bater, C.W.; Coops, N.C.

    2014-01-01

    Consumer-grade digital cameras are recognized as a cost-effective method of monitoring plant health and phenology. The capacity to use these cameras to produce time series information contributes to a better understanding of relationships between environmental conditions, vegetation health, and prod

  16. 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…

  17. Breath acetone to monitor life style interventions in field conditions: an exploratory study.

    NARCIS (Netherlands)

    Samudrala, D.; Lammers, G.; Mandon, J.B.; Blanchet, Lionel; Schreuder, T.H.A.; Hopman, M.T.E.; Harren, F.J.M.; Tappy, L.; Cristescu, S.M.

    2014-01-01

    OBJECTIVE: 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. METHODS: Twenty-three non-diabetic, 11 type 1 diabetic, and 17 type 2 diabetic subjects provided breath a

  18. 14 CFR 414.31 - Monitoring compliance with the terms and conditions of a safety approval.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 4 2010-01-01 2010-01-01 false Monitoring compliance with the terms and conditions of a safety approval. 414.31 Section 414.31 Aeronautics and Space COMMERCIAL SPACE TRANSPORTATION, FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION LICENSING SAFETY APPROVALS Safety Approval Review and Issuance §...

  19. 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…

  20. Integrating remote sensing data from multiple optical sensors for ecological and crop condition monitoring

    Science.gov (United States)

    Ecological and crop condition monitoring requires high temporal and spatial resolution remote sensing data. Due to technical limitations and budget constraints, remote sensing instruments trade spatial resolution for swath width. As a result, it is difficult to acquire remotely sensed data with both...

  1. Online Condition Monitoring to Enable Extended Operation of Nuclear Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    Meyer, Ryan M.; Bond, Leonard J.; Ramuhalli, Pradeep

    2012-03-31

    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 for 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.

  2. 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 thes

  3. Breath acetone to monitor life style interventions in field conditions: an exploratory study.

    NARCIS (Netherlands)

    Samudrala, D.; Lammers, G.; Mandon, J.B.; Blanchet, Lionel; Schreuder, T.H.A.; Hopman, M.T.E.; Harren, F.J.M.; Tappy, L.; Cristescu, S.M.

    2014-01-01

    OBJECTIVE: 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. METHODS: Twenty-three non-diabetic, 11 type 1 diabetic, and 17 type 2 diabetic subjects provided breath

  4. Thoughts on Geographical Conditions Monitoring%地理国情普查的思考

    Institute of Scientific and Technical Information of China (English)

    刘娇; 程晓勇; 葛超

    2015-01-01

    地理国情普查是当下我国重要的测绘地理信息工程和社会服务之一,它的重要性和紧迫性来源于社会的发展和测绘地理信息技术在当今社会大背景下的滋长与提高。本文从地理国情普查的概念入手,由浅向深探索,思考了地理国情普查的背景、意义和获取及处理数据等技术手段,表达了在地理国情普查工作之中的诸多认识和理解。%Geographical Conditions monitoring is one of the most important Mapping & Geographic Information and social service in our country;its importance and urgency origin from the development of Chinese society and the improvement of the technology under the nowadays society background.This article from the concept perspective of Geographical National Conditions monitoring , thought a-bout the background and significant of Geographical National Conditions monitoring, the technological means of acquire and manage the information.Besides, this article expresses the cognition and understanding of the Geographical National Conditions monitoring during the practical work.

  5. Observation of Built-up Edge Formation on a Carbide Cutting Tool with Machining Aluminium Alloy under Dry and Wet Conditions

    Directory of Open Access Journals (Sweden)

    Azlan U.A.A.

    2017-01-01

    Full Text Available This paper presents the morphology of built-up edge (BUE formation under wet and dry conditions with low and high cutting speeds. The workpiece materials and cutting tools selected for this work were aluminium alloy and canela carbide inserts graded PM25. The cutting tools underwent turning operation machining tests and their performance was evaluated by the flank wear and observation of the tool wear area. The machining tests were conducted at different spindle speeds and feed rates while the cut depth was kept constant. The analysis showed that formation of the BUE was dominant at low cutting speeds in dry conditions, but in wet conditions at high cutting speeds, a better performance was exhibited in terms of wear analysis.

  6. Condition monitoring of oil-impregnated paper bushings using extension neural network, Gaussian mixture and hidden Markov models

    CSIR Research Space (South Africa)

    Miya, WS

    2008-10-01

    Full Text Available In this paper, a comparison between Extension Neural Network (ENN), Gaussian Mixture Model (GMM) and Hidden Markov model (HMM) is conducted for bushing condition monitoring. The monitoring process is a two-stage implementation of a classification...

  7. Assessing the ecological condition of streams in a southeastern Brazilian basin using a probabilistic monitoring design.

    Science.gov (United States)

    Jiménez-Valencia, Juliana; Kaufmann, Philip R; Sattamini, Ana; Mugnai, Riccardo; Baptista, Darcilio Fernandes

    2014-08-01

    Prompt assessment and management actions are required if we are to reduce the current rapid loss of habitat and biodiversity worldwide. Statistically valid quantification of the biota and habitat condition in water bodies are prerequisites for rigorous assessment of aquatic biodiversity and habitat. We assessed the ecological condition of streams in a southeastern Brazilian basin. We quantified the percentage of stream length in good, fair, and poor ecological condition according to benthic macroinvertebrate assemblage. We assessed the risk of finding degraded ecological condition associated with degraded aquatic riparian physical habitat condition, watershed condition, and water quality. We describe field sampling and implementation issues encountered in our survey and discuss design options to remedy them. Survey sample sites were selected using a spatially balanced, stratified random design, which enabled us to put confidence bounds on the ecological condition estimates derived from the stream survey. The benthic condition index indicated that 62 % of stream length in the basin was in poor ecological condition, and 13 % of stream length was in fair condition. The risk of finding degraded biological condition when the riparian vegetation and forests in upstream catchments were degraded was 2.5 and 4 times higher, compared to streams rated as good for the same stressors. We demonstrated that the GRTS statistical sampling method can be used routinely in Brazilian rain forests and other South American regions with similar conditions. This survey establishes an initial baseline for monitoring the condition and trends of streams in the region.

  8. Measurements of the performance of a beam condition monitor prototype in a 5 GeV electron beam

    Science.gov (United States)

    Hempel, M.; Afanaciev, K.; Burtowy, P.; Dabrowski, A.; Henschel, H.; Idzik, M.; Karacheban, O.; Lange, W.; Leonard, J.; Levy, I.; Lohmann, W.; Pollak, B.; Przyborowski, D.; Ryjov, V.; Schuwalow, S.; Stickland, D.; Walsh, R.; Zagozdzinska, A.

    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.

  9. Fault-diagnosis applications. Model-based condition monitoring. Acutators, drives, machinery, plants, sensors, and fault-tolerant systems

    Energy Technology Data Exchange (ETDEWEB)

    Isermann, Rolf [Technische Univ. Darmstadt (DE). Inst. fuer Automatisierungstechnik (IAT)

    2011-07-01

    Supervision, condition-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes and vehicles in order to improve reliability, availability, maintenance and lifetime. For safety-related processes fault-tolerant systems with redundancy are required in order to reach comprehensive system integrity. This book is a sequel of the book ''Fault-Diagnosis Systems'' published in 2006, where the basic methods were described. After a short introduction into fault-detection and fault-diagnosis methods the book shows how these methods can be applied for a selection of 20 real technical components and processes as examples, such as: Electrical drives (DC, AC) Electrical actuators Fluidic actuators (hydraulic, pneumatic) Centrifugal and reciprocating pumps Pipelines (leak detection) Industrial robots Machine tools (main and feed drive, drilling, milling, grinding) Heat exchangers Also realized fault-tolerant systems for electrical drives, actuators and sensors are presented. The book describes why and how the various signal-model-based and process-model-based methods were applied and which experimental results could be achieved. In several cases a combination of different methods was most successful. The book is dedicated to graduate students of electrical, mechanical, chemical engineering and computer science and for engineers. (orig.)

  10. 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

  11. 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

    systems. The health condition of the wind turbine gearboxes can be indicated by the quantity and size of the metal abrasive particles, which may provide very early warnings of faults/failures and benefit the condition based maintenance of the system. An improved inductive sensor probe is proposed......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...... in this paper for the online health monitoring of wind turbine gearbox. The magnetic field homogeneity as well as the performance of the proposed Helmholtz-coil probe are analyzed and verified by finite element analysis....

  12. Condition monitoring of valve clearance fault on a small four strokes petrol engine using vibration signals

    Directory of Open Access Journals (Sweden)

    Songpon Klinchaeam

    2010-12-01

    Full Text Available This paper studies condition monitoring technique of a small four strokes, single cylinder petrol engine using vibrationsignal analysis based on time domain, crank angle domain, and signal energy. Vibration signals are acquired from the cylinderhead of the engine and used to describe engine processes such as intake/exhaust valve operations, ignition process, andcombustion process. In this study, vibration signals have been applied to monitor various fault conditions in the engine suchas intake and exhaust valve clearance faults. Vibration signals acquired in time domain could be mapped onto crank angledomain using top dead center signal. Time domain techniques were used to analyze vibration signals so that the main eventsrelated to the engine operations could be described easily. Using energy analysis technique, all fault conditions could bealso identified. For future work, signal analysis techniques must be developed and the detected signals should be comparedwith other signals such as pressure signal in order to verify the accuracy of the results.

  13. 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...... suggests a novel approach for low-cost, indirect monitoring of the shaft torque from standard WT measurements. The shaft torque is estimated recursively from measurements of generator torque, high speed shaft and low speed shaft angular speeds using the well-known Kalman filter theory. The performance...... 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...

  14. Automated System Of Monitoring Of The Physical Condition Of The Staff Of The Enterprise

    Science.gov (United States)

    Pilipenko, A.

    2017-01-01

    In the work the author solves an important applied problem of increasing of safety of engineering procedures and production using technologies of monitoring of a condition of employees. The author offers a work algorithm, structural and basic electric schemes of system of collection of data of employee’s condition of the enterprise and some parameters of the surrounding environment. In the article the author offers an approach to increasing of efficiency of acceptance of management decisions at the enterprise at the expense of the prompt analysis of information about employee’s condition and productivity of his work and also about various parameters influencing these factors.

  15. CONDITION MONITOR OF DEEP-HOLE DRILLING BASED ON MULTI-SENSOR INFORMATION FUSION

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel 0Crl7Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.

  16. Research of on-line monitoring method for insulation condition of power transformer bushing

    Science.gov (United States)

    Xia, Jiuyun; Qian, Zheng; Yu, Hao; Yao, Junda

    2016-01-01

    The power transformer is the key equipment of the power system; its insulation condition will directly influence the security and reliability of the power system. Thus, the on-line monitoring of power transformer is urgently required in order to guarantee the normal operation of the power system. Moreover, the dielectric loss factor is a significant parameter reflecting the condition of transformer bushing, so the on-line measurement of dielectric loss factor is really important. In this paper, the phase-to-phase comparison method is selected as the on-line monitoring method based on the overall analysis and discussion of the existing on-line monitoring methods. At first, the harmonic analysis method is utilized to calculate the dielectric loss of each phase of the three-phase transformer bushing, and then the differences of dielectric loss between every two phases are calculated and analyzed. So the insulation condition of each bushing could be achieved based on the careful analysis of different phase-to-phase dielectric loss. The simulation results of phase-to-phase comparison method are carried out in this paper, and the validity is verified. At last, this method is utilized in an actual equipment of on-line monitoring.

  17. Thick-film acoustic emission sensors for use in structurally integrated condition-monitoring applications.

    Science.gov (United States)

    Pickwell, Andrew J; Dorey, Robert A; Mba, David

    2011-09-01

    Monitoring the condition of complex engineering structures is an important aspect of modern engineering, eliminating unnecessary work and enabling planned maintenance, preventing failure. Acoustic emissions (AE) testing is one method of implementing continuous nondestructive structural health monitoring. A novel thick-film (17.6 μm) AE sensor is presented. Lead zirconate titanate thick films were fabricated using a powder/sol composite ink deposition technique and mechanically patterned to form a discrete thick-film piezoelectric AE sensor. The thick-film sensor was benchmarked against a commercial AE device and was found to exhibit comparable responses to simulated acoustic emissions.

  18. An Online Non-Invasive Condition Monitoring Method for Stepping Motor CRDM in HTGR

    Directory of Open Access Journals (Sweden)

    S. Bakhri

    2016-12-01

    Full Text Available Control Rod Drive Mechanism (CRDM based on stepping motor is one of the components applied in High Temperature Gas Coold Reactor (HTGR to control the reactivity as well as to maintain the safety of reactor. The stepping motor requires a unique condition monitoring to avoid any failures especially due to the specific environments of CRDM in HTGR such as the allowable of high temperature, high radiation and the location of stepper motor inside a pressure shell. This research aims to demonstrate an online non-invasive condition monitoring method without direct access to the CRDM of HTGR based on voltage and stator current measurements. A simple stepping motor CRDM simulator is employed. The online condition monitoring is carried out by direct pattern matching of the output signals of logic generator block and the output signals of motor driver. The online method utilizes signature patterns of voltage and stator current signals of the healthy motor as a baseline for healthy motor. In addition, the method is applied to detect high-resistance problem on the connector between the motor driver block and the stepper motor to show the effectiveness and the applicability of this method. The online condition monitoring system demonstrates a capability to identify a minimum detectable simulated high-resistance for about 2.9% which decreases the measured stator current and motor’s torque for around 5.1% and 3.3%, respectively. The paper also points out signatures of healthy motor, including mutual inductions of the motor’s winding in voltage and current measurement which can be used as the fault symptom indicators for online monitoring purposes.

  19. Reduction of Doppler effect for the needs of wayside condition monitoring system of railway vehicles

    Science.gov (United States)

    Dybała, Jacek; Radkowski, Stanisław

    2013-07-01

    Technology of acoustic condition monitoring of vehicles in motion is based on the assumption that diagnostically relevant information is stored in the acoustic signal generated by a passing vehicle. Analyzing the possibilities of increasing the effectiveness of condition monitoring of a passing vehicle with stationary microphones, it should be noted that the acoustic signal recorded in these conditions is disturbed with the disturbance resulting from the Doppler effect. Reduction of signal's frequential structure disturbance resulting from the Doppler effect allows efficient analysis of changes in frequential structure of recorded signals and as a result extraction of relevant diagnostic information related with technical condition of running gear of vehicle. This article presents a method for removal of signal's frequential structure disturbances related with relative move of vehicles and stationary monitoring station. For elimination of the frequential non-stationary of signals disturbance-oriented dynamic signal resampling method was used. The paper provides a test of two methods for defining the time course of local disturbance of signal's frequential structure: the method based on the Hilbert transform and the method of analytical description of signal's disturbance based on the knowledge of a phenomenon that causes frequential non-stationarity of signals. As an example, the results of the processing and analysis of acoustic signals recorded by wayside measuring station, during the passage of WM-15A railway vehicle on an experimental track of Polish Railway Institute, are presented.

  20. 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.