WorldWideScience

Sample records for machine health monitoring

  1. 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.

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

    International Nuclear Information System (INIS)

    Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka

    2014-01-01

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

  3. 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.

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

    OpenAIRE

    Rui Zhao; Ruqiang Yan; Jinjiang Wang; Kezhi Mao

    2017-01-01

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

  5. 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.

  6. 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.

  7. 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.

  8. Tool wear and breakage monitoring in machining

    International Nuclear Information System (INIS)

    Madl, J.

    1992-01-01

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

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

    Science.gov (United States)

    Widodo, Achmad; Yang, Bo-Suk

    2007-08-01

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

  10. Ultrasensitive and Highly Stable Resistive Pressure Sensors with Biomaterial-Incorporated Interfacial Layers for Wearable Health-Monitoring and Human-Machine Interfaces.

    Science.gov (United States)

    Chang, Hochan; Kim, Sungwoong; Jin, Sumin; Lee, Seung-Woo; Yang, Gil-Tae; Lee, Ki-Young; Yi, Hyunjung

    2018-01-10

    Flexible piezoresistive sensors have huge potential for health monitoring, human-machine interfaces, prosthetic limbs, and intelligent robotics. A variety of nanomaterials and structural schemes have been proposed for realizing ultrasensitive flexible piezoresistive sensors. However, despite the success of recent efforts, high sensitivity within narrower pressure ranges and/or the challenging adhesion and stability issues still potentially limit their broad applications. Herein, we introduce a biomaterial-based scheme for the development of flexible pressure sensors that are ultrasensitive (resistance change by 5 orders) over a broad pressure range of 0.1-100 kPa, promptly responsive (20 ms), and yet highly stable. We show that employing biomaterial-incorporated conductive networks of single-walled carbon nanotubes as interfacial layers of contact-based resistive pressure sensors significantly enhances piezoresistive response via effective modulation of the interlayer resistance and provides stable interfaces for the pressure sensors. The developed flexible sensor is capable of real-time monitoring of wrist pulse waves under external medium pressure levels and providing pressure profiles applied by a thumb and a forefinger during object manipulation at a low voltage (1 V) and power consumption (<12 μW). This work provides a new insight into the material candidates and approaches for the development of wearable health-monitoring and human-machine interfaces.

  11. Monitoring large rotating machines at EDF

    International Nuclear Information System (INIS)

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

    1992-09-01

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

  12. Moved range monitor of a refueling machine

    International Nuclear Information System (INIS)

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

    1976-01-01

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

  13. Improved thermal monitoring of rotating machine insulation

    International Nuclear Information System (INIS)

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

    1991-01-01

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

  14. Integrating Oil Debris and Vibration Measurements for Intelligent Machine Health Monitoring. Degree awarded by Toledo Univ., May 2002

    Science.gov (United States)

    Dempsey, Paula J.

    2003-01-01

    A diagnostic tool for detecting damage to gears was developed. Two different measurement technologies, oil debris analysis and vibration were integrated into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual measurement technologies. This diagnostic tool was developed and evaluated experimentally by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Rig. An oil debris sensor and the two vibration algorithms were adapted as the diagnostic tools. An inductance type oil debris sensor was selected for the oil analysis measurement technology. Gear damage data for this type of sensor was limited to data collected in the NASA Glenn test rigs. For this reason, this analysis included development of a parameter for detecting gear pitting damage using this type of sensor. The vibration data was used to calculate two previously available gear vibration diagnostic algorithms. The two vibration algorithms were selected based on their maturity and published success in detecting damage to gears. Oil debris and vibration features were then developed using fuzzy logic analysis techniques, then input into a multi sensor data fusion process. Results show combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spur gears. As a result of this research, this new diagnostic tool has significantly improved detection of gear damage in the NASA Glenn Spur Gear Fatigue Rigs. This research also resulted in several other findings that will improve the development of future health monitoring systems. Oil debris analysis was found to be more reliable than vibration analysis for detecting pitting fatigue failure of gears and is capable of indicating damage progression. Also, some vibration algorithms are as sensitive to operational effects as they

  15. Monitoring of large rotating machines at EDF

    International Nuclear Information System (INIS)

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

    1993-09-01

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

  16. Beam Loss Monitoring for LHC Machine Protection

    Science.gov (United States)

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

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

  17. Monitoring machining conditions by infrared images

    Science.gov (United States)

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

    2001-03-01

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

  18. Monitoring coordinate measuring machines by calibrated parts

    International Nuclear Information System (INIS)

    Weckenmann, A; Lorz, J

    2005-01-01

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

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

    Science.gov (United States)

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

    2018-06-01

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

  20. Indirect Tire Monitoring System - Machine Learning Approach

    Science.gov (United States)

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

    2017-10-01

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

  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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  3. Augmented fish health monitoring

    International Nuclear Information System (INIS)

    Michak, P.; Rogers, R.; Amos, K.

    1991-05-01

    The Bonneville Power Administration (BPA) initiated the Augmented Fish Health Monitoring project in 1986. This project was a five year interagency project involving fish rearing agencies in the Columbia Basin. Historically, all agencies involved with fish health in the Columbia Basin were conducting various levels of fish health monitoring, pathogen screening and collection. The goals of this project were; to identify, develop and implement a standardized level of fish health methodologies, develop a common data collection and reporting format in the area of artificial production, evaluate and monitor water quality, improve communications between agencies and provide annual evaluation of fish health information for production of healthier smolts. This completion report will contain a project evaluation, review of the goals of the project, evaluation of the specific fish health analyses, an overview of highlights of the project and concluding remarks. 8 refs., 1 fig., 4 tabs

  4. Air quality monitoring using mobile microscopy and machine learning

    KAUST Repository

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

    2017-01-01

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

  5. 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.

  6. Machine monitoring via current signature analysis techniques

    International Nuclear Information System (INIS)

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

    1992-01-01

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

  7. Beam loss monitor system for machine protection

    CERN Document Server

    Dehning, B

    2005-01-01

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

  8. Monitoring machining conditions by analyzing cutting force vibration

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-09-15

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

  9. Monitoring machining conditions by analyzing cutting force vibration

    International Nuclear Information System (INIS)

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

    2015-01-01

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

  10. Lunar Health Monitor (LHM)

    Science.gov (United States)

    Lisy, Frederick J.

    2015-01-01

    Orbital Research, Inc., has developed a low-profile, wearable sensor suite for monitoring astronaut health in both intravehicular and extravehicular activities. The Lunar Health Monitor measures respiration, body temperature, electrocardiogram (EKG) heart rate, and other cardiac functions. Orbital Research's dry recording electrode is central to the innovation and can be incorporated into garments, eliminating the need for conductive pastes, adhesives, or gels. The patented dry recording electrode has been approved by the U.S. Food and Drug Administration. The LHM is easily worn under flight gear or with civilian clothing, making the system completely versatile for applications where continuous physiological monitoring is needed. During Phase II, Orbital Research developed a second-generation LHM that allows sensor customization for specific monitoring applications and anatomical constraints. Evaluations included graded exercise tests, lunar mission task simulations, functional battery tests, and resting measures. The LHM represents the successful integration of sensors into a wearable platform to capture long-duration and ambulatory physiological markers.

  11. A machine protection beam position monitor system

    International Nuclear Information System (INIS)

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

    1998-01-01

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

  12. Detecting System of Nested Hardware Virtual Machine Monitor

    Directory of Open Access Journals (Sweden)

    Artem Vladimirovich Iuzbashev

    2015-03-01

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

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

    International Nuclear Information System (INIS)

    Kanemoto, Shigeru

    2010-01-01

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

  14. Behavioral Modeling for Mental Health using Machine Learning Algorithms.

    Science.gov (United States)

    Srividya, M; Mohanavalli, S; Bhalaji, N

    2018-04-03

    Mental health is an indicator of emotional, psychological and social well-being of an individual. It determines how an individual thinks, feels and handle situations. Positive mental health helps one to work productively and realize their full potential. Mental health is important at every stage of life, from childhood and adolescence through adulthood. Many factors contribute to mental health problems which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, and personality disorders. It is becoming increasingly important to determine the onset of the mental illness to maintain proper life balance. The nature of machine learning algorithms and Artificial Intelligence (AI) can be fully harnessed for predicting the onset of mental illness. Such applications when implemented in real time will benefit the society by serving as a monitoring tool for individuals with deviant behavior. This research work proposes to apply various machine learning algorithms such as support vector machines, decision trees, naïve bayes classifier, K-nearest neighbor classifier and logistic regression to identify state of mental health in a target group. The responses obtained from the target group for the designed questionnaire were first subject to unsupervised learning techniques. The labels obtained as a result of clustering were validated by computing the Mean Opinion Score. These cluster labels were then used to build classifiers to predict the mental health of an individual. Population from various groups like high school students, college students and working professionals were considered as target groups. The research presents an analysis of applying the aforementioned machine learning algorithms on the target groups and also suggests directions for future work.

  15. System health monitoring

    International Nuclear Information System (INIS)

    Reneke, J.A.; Fryer, M.O.

    1995-01-01

    Well designed large systems include many instrument taking data. These data are used in a variety of ways. They are used to control the system and its components, to monitor system and component health, and often for historical or financial purposes. This paper discusses a new method of using data from low level instrumentation to monitor system and component health. The method uses the covariance of instrument outputs to calculate a measure of system change. The method involves no complicated modeling since it is not a parameter estimation algorithm. The method is iterative and can be implemented on a computer in real time. Examples are presented for a metal lathe and a high efficiency particulate air (HEPA) filter. It is shown that the proposed method is quite sensitive to system changes such as wear out and failure. The method is useful for low level system diagnostics and fault detection

  16. 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

  17. Interpreting streaming biosignals : in search of best approaches to augmenting mobile health monitoring with machine learning for adaptive clinical decision support

    NARCIS (Netherlands)

    Jones, Valerie M.; Mendes Batista, R.J.; Bults, Richard G.A.; op den Akker, Harm; Widya, I.A.; Hermens, Hermanus J.; Huis in 't Veld, M.H.A.; Tönis, Thijs; Tonis, T.; Vollenbroek-Hutten, Miriam Marie Rosé

    2011-01-01

    We investigate Body Area Networks for ambulant patient monitoring. As well as sensing physiological parameters, BAN applications may provide feedback to patients. Automating formulation of feedback requires realtime analysis and interpretation of streaming biosignals and other context and knowledge

  18. 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.

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

    Science.gov (United States)

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

    2018-05-01

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

  20. 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

  1. 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.

  2. Lunar Health Monitor, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — During the Phase II Lunar Health Monitor program, Orbital Research will develop a second generation wearable sensor suite for astronaut physiologic monitoring. The...

  3. Ultrasonic wireless health monitoring

    Science.gov (United States)

    Petit, Lionel; Lefeuvre, Elie; Guyomar, Daniel; Richard, Claude; Guy, Philippe; Yuse, Kaori; Monnier, Thomas

    2006-03-01

    The integration of autonomous wireless elements in health monitoring network increases the reliability by suppressing power supplies and data transmission wiring. Micro-power piezoelectric generators are an attractive alternative to primary batteries which are limited by a finite amount of energy, a limited capacity retention and a short shelf life (few years). Our goal is to implement such an energy harvesting system for powering a single AWT (Autonomous Wireless Transmitter) using our SSH (Synchronized Switch Harvesting) method. Based on a non linear process of the piezoelement voltage, this SSH method optimizes the energy extraction from the mechanical vibrations. This AWT has two main functions : The generation of an identifier code by RF transmission to the central receiver and the Lamb wave generation for the health monitoring of the host structure. A damage index is derived from the variation between the transmitted wave spectrum and a reference spectrum. The same piezoelements are used for the energy harvesting function and the Lamb wave generation, thus reducing mass and cost. A micro-controller drives the energy balance and synchronizes the functions. Such an autonomous transmitter has been evaluated on a 300x50x2 mm 3 composite cantilever beam. Four 33x11x0.3 mm 3 piezoelements are used for the energy harvesting and for the wave lamb generation. A piezoelectric sensor is placed at the free end of the beam to track the transmitted Lamb wave. In this configuration, the needed energy for the RF emission is 0.1 mJ for a 1 byte-information and the Lamb wave emission requires less than 0.1mJ. The AWT can harvested an energy quantity of approximately 20 mJ (for a 1.5 Mpa lateral stress) with a 470 μF storage capacitor. This corresponds to a power density near to 6mW/cm 3. The experimental AWT energy abilities are presented and the damage detection process is discussed. Finally, some envisaged solutions are introduced for the implementation of the required data

  4. Wireless Monitoring of Induction Machine Rotor Physical Variables

    Directory of Open Access Journals (Sweden)

    Jefferson Doolan Fernandes

    2017-11-01

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

  5. Wireless Monitoring of Induction Machine Rotor Physical Variables.

    Science.gov (United States)

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

    2017-11-18

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

  6. Structural health monitoring an advanced signal processing perspective

    CERN Document Server

    Chen, Xuefeng; Mukhopadhyay, Subhas

    2017-01-01

    This book highlights the latest advances and trends in advanced signal processing (such as wavelet theory, time-frequency analysis, empirical mode decomposition, compressive sensing and sparse representation, and stochastic resonance) for structural health monitoring (SHM). Its primary focus is on the utilization of advanced signal processing techniques to help monitor the health status of critical structures and machines encountered in our daily lives: wind turbines, gas turbines, machine tools, etc. As such, it offers a key reference guide for researchers, graduate students, and industry professionals who work in the field of SHM.

  7. Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

    Science.gov (United States)

    Yu, Jianbo

    2015-12-01

    Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

  8. Machine-Learning Algorithms to Code Public Health Spending Accounts.

    Science.gov (United States)

    Brady, Eoghan S; Leider, Jonathon P; Resnick, Beth A; Alfonso, Y Natalia; Bishai, David

    Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence-based public health resource allocation.

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

    Science.gov (United States)

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

    2010-05-01

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

  10. 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.

  11. Structural health monitoring of wind turbine blades

    Science.gov (United States)

    Rumsey, Mark A.; Paquette, Joshua A.

    2008-03-01

    As electric utility wind turbines increase in size, and correspondingly, increase in initial capital investment cost, there is an increasing need to monitor the health of the structure. Acquiring an early indication of structural or mechanical problems allows operators to better plan for maintenance, possibly operate the machine in a de-rated condition rather than taking the unit off-line, or in the case of an emergency, shut the machine down to avoid further damage. This paper describes several promising structural health monitoring (SHM) techniques that were recently exercised during a fatigue test of a 9 meter glass-epoxy and carbon-epoxy wind turbine blade. The SHM systems were implemented by teams from NASA Kennedy Space Center, Purdue University and Virginia Tech. A commercial off-the-shelf acoustic emission (AE) NDT system gathered blade AE data throughout the test. At a fatigue load cycle rate around 1.2 Hertz, and after more than 4,000,000 fatigue cycles, the blade was diagnostically and visibly failing at the out-board blade spar-cap termination point at 4.5 meters. For safety reasons, the test was stopped just before the blade completely failed. This paper provides an overview of the SHM and NDT system setups and some current test results.

  12. A VME based health monitoring system

    International Nuclear Information System (INIS)

    Huang Yiming; Wang Chunhong

    2011-01-01

    It introduces a VME based health system for monitoring the working status of VME crates in the BEPCⅡ. It consists of a PC and a VME crate where a CMM (Classic Monitor System) is installed. The CMM module is responsible for collecting data from the power supply and temperature as well as fan speed inside the VME crate and send these data to the PC via the serial port. The author developed EPICS asynchronous driver by using a character-based device protocol StreamDevice. The data is saved into EPICS IOC database in character. Man-machine interface which is designed by BOY displays the running status of the VME crate including the power supply and temperature as well as fan speed. If the value of records display unusual, the color of the value will be changed into red. This can facilitate the maintenance of the VME crates. (authors)

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

    Directory of Open Access Journals (Sweden)

    Ismoyo Haryanto

    2013-07-01

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

  14. A deviation based assessment methodology for multiple machine health patterns classification and fault detection

    Science.gov (United States)

    Jia, Xiaodong; Jin, Chao; Buzza, Matt; Di, Yuan; Siegel, David; Lee, Jay

    2018-01-01

    Successful applications of Diffusion Map (DM) in machine failure detection and diagnosis have been reported in several recent studies. DM provides an efficient way to visualize the high-dimensional, complex and nonlinear machine data, and thus suggests more knowledge about the machine under monitoring. In this paper, a DM based methodology named as DM-EVD is proposed for machine degradation assessment, abnormality detection and diagnosis in an online fashion. Several limitations and challenges of using DM for machine health monitoring have been analyzed and addressed. Based on the proposed DM-EVD, a deviation based methodology is then proposed to include more dimension reduction methods. In this work, the incorporation of Laplacian Eigen-map and Principal Component Analysis (PCA) are explored, and the latter algorithm is named as PCA-Dev and is validated in the case study. To show the successful application of the proposed methodology, case studies from diverse fields are presented and investigated in this work. Improved results are reported by benchmarking with other machine learning algorithms.

  15. [Monitoring social determinants of health].

    Science.gov (United States)

    Espelt, Albert; Continente, Xavier; Domingo-Salvany, Antonia; Domínguez-Berjón, M Felicitas; Fernández-Villa, Tania; Monge, Susana; Ruiz-Cantero, M Teresa; Perez, Glòria; Borrell, Carme

    2016-11-01

    Public health surveillance is the systematic and continuous collection, analysis, dissemination and interpretation of health-related data for planning, implementation and evaluation of public health initiatives. Apart from the health system, social determinants of health include the circumstances in which people are born, grow up, live, work and age, and they go a long way to explaining health inequalities. A surveillance system of the social determinants of health requires a comprehensive and social overview of health. This paper analyses the importance of monitoring social determinants of health and health inequalities, and describes some relevant aspects concerning the implementation of surveillance during the data collection, compilation and analysis phases, as well as dissemination of information and evaluation of the surveillance system. It is important to have indicators from sources designed for this purpose, such as continuous records or periodic surveys, explicitly describing its limitations and strengths. The results should be published periodically in a communicative format that both enhances the public's ability to understand the problems that affect them, whilst at the same time empowering the population, with the ultimate goal of guiding health-related initiatives at different levels of intervention. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

  16. On the Impossibility of Detecting Virtual Machine Monitors

    Science.gov (United States)

    Gueron, Shay; Seifert, Jean-Pierre

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

  17. Wearable sensors for health monitoring

    Science.gov (United States)

    Suciu, George; Butca, Cristina; Ochian, Adelina; Halunga, Simona

    2015-02-01

    In this paper we describe several wearable sensors, designed for monitoring the health condition of the patients, based on an experimental model. Wearable sensors enable long-term continuous physiological monitoring, which is important for the treatment and management of many chronic illnesses, neurological disorders, and mental health issues. The system is based on a wearable sensors network, which is connected to a computer or smartphone. The wearable sensor network integrates several wearable sensors that can measure different parameters such as body temperature, heart rate and carbon monoxide quantity from the air. After the portable sensors measuring parameter values, they are transmitted by microprocessor through the Bluetooth to the application developed on computer or smartphone, to be interpreted.

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

    International Nuclear Information System (INIS)

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

    1994-01-01

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

  19. A Machine-to-Machine protocol benchmark for eHealth applications - Use case: Respiratory rehabilitation.

    Science.gov (United States)

    Talaminos-Barroso, Alejandro; Estudillo-Valderrama, Miguel A; Roa, Laura M; Reina-Tosina, Javier; Ortega-Ruiz, Francisco

    2016-06-01

    M2M (Machine-to-Machine) communications represent one of the main pillars of the new paradigm of the Internet of Things (IoT), and is making possible new opportunities for the eHealth business. Nevertheless, the large number of M2M protocols currently available hinders the election of a suitable solution that satisfies the requirements that can demand eHealth applications. In the first place, to develop a tool that provides a benchmarking analysis in order to objectively select among the most relevant M2M protocols for eHealth solutions. In the second place, to validate the tool with a particular use case: the respiratory rehabilitation. A software tool, called Distributed Computing Framework (DFC), has been designed and developed to execute the benchmarking tests and facilitate the deployment in environments with a large number of machines, with independence of the protocol and performance metrics selected. DDS, MQTT, CoAP, JMS, AMQP and XMPP protocols were evaluated considering different specific performance metrics, including CPU usage, memory usage, bandwidth consumption, latency and jitter. The results obtained allowed to validate a case of use: respiratory rehabilitation of chronic obstructive pulmonary disease (COPD) patients in two scenarios with different types of requirement: Home-Based and Ambulatory. The results of the benchmark comparison can guide eHealth developers in the choice of M2M technologies. In this regard, the framework presented is a simple and powerful tool for the deployment of benchmark tests under specific environments and conditions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    S. Gallina

    2010-06-01

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

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

    Science.gov (United States)

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

    2018-03-01

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

  2. Machine learning methods for clinical forms analysis in mental health.

    Science.gov (United States)

    Strauss, John; Peguero, Arturo Martinez; Hirst, Graeme

    2013-01-01

    In preparation for a clinical information system implementation, the Centre for Addiction and Mental Health (CAMH) Clinical Information Transformation project completed multiple preparation steps. An automated process was desired to supplement the onerous task of manual analysis of clinical forms. We used natural language processing (NLP) and machine learning (ML) methods for a series of 266 separate clinical forms. For the investigation, documents were represented by feature vectors. We used four ML algorithms for our examination of the forms: cluster analysis, k-nearest neigh-bours (kNN), decision trees and support vector machines (SVM). Parameters for each algorithm were optimized. SVM had the best performance with a precision of 64.6%. Though we did not find any method sufficiently accurate for practical use, to our knowledge this approach to forms has not been used previously in mental health.

  3. 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.

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

    Directory of Open Access Journals (Sweden)

    Tiziana Segreto

    2017-12-01

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

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

    Science.gov (United States)

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

    2017-12-12

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

  6. Discovery machines accelerators for science, technology, health and innovation

    CERN Document Server

    Australian Academy of Sciences

    2016-01-01

    Discovery machines: Accelerators for science, technology, health and innovation explores the science of particle accelerators, the machines that supercharge our ability to discover the secrets of nature and have opened up new tools in medicine, energy, manufacturing, and the environment as well as in pure research. Particle accelerators are now an essential ingredient in discovery science because they offer new ways to analyse the world, such as by probing objects with high energy x-rays or colliding them beams of electrons. They also have a huge—but often unnoticed—impact on all our lives; medical imaging, cancer treatment, new materials and even the chips that power our phones and computers have all been transformed by accelerators of various types. Research accelerators also provide fundamental infrastructure that encourages better collaboration between international and domestic scientists, organisations and governments.

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

    DEFF Research Database (Denmark)

    Wang, Chao; Liu, Hui; Liu, Xiao

    2015-01-01

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

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

    CSIR Research Space (South Africa)

    Heyns, T

    2012-12-01

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

  9. Forest health monitoring: 2008 national technical report

    Science.gov (United States)

    Kevin M. Potter; Barbara L. Conkling

    2012-01-01

    The Forest Health Monitoring (FHM) Program’s annual national technical report has three objectives: (1) to present forest health status and trends from a national or a multi-State regional perspective using a variety of sources, (2) to introduce new techniques for analyzing forest health data, and (3) to report results of recently completed evaluation monitoring...

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1998-12-31

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1997-12-31

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

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

    International Nuclear Information System (INIS)

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

    2008-01-01

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

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

    OpenAIRE

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

    2015-01-01

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

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

    OpenAIRE

    Abbas, JK

    2013-01-01

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

  15. 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.

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

  17. Networked Biomedical System for Ubiquitous Health Monitoring

    Directory of Open Access Journals (Sweden)

    Arjan Durresi

    2008-01-01

    Full Text Available We propose a distributed system that enables global and ubiquitous health monitoring of patients. The biomedical data will be collected by wearable health diagnostic devices, which will include various types of sensors and will be transmitted towards the corresponding Health Monitoring Centers. The permanent medical data of patients will be kept in the corresponding Home Data Bases, while the measured biomedical data will be sent to the Visitor Health Monitor Center and Visitor Data Base that serves the area of present location of the patient. By combining the measured biomedical data and the permanent medical data, Health Medical Centers will be able to coordinate the needed actions and help the local medical teams to make quickly the best decisions that could be crucial for the patient health, and that can reduce the cost of health service.

  18. Forest health monitoring: 2007 national technical report

    Science.gov (United States)

    Barbara L. Conkling

    2011-01-01

    The Forest Health Monitoring Program produces an annual technical report that has two main objectives. The first objective is to present information about forest health from a national perspective. The second objective is to present examples of useful techniques for analyzing forest health data new to the annual national reports and new applications of techniques...

  19. Introduction to:Forest health monitoring program

    Science.gov (United States)

    Mark J. Ambrose

    2009-01-01

    This annual technical report is a product of the Forest Health Monitoring (FHM) Program. The report provides information about a variety of issues relating to forest health at a national scale. FHM national reports have the dual focus of presenting analyses of the latest available data and showcasing innovative techniques for analyzing forest health data. The report is...

  20. Forest health monitoring: 2009 national technical report

    Science.gov (United States)

    Kevin M. Potter; Barbara L. Conkling

    2012-01-01

    The annual national technical report of the Forest Health Monitoring Program of the Forest Service, U.S. Department of Agriculture, presents forest health status and trends from a national or multi-State regional perspective using a variety of sources, introduces new techniques for analyzing forest health data, and summarizes results of recently completed Evaluation...

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

    Science.gov (United States)

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

    2002-06-01

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

  2. Machine learning approaches to the social determinants of health in the health and retirement study.

    Science.gov (United States)

    Seligman, Benjamin; Tuljapurkar, Shripad; Rehkopf, David

    2018-04-01

    Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks. All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data ( R 2 between 0.4-0.6, versus learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender. Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.

  3. 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.

  4. Wearable Health Monitoring Systems, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The objective of this proposal is to demonstrate the feasibility of producing a wearable health monitoring system for the human body that is functional, comfortable,...

  5. Wearable Health Monitoring Systems, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — The objective of this proposal is to demonstrate the feasibility of producing a wearable health monitoring system for the human body that is functional, comfortable,...

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

    Science.gov (United States)

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

    2017-12-01

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

  7. Context aware sensing for health monitoring

    NARCIS (Netherlands)

    Landete, F.; Chen, W.; Bouwstra, S.; Feijs, L.M.G.; Bambang Oetomo, S.

    2012-01-01

    Health Monitoring systems with textile sensors offer more comfort compared to gel electrodes, however they tend to suffer from poor skin contact and motion artifacts. In order to improve the monitoring reliability, we propose to apply multiple sensors and context aware sensing. A context aware

  8. Activity monitoring systems in health care

    NARCIS (Netherlands)

    Kröse, B.; van Oosterhout, T.; van Kasteren, T.; Salah, A.A.; Gevers, T.

    2011-01-01

    This chapter focuses on activity monitoring in a home setting for health care purposes. First the most current sensing systems are described, which consist of wearable and ambient sensors. Then several approaches for the monitoring of simple actions are discussed, like falls or therapies. After

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

    OpenAIRE

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

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Dimitrov Vasil

    2017-01-01

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

  11. Big Data in Public Health: Terminology, Machine Learning, and Privacy.

    Science.gov (United States)

    Mooney, Stephen J; Pejaver, Vikas

    2018-04-01

    The digital world is generating data at a staggering and still increasing rate. While these "big data" have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores several key issues that have arisen around big data. First, we propose a taxonomy of sources of big data to clarify terminology and identify threads common across some subtypes of big data. Next, we consider common public health research and practice uses for big data, including surveillance, hypothesis-generating research, and causal inference, while exploring the role that machine learning may play in each use. We then consider the ethical implications of the big data revolution with particular emphasis on maintaining appropriate care for privacy in a world in which technology is rapidly changing social norms regarding the need for (and even the meaning of) privacy. Finally, we make suggestions regarding structuring teams and training to succeed in working with big data in research and practice.

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

    Science.gov (United States)

    Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk

    2018-04-06

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

  13. Hybrid Modeling Improves Health and Performance Monitoring

    Science.gov (United States)

    2007-01-01

    Scientific Monitoring Inc. was awarded a Phase I Small Business Innovation Research (SBIR) project by NASA's Dryden Flight Research Center to create a new, simplified health-monitoring approach for flight vehicles and flight equipment. The project developed a hybrid physical model concept that provided a structured approach to simplifying complex design models for use in health monitoring, allowing the output or performance of the equipment to be compared to what the design models predicted, so that deterioration or impending failure could be detected before there would be an impact on the equipment's operational capability. Based on the original modeling technology, Scientific Monitoring released I-Trend, a commercial health- and performance-monitoring software product named for its intelligent trending, diagnostics, and prognostics capabilities, as part of the company's complete ICEMS (Intelligent Condition-based Equipment Management System) suite of monitoring and advanced alerting software. I-Trend uses the hybrid physical model to better characterize the nature of health or performance alarms that result in "no fault found" false alarms. Additionally, the use of physical principles helps I-Trend identify problems sooner. I-Trend technology is currently in use in several commercial aviation programs, and the U.S. Air Force recently tapped Scientific Monitoring to develop next-generation engine health-management software for monitoring its fleet of jet engines. Scientific Monitoring has continued the original NASA work, this time under a Phase III SBIR contract with a joint NASA-Pratt & Whitney aviation security program on propulsion-controlled aircraft under missile-damaged aircraft conditions.

  14. Structural Health Monitoring with Fiber Bragg Grating and Piezo Arrays

    Science.gov (United States)

    Black, Richard J.; Faridian, Ferey; Moslehi, Behzad; Sotoudeh, Vahid

    2012-01-01

    Structural health monitoring (SHM) is one of the most important tools available for the maintenance, safety, and integrity of aerospace structural systems. Lightweight, electromagnetic-interference- immune, fiber-optic sensor-based SHM will play an increasing role in more secure air transportation systems. Manufacturers and maintenance personnel have pressing needs for significantly improving safety and reliability while providing for lower inspection and maintenance costs. Undetected or untreated damage may grow and lead to catastrophic structural failure. Damage can originate from the strain/stress history of the material, imperfections of domain boundaries in metals, delamination in multi-layer materials, or the impact of machine tools in the manufacturing process. Damage can likewise develop during service life from wear and tear, or under extraordinary circumstances such as with unusual forces, temperature cycling, or impact of flying objects. Monitoring and early detection are key to preventing a catastrophic failure of structures, especially when these are expected to perform near their limit conditions.

  15. Optical Structural Health Monitoring Device

    Science.gov (United States)

    Buckner, Benjamin D.; Markov, Vladimir; Earthman, James C.

    2010-01-01

    This non-destructive, optical fatigue detection and monitoring system relies on a small and unobtrusive light-scattering sensor that is installed on a component at the beginning of its life in order to periodically scan the component in situ. The method involves using a laser beam to scan the surface of the monitored component. The device scans a laser spot over a metal surface to which it is attached. As the laser beam scans the surface, disruptions in the surface cause increases in scattered light intensity. As the disruptions in the surface grow, they will cause the light to scatter more. Over time, the scattering intensities over the scanned line can be compared to detect changes in the metal surface to find cracks, crack precursors, or corrosion. This periodic monitoring of the surface can be used to indicate the degree of fatigue damage on a component and allow one to predict the remaining life and/or incipient mechanical failure of the monitored component. This wireless, compact device can operate for long periods under its own battery power and could one day use harvested power. The prototype device uses the popular open-source TinyOS operating system on an off-the-shelf Mica2 sensor mote, which allows wireless command and control through dynamically reconfigurable multi-node sensor networks. The small size and long life of this device could make it possible for the nodes to be installed and left in place over the course of years, and with wireless communication, data can be extracted from the nodes by operators without physical access to the devices. While a prototype has been demonstrated at the time of this reporting, further work is required in the system s development to take this technology into the field, especially to improve its power management and ruggedness. It should be possible to reduce the size and sensitivity as well. Establishment of better prognostic methods based on these data is also needed. The increase of surface roughness with

  16. Advanced health monitor for automated driving functions

    OpenAIRE

    Mikovski Iotov, I.

    2017-01-01

    There is a trend in the automotive domain where driving functions are taken from the driver by automated driving functions. In order to guarantee the correct behavior of these auto-mated driving functions, the report introduces an Advanced Health Monitor that uses Tem-poral Logic and Probabilistic Analysis to indicate the system’s health.

  17. Introduction to: The Forest Health monitoring program

    Science.gov (United States)

    Barbara L. Conkling

    2011-01-01

    The National Forest Health Monitoring (FHM) Program of the Forest Service, U.S. Department of Agriculture, produces an annual technical report on forest health as one of its products. The report is organized using the Criteria and Indicators for the Conservation and Sustainable Management of Temperate and Boreal Forests (Montréal Process Working Group 2007) as a...

  18. Forest health monitoring: 2005 national technical report

    Science.gov (United States)

    Mark J. Ambrose; Barbara L. Conkling

    2007-01-01

    The Forest Health Monitoring program's annual national technical report presents results of forest health analyses from a national perspective using data from a variety of sources. The report is organized according to the Criteria and Indicators for the Conservation and Sustainable Management of Temperate and Boreal Forests of the Santiago Declaration. The results...

  19. Forest health monitoring: 2006 national technical report

    Science.gov (United States)

    Mark J. Ambrose; Barbara L. Conkling

    2009-01-01

    The Forest Health Monitoring Program’s annual national technical report presents results of forest health analyses from a national perspective using data from a variety of sources. The report is organized according to the Criteria and Indicators for the Conservation and Sustainable Management of Temperate and Boreal Forests of the...

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-01-15

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

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

    International Nuclear Information System (INIS)

    Saari, J.; Lakio, A.

    2009-01-01

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

  2. In situ health monitoring of piezoelectric sensors

    Science.gov (United States)

    Jensen, Scott L. (Inventor); Drouant, George J. (Inventor)

    2013-01-01

    An in situ health monitoring apparatus may include an exciter circuit that applies a pulse to a piezoelectric transducer and a data processing system that determines the piezoelectric transducer's dynamic response to the first pulse. The dynamic response can be used to evaluate the operating range, health, and as-mounted resonance frequency of the transducer, as well as the strength of a coupling between the transducer and a structure and the health of the structure.

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

  5. 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.

  6. 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.

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

    Science.gov (United States)

    Fachrurrozi, Muhammad; Saparudin; Erwin

    2017-04-01

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

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

    International Nuclear Information System (INIS)

    Kim, I.S.

    1992-02-01

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

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

    International Nuclear Information System (INIS)

    Iorga, C.; Iorga, H.

    2016-01-01

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

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

    International Nuclear Information System (INIS)

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

    2001-01-01

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

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

    Science.gov (United States)

    Kunze, Rouwen; König, Niels; Schmitt, Robert

    2017-06-01

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

  12. Design of wearable health monitoring device

    Science.gov (United States)

    Devara, Kresna; Ramadhanty, Savira; Abuzairi, Tomy

    2018-02-01

    Wearable smart health monitoring devices have attracted considerable attention in both research community and industry. Some of the causes are the increasing healthcare costs, along with the growing technology. To address this demand, in this paper, design and evaluation of wearable health monitoring device integrated with smartphone were presented. This device was designed for patients in need of constant health monitoring. The performance of the proposed design has been tested by conducting measurement once in 2 minutes for 10 minutes to obtain heart rate and body temperature data. The comparation between data measured by the proposed device and that measured by the reference device yields only an average error of 1.45% for heart rate and 1.04% for body temperature.

  13. Assessing the value of structural health monitoring

    DEFF Research Database (Denmark)

    Thöns, S.; Faber, Michael Havbro

    2013-01-01

    Structural Health Monitoring (SHM) systems are designed for assisting owners and operators with information and forecasts concerning the fitness for purpose of structures and building systems. The benefit associated with the implementation of SHM may in some cases be intuitively anticipated...... as their responses and performances over their life-cycle. In addition, the quality of monitoring and the performance of possible remedial actions triggered by monitoring results are modeled probabilistically.The consequences accounted for, in principle include all consequences associated with the performance...

  14. Acoustic Techniques for Structural Health Monitoring

    Science.gov (United States)

    Frankenstein, B.; Augustin, J.; Hentschel, D.; Schubert, F.; Köhler, B.; Meyendorf, N.

    2008-02-01

    Future safety and maintenance strategies for industrial components and vehicles are based on combinations of monitoring systems that are permanently attached to or embedded in the structure, and periodic inspections. The latter belongs to conventional nondestructive evaluation (NDE) and can be enhanced or partially replaced by structural health monitoring systems. However, the main benefit of this technology for the future will consist of systems that can be differently designed based on improved safety philosophies, including continuous monitoring. This approach will increase the efficiency of inspection procedures at reduced inspection times. The Fraunhofer IZFP Dresden Branch has developed network nodes, miniaturized transmitter and receiver systems for active and passive acoustical techniques and sensor systems that can be attached to or embedded into components or structures. These systems have been used to demonstrate intelligent sensor networks for the monitoring of aerospace structures, railway systems, wind energy generators, piping system and other components. Material discontinuities and flaws have been detected and monitored during full scale fatigue testing. This paper will discuss opportunities and future trends in nondestructive evaluation and health monitoring based on new sensor principles and advanced microelectronics. It will outline various application examples of monitoring systems based on acoustic techniques and will indicate further needs for research and development.

  15. Mobile health monitoring system for community health workers

    CSIR Research Space (South Africa)

    Sibiya, G

    2014-09-01

    Full Text Available of hypertension as it provides real time information and eliminates the need to visit a healthcare facility to take blood pressure readings. Our proposed mobile health monitoring system enables faster computerization of data that has been recorded... pressure, heart rate and glucose readings. These reading closely related to most common NCDs. D. Feedback to health worker and the subject of care Community health workers are often not professionally trained on health. As a result they are not expected...

  16. 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.

  17. 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.

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

    KAUST Repository

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

    2016-01-01

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

  19. Privacy by design in personal health monitoring.

    Science.gov (United States)

    Nordgren, Anders

    2015-06-01

    The concept of privacy by design is becoming increasingly popular among regulators of information and communications technologies. This paper aims at analysing and discussing the ethical implications of this concept for personal health monitoring. I assume a privacy theory of restricted access and limited control. On the basis of this theory, I suggest a version of the concept of privacy by design that constitutes a middle road between what I call broad privacy by design and narrow privacy by design. The key feature of this approach is that it attempts to balance automated privacy protection and autonomously chosen privacy protection in a way that is context-sensitive. In personal health monitoring, this approach implies that in some contexts like medication assistance and monitoring of specific health parameters one single automatic option is legitimate, while in some other contexts, for example monitoring in which relatives are receivers of health-relevant information rather than health care professionals, a multi-choice approach stressing autonomy is warranted.

  20. Machine and Woodworking Tool Safety. Module SH-24. Safety and Health.

    Science.gov (United States)

    Center for Occupational Research and Development, Inc., Waco, TX.

    This student module on machine and woodworking tool safety is one of 50 modules concerned with job safety and health. This module discusses specific practices and precautions concerned with the efficient operation and use of most machine and woodworking tools in use today. Following the introduction, 13 objectives (each keyed to a page in the…

  1. Bridge Health Monitoring Using a Machine Learning Strategy

    Science.gov (United States)

    2017-01-01

    The goal of this project was to cast the SHM problem within a statistical pattern recognition framework. Techniques borrowed from speaker recognition, particularly speaker verification, were used as this discipline deals with problems very similar to...

  2. Wearable sensors for human health monitoring

    Science.gov (United States)

    Asada, H. Harry; Reisner, Andrew

    2006-03-01

    Wearable sensors for continuous monitoring of vital signs for extended periods of weeks or months are expected to revolutionize healthcare services in the home and workplace as well as in hospitals and nursing homes. This invited paper describes recent research progress in wearable health monitoring technology and its clinical applications, with emphasis on blood pressure and circulatory monitoring. First, a finger ring-type wearable blood pressure sensor based on photo plethysmogram is presented. Technical issues, including motion artifact reduction, power saving, and wearability enhancement, will be addressed. Second, sensor fusion and sensor networking for integrating multiple sensors with diverse modalities will be discussed for comprehensive monitoring and diagnosis of health status. Unlike traditional snap-shot measurements, continuous monitoring with wearable sensors opens up the possibility to treat the physiological system as a dynamical process. This allows us to apply powerful system dynamics and control methodologies, such as adaptive filtering, single- and multi-channel system identification, active noise cancellation, and adaptive control, to the monitoring and treatment of highly complex physiological systems. A few clinical trials illustrate the potentials of the wearable sensor technology for future heath care services.

  3. Intelligent Control and Health Monitoring. Chapter 3

    Science.gov (United States)

    Garg, Sanjay; Kumar, Aditya; Mathews, H. Kirk; Rosenfeld, Taylor; Rybarik, Pavol; Viassolo, Daniel E.

    2009-01-01

    Advanced model-based control architecture overcomes the limitations state-of-the-art engine control and provides the potential of virtual sensors, for example for thrust and stall margin. "Tracking filters" are used to adapt the control parameters to actual conditions and to individual engines. For health monitoring standalone monitoring units will be used for on-board analysis to determine the general engine health and detect and isolate sudden faults. Adaptive models open up the possibility of adapting the control logic to maintain desired performance in the presence of engine degradation or to accommodate any faults. Improved and new sensors are required to allow sensing at stations within the engine gas path that are currently not instrumented due in part to the harsh conditions including high operating temperatures and to allow additional monitoring of vibration, mass flows and energy properties, exhaust gas composition, and gas path debris. The environmental and performance requirements for these sensors are summarized.

  4. Advanced health monitor for automated driving functions

    NARCIS (Netherlands)

    Mikovski Iotov, I.

    2017-01-01

    There is a trend in the automotive domain where driving functions are taken from the driver by automated driving functions. In order to guarantee the correct behavior of these auto-mated driving functions, the report introduces an Advanced Health Monitor that uses Tem-poral Logic and Probabilistic

  5. Wearable Sensors for Remote Health Monitoring.

    Science.gov (United States)

    Majumder, Sumit; Mondal, Tapas; Deen, M Jamal

    2017-01-12

    Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant  burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.

  6. Wearable Sensors for Remote Health Monitoring

    Directory of Open Access Journals (Sweden)

    Sumit Majumder

    2017-01-01

    Full Text Available Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant  burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.

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

    Science.gov (United States)

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

    2013-11-01

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

  8. Real-Time Probabilistic Structural Health Management Using Machine Learning and GPU Computing Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed project seeks to deliver an ultra-efficient, high-fidelity structural health management (SHM) framework using machine learning and graphics processing...

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

    International Nuclear Information System (INIS)

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

    1981-01-01

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

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

    International Nuclear Information System (INIS)

    Meng Qinghu; Meng Qingfeng; Feng Wuwei

    2012-01-01

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

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

    International Nuclear Information System (INIS)

    Johannsen, G.

    1979-01-01

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

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Jongryun Roh

    2018-01-01

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

  14. Novelty detection methods for online health monitoring and post data analysis of turbopumps

    International Nuclear Information System (INIS)

    Lei Hu; Niaoqing, Hu; Xinpeng, Zhang; Fengshou, Gu; Ming, Gao

    2013-01-01

    As novelty detection works when only normal data are available, it is of considerable promise for health monitoring in cases lacking fault samples and prior knowledge. We present two novelty detection methods for health monitoring of turbopumps in large-scale liquid propellant rocket engines. The first method is the adaptive Gaussian threshold model. This method is designed to monitor the vibration of the turbopumps online because it has minimal computational complexity and is easy for implementation in real time. The second method is the one-class support vector machine (OCSVM) which is developed for post analysis of historical vibration signals. Via post analysis the method not only confirms the online monitoring results but also provides diagnostic results so that faults from sensors are separated from those actually from the turbopumps. Both of these two methods are validated to be efficient for health monitoring of the turbopumps.

  15. Guided wave based structural health monitoring: A review

    International Nuclear Information System (INIS)

    Mitra, Mira; Gopalakrishnan, S

    2016-01-01

    The paper provides a state of the art review of guided wave based structural health monitoring (SHM). First, the fundamental concepts of guided wave propagation and its implementation for SHM is explained. Following sections present the different modeling schemes adopted, developments in the area of transducers for generation, and sensing of wave, signal processing and imaging technique, statistical and machine learning schemes for feature extraction. Next, a section is presented on the recent advancements in nonlinear guided wave for SHM. This is followed by section on Rayleigh and SH waves. Next is a section on real-life implementation of guided wave for industrial problems. The paper, though briefly talks about the early development for completeness, is primarily focussed on the recent progress made in the last decade. The paper ends by discussing and highlighting the future directions and open areas of research in guided wave based SHM. (topical review)

  16. Principles in wireless building health monitoring systems.

    Science.gov (United States)

    Pentaris, F. P.; Makris, J. P.; Stonham, J.; Vallianatos, F.

    2012-04-01

    Monitoring the structural state of a building is essential for the safety of the people who work, live, visit or just use it as well as for the civil protection of urban areas. Many factors can affect the state of the health of a structure, namely man made, like mistakes in the construction, traffic, heavy loads on the structures, explosions, environmental impacts like wind loads, humidity, chemical reactions, temperature changes and saltiness, and natural hazards like earthquakes and landslides. Monitoring the health of a structure provides the ability to anticipate structural failures and secure the safe use of buildings especially those of public services. This work reviews the state of the art and the challenges of a wireless Structural Health Monitoring (WiSHM). Literature review reveals that although there is significant evolution in wireless structural health monitoring, in many cases, monitoring by itself is not enough to predict when a structure becomes inappropriate and/or unsafe for use, and the damage or low durability of a structure cannot be revealed (Chintalapudi, et al., 2006; Ramos, Aguilar, & Lourenço, 2011). Several features and specifications of WiSHM like wireless sensor networking, reliability and autonomy of sensors, algorithms of data transmission and analysis should still be evolved and improved in order to increase the predictive effectiveness of the SHM (Jinping Ou & Hui Li, 2010; Lu & Loh, 2010) . Acknowledgments This work was supported in part by the ARCHEMEDES III Program of the Ministry of Education of Greece and the European Union in the framework of the project entitled «Interdisciplinary Multi-Scale Research of Earthquake Physics and Seismotectonics at the front of the Hellenic Arc (IMPACT-ARC) ».

  17. Integrating structural health and condition monitoring

    DEFF Research Database (Denmark)

    May, Allan; Thöns, Sebastian; McMillan, David

    2015-01-01

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

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

    Science.gov (United States)

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

    2016-03-01

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

  19. Identification methods for structural health monitoring

    CERN Document Server

    Papadimitriou, Costas

    2016-01-01

    The papers in this volume provide an introduction to well known and established system identification methods for structural health monitoring and to more advanced, state-of-the-art tools, able to tackle the challenges associated with actual implementation. Starting with an overview on fundamental methods, introductory concepts are provided on the general framework of time and frequency domain, parametric and non-parametric methods, input-output or output only techniques. Cutting edge tools are introduced including, nonlinear system identification methods; Bayesian tools; and advanced modal identification techniques (such as the Kalman and particle filters, the fast Bayesian FFT method). Advanced computational tools for uncertainty quantification are discussed to provide a link between monitoring and structural integrity assessment. In addition, full scale applications and field deployments that illustrate the workings and effectiveness of the introduced monitoring schemes are demonstrated.

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

    International Nuclear Information System (INIS)

    Guzzardo, Tyler; Livesay, Jake

    2012-01-01

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

  1. Structural health monitoring using wireless sensor networks

    Science.gov (United States)

    Sreevallabhan, K.; Nikhil Chand, B.; Ramasamy, Sudha

    2017-11-01

    Monitoring and analysing health of large structures like bridges, dams, buildings and heavy machinery is important for safety, economical, operational, making prior protective measures, and repair and maintenance point of view. In recent years there is growing demand for such larger structures which in turn make people focus more on safety. By using Microelectromechanical Systems (MEMS) Accelerometer we can perform Structural Health Monitoring by studying the dynamic response through measure of ambient vibrations and strong motion of such structures. By using Wireless Sensor Networks (WSN) we can embed these sensors in wireless networks which helps us to transmit data wirelessly thus we can measure the data wirelessly at any remote location. This in turn reduces heavy wiring which is a cost effective as well as time consuming process to lay those wires. In this paper we developed WSN based MEMS-accelerometer for Structural to test the results in the railway bridge near VIT University, Vellore campus.

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

    Science.gov (United States)

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

    2013-07-01

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

  3. 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.

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

    International Nuclear Information System (INIS)

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

    2013-01-01

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

  5. New trends in structural health monitoring

    CERN Document Server

    Güemes, J

    2013-01-01

    Experts actively working in structural health monitoring and control techniques present the current research, areas of application and tendencies for the future of this technology, including various design issues involved. Examples using some of the latest hardware and software tools, experimental data from small scale laboratory demonstrators and measurements made on real structures illustrate the book. It will be a reference for professionals and students in the areas of engineering, applied natural sciences and engineering management.

  6. Nuclear propulsion control and health monitoring

    Science.gov (United States)

    Walter, P. B.; Edwards, R. M.

    1993-11-01

    An integrated control and health monitoring architecture is being developed for the Pratt & Whitney XNR2000 nuclear rocket. Current work includes further development of the dynamic simulation modeling and the identification and configuration of low level controllers to give desirable performance for the various operating modes and faulted conditions. Artificial intelligence and knowledge processing technologies need to be investigated and applied in the development of an intelligent supervisory controller module for this control architecture.

  7. Machine learning-based Landsat-MODIS data fusion approach for 8-day 30m evapotranspiration monitoring

    Science.gov (United States)

    Im, J.; Ke, Y.; Park, S.

    2016-12-01

    Continuous monitoring of evapotranspiration (ET) is important for understanding of hydrological cycles and energy flux dynamics. At regional and local scales, routine ET estimation is a critical for efficient water management, drought impact assessment and ecosystem health monitoring, etc. Remote sensing has long been recognized to be able to provide ET monitoring over large areas. However, no single satellite could provide temporally continuous ET at relatively high spatial resolution due to the trade-off between the spatial and temporal resolution of current satellite sensors. Landsat-series satellites provide optical and thermal imagery at 30-100m resolution, whereas the 16-day revisit cycle hinders the observation of ET dynamics; MODIS provides sources of ET estimation at daily basis, but the 500-1000m ground sampling distance is too coarse for field level applications. In this study, we present a machine learning and STARFM based method for Landsat/MODIS ET fusion. The approach first downscales MODIS 8-day 1km ET (MOD16A2) to 30m based on eleven Landsat-derived indicators such as NDVI, EVI, NDWI etc on the cloud-free Landsat-available days using Random Forest approach. For the days when Landsat data are not available, downscaled ET is synthesized by MODIS and Landsat data fusion with STARFM and STI-FM approaches. The models are evaluated using in situ flux tower measurements at US-ARM and US-Twt AmeriFlux sites the United States. Results show that the downscaled 30m ET have good agreement with MODIS ET (RMSE=0.42-3.4mm/8days, rRMSE=3.2%-26%) and the downscaled ET have higher accuracy than MODIS ET when compared to in-situ measurements.

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

    International Nuclear Information System (INIS)

    Syaiful Bakhri

    2013-01-01

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

  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. Structural health monitoring 2012. Proceedings. Vol. 2

    International Nuclear Information System (INIS)

    Boller, Christian

    2012-01-01

    Structural Health Monitoring (SHM) is an emerging technology, dealing with the development and implementation of techniques and systems where monitoring, inspection and damage detection become an integral part of structures and thus a matter of automation. It further merges with a variety of techniques related to diagnostics and prognostics. SHM emerged from the field of smart structures and laterally encompasses disciplines such as structural dynamics, materials and structures, fatigue and fracture, non-destructive testing and evaluation, sensors and actuators, microelectronics, signal processing and much more. To be effective in the development of SHM systems, a multidisciplinary approach is therefore required. Without this global view it will be difficult for engineers to holistically manage the operation of an engineering structure through its life cycle in the future and to generate new breakthroughs in structural engineering. The second volume of the proceedings contains topics dealing with applications in the field of aeronautics, astronautic, civil engineering (bridges), energy (wind power), structural health monitoring (transportation), and poster presentations. Ten of the contributions are separately analyzed for the ENERGY database.

  12. Frequency Selective Surface for Structural Health Monitoring

    Science.gov (United States)

    Norlyana Azemi, Saidatul; Mustaffa, Farzana Hazira Wan; Faizal Jamlos, Mohd; Abdullah Al-Hadi, Azremi; Soh, Ping Jack

    2018-03-01

    Structural health monitoring (SHM) technologies have attained attention to monitor civil structures. SHM sensor systems have been used in various civil structures such as bridges, buildings, tunnels and so on. However the previous sensor for SHM is wired and encounter with problem to cover large areas. Therefore, wireless sensor was introduced for SHM to reduce network connecting problem. Wireless sensors for Structural Health monitoring are new technology and have many advantages to overcome the drawback of conventional and wired sensor. This project proposed passive wireless SHM sensor using frequency selective surface (FSS) as an alternative to conventional sensors. The electromagnetic wave characteristic of FSS will change by geometrical changes of FSS due to mechanical strain or structural failure. The changes feature is used as a sensing function without any connecting wires. Two type of design which are circular ring and square loop along with the transmission and reflection characteristics of SHM using FSS were discussed in this project. A simulation process has shown that incident angle characteristics can be use as a data for SHM application.

  13. An autonomous structural health monitoring solution

    Science.gov (United States)

    Featherston, Carol A.; Holford, Karen M.; Pullin, Rhys; Lees, Jonathan; Eaton, Mark; Pearson, Matthew

    2013-05-01

    Combining advanced sensor technologies, with optimised data acquisition and diagnostic and prognostic capability, structural health monitoring (SHM) systems provide real-time assessment of the integrity of bridges, buildings, aircraft, wind turbines, oil pipelines and ships, leading to improved safety and reliability and reduced inspection and maintenance costs. The implementation of power harvesting, using energy scavenged from ambient sources such as thermal gradients and sources of vibration in conjunction with wireless transmission enables truly autonomous systems, reducing the need for batteries and associated maintenance in often inaccessible locations, alongside bulky and expensive wiring looms. The design and implementation of such a system however presents numerous challenges. A suitable energy source or multiple sources capable of meeting the power requirements of the system, over the entire monitoring period, in a location close to the sensor must be identified. Efficient power management techniques must be used to condition the power and deliver it, as required, to enable appropriate measurements to be taken. Energy storage may be necessary, to match a continuously changing supply and demand for a range of different monitoring states including sleep, record and transmit. An appropriate monitoring technique, capable of detecting, locating and characterising damage and delivering reliable information, whilst minimising power consumption, must be selected. Finally a wireless protocol capable of transmitting the levels of information generated at the rate needed in the required operating environment must be chosen. This paper considers solutions to some of these challenges, and in particular examines SHM in the context of the aircraft environment.

  14. Structural health monitoring 2012. Proceedings. Vol. 1

    International Nuclear Information System (INIS)

    Boller, Christian

    2012-01-01

    Structural Health Monitoring (SHM) is an emerging technology, dealing with the development and implementation of techniques and systems where monitoring, inspection and damage detection become an integral part of structures and thus a matter of automation. It further merges with a variety of techniques related to diagnostics and prognostics. SHM emerged from the field of smart structures and laterally encompasses disciplines such as structural dynamics, materials and structures, fatigue and fracture, non-destructive testing and evaluation, sensors and actuators, microelectronics, signal processing and much more. To be effective in the development of SHM systems, a multidisciplinary approach is therefore required. Without this global view it will be difficult for engineers to holistically manage the operation of an engineering structure through its life cycle in the future and to generate new breakthroughs in structural engineering. The first volume of the proceedings contains topics dealing with physics, materials and sensors. Five of the contributions are separately analyzed for the ENERGY database.

  15. Health-promoting vending machines: evaluation of a pediatric hospital intervention.

    Science.gov (United States)

    Van Hulst, Andraea; Barnett, Tracie A; Déry, Véronique; Côté, Geneviève; Colin, Christine

    2013-01-01

    Taking advantage of a natural experiment made possible by the placement of health-promoting vending machines (HPVMs), we evaluated the impact of the intervention on consumers' attitudes toward and practices with vending machines in a pediatric hospital. Vending machines offering healthy snacks, meals, and beverages were developed to replace four vending machines offering the usual high-energy, low-nutrition fare. A pre- and post-intervention evaluation design was used; data were collected through exit surveys and six-week follow-up telephone surveys among potential vending machine users before (n=293) and after (n=226) placement of HPVMs. Chi-2 statistics were used to compare pre- and post-intervention participants' responses. More than 90% of pre- and post-intervention participants were satisfied with their purchase. Post-intervention participants were more likely to state that nutritional content and appropriateness of portion size were elements that influenced their purchase. Overall, post-intervention participants were more likely than pre-intervention participants to perceive as healthy the options offered by the hospital vending machines. Thirty-three percent of post-intervention participants recalled two or more sources of information integrated in the HPVM concept. No differences were found between pre- and post-intervention participants' readiness to adopt healthy diets. While the HPVM project had challenges as well as strengths, vending machines offering healthy snacks are feasible in hospital settings.

  16. Health monitoring method for composite materials

    Science.gov (United States)

    Watkins, Jr., Kenneth S.; Morris, Shelby J [Hampton, VA

    2011-04-12

    An in-situ method for monitoring the health of a composite component utilizes a condition sensor made of electrically conductive particles dispersed in a polymeric matrix. The sensor is bonded or otherwise formed on the matrix surface of the composite material. Age-related shrinkage of the sensor matrix results in a decrease in the resistivity of the condition sensor. Correlation of measured sensor resistivity with data from aged specimens allows indirect determination of mechanical damage and remaining age of the composite component.

  17. Students' perspectives on promoting healthful food choices from campus vending machines: a qualitative interview study.

    Science.gov (United States)

    Ali, Habiba I; Jarrar, Amjad H; Abo-El-Enen, Mostafa; Al Shamsi, Mariam; Al Ashqar, Huda

    2015-05-28

    Increasing the healthfulness of campus food environments is an important step in promoting healthful food choices among college students. This study explored university students' suggestions on promoting healthful food choices from campus vending machines. It also examined factors influencing students' food choices from vending machines. Peer-led semi-structured individual interviews were conducted with 43 undergraduate students (33 females and 10 males) recruited from students enrolled in an introductory nutrition course in a large national university in the United Arab Emirates. Interviews were audiotaped, transcribed, and coded to generate themes using N-Vivo software. Accessibility, peer influence, and busy schedules were the main factors influencing students' food choices from campus vending machines. Participants expressed the need to improve the nutritional quality of the food items sold in the campus vending machines. Recommendations for students' nutrition educational activities included placing nutrition tips on or beside the vending machines and using active learning methods, such as competitions on nutrition knowledge. The results of this study have useful applications in improving the campus food environment and nutrition education opportunities at the university to assist students in making healthful food choices.

  18. Monitoring 'monitoring' and evaluating 'evaluation': an ethical framework for monitoring and evaluation in public health.

    Science.gov (United States)

    Gopichandran, Vijayaprasad; Indira Krishna, Anil Kumar

    2013-01-01

    Monitoring and evaluation (M&E) is an essential part of public health programmes. Since M&E is the backbone of public health programmes, ethical considerations are important in their conduct. Some of the key ethical considerations are avoiding conflicts of interest, maintaining independence of judgement, maintaining fairness, transparency, full disclosure, privacy and confidentiality, respect, responsibility, accountability, empowerment and sustainability. There are several ethical frameworks in public health, but none focusing on the monitoring and evaluation process. There is a need to institutionalise the ethical review of M&E proposals. A theoretical framework for ethical considerations is proposed in this paper. This proposed theoretical framework can act as the blueprint for building the capacity of ethics committees to review M&E proposals. A case study is discussed in this context. After thorough field testing, this practical and field-based ethical framework can be widely used by donor agencies, M&E teams, institutional review boards and ethics committees.

  19. A Mobile Health Application to Predict Postpartum Depression Based on Machine Learning.

    Science.gov (United States)

    Jiménez-Serrano, Santiago; Tortajada, Salvador; García-Gómez, Juan Miguel

    2015-07-01

    Postpartum depression (PPD) is a disorder that often goes undiagnosed. The development of a screening program requires considerable and careful effort, where evidence-based decisions have to be taken in order to obtain an effective test with a high level of sensitivity and an acceptable specificity that is quick to perform, easy to interpret, culturally sensitive, and cost-effective. The purpose of this article is twofold: first, to develop classification models for detecting the risk of PPD during the first week after childbirth, thus enabling early intervention; and second, to develop a mobile health (m-health) application (app) for the Android(®) (Google, Mountain View, CA) platform based on the model with best performance for both mothers who have just given birth and clinicians who want to monitor their patient's test. A set of predictive models for estimating the risk of PPD was trained using machine learning techniques and data about postpartum women collected from seven Spanish hospitals. An internal evaluation was carried out using a hold-out strategy. An easy flowchart and architecture for designing the graphical user interface of the m-health app was followed. Naive Bayes showed the best balance between sensitivity and specificity as a predictive model for PPD during the first week after delivery. It was integrated into the clinical decision support system for Android mobile apps. This approach can enable the early prediction and detection of PPD because it fulfills the conditions of an effective screening test with a high level of sensitivity and specificity that is quick to perform, easy to interpret, culturally sensitive, and cost-effective.

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

    Directory of Open Access Journals (Sweden)

    Björn J. P. van der Ster

    2018-01-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  2. Data driven innovations in structural health monitoring

    Science.gov (United States)

    Rosales, M. J.; Liyanapathirana, R.

    2017-05-01

    At present, substantial investments are being allocated to civil infrastructures also considered as valuable assets at a national or global scale. Structural Health Monitoring (SHM) is an indispensable tool required to ensure the performance and safety of these structures based on measured response parameters. The research to date on damage assessment has tended to focus on the utilization of wireless sensor networks (WSN) as it proves to be the best alternative over the traditional visual inspections and tethered or wired counterparts. Over the last decade, the structural health and behaviour of innumerable infrastructure has been measured and evaluated owing to several successful ventures of implementing these sensor networks. Various monitoring systems have the capability to rapidly transmit, measure, and store large capacities of data. The amount of data collected from these networks have eventually been unmanageable which paved the way to other relevant issues such as data quality, relevance, re-use, and decision support. There is an increasing need to integrate new technologies in order to automate the evaluation processes as well as to enhance the objectivity of data assessment routines. This paper aims to identify feasible methodologies towards the application of time-series analysis techniques to judiciously exploit the vast amount of readily available as well as the upcoming data resources. It continues the momentum of a greater effort to collect and archive SHM approaches that will serve as data-driven innovations for the assessment of damage through efficient algorithms and data analytics.

  3. National health inequality monitoring: current challenges and opportunities.

    Science.gov (United States)

    Hosseinpoor, Ahmad Reza; Bergen, Nicole; Schlotheuber, Anne; Boerma, Ties

    National health inequality monitoring needs considerably more investment to realize equity-oriented health improvements in countries, including advancement towards the Sustainable Development Goals. Following an overview of national health inequality monitoring and the associated resource requirements, we highlight challenges that countries may encounter when setting up, expanding or strengthening national health inequality monitoring systems, and discuss opportunities and key initiatives that aim to address these challenges. We provide specific proposals on what is needed to ensure that national health inequality monitoring systems are harnessed to guide the reduction of health inequalities.

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

    Directory of Open Access Journals (Sweden)

    Amiya BHAUMIK

    2008-12-01

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

  5. 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

  6. Wearable health monitoring using capacitive voltage-mode Human Body Communication.

    Science.gov (United States)

    Maity, Shovan; Das, Debayan; Sen, Shreyas

    2017-07-01

    Rapid miniaturization and cost reduction of computing, along with the availability of wearable and implantable physiological sensors have led to the growth of human Body Area Network (BAN) formed by a network of such sensors and computing devices. One promising application of such a network is wearable health monitoring where the collected data from the sensors would be transmitted and analyzed to assess the health of a person. Typically, the devices in a BAN are connected through wireless (WBAN), which suffers from energy inefficiency due to the high-energy consumption of wireless transmission. Human Body Communication (HBC) uses the relatively low loss human body as the communication medium to connect these devices, promising order(s) of magnitude better energy-efficiency and built-in security compared to WBAN. In this paper, we demonstrate a health monitoring device and system built using Commercial-Off-The-Shelf (COTS) sensors and components, that can collect data from physiological sensors and transmit it through a) intra-body HBC to another device (hub) worn on the body or b) upload health data through HBC-based human-machine interaction to an HBC capable machine. The system design constraints and signal transfer characteristics for the implemented HBC-based wearable health monitoring system are measured and analyzed, showing reliable connectivity with >8× power savings compared to Bluetooth low-energy (BTLE).

  7. Fiber Optic Thermal Health Monitoring of Composites

    Science.gov (United States)

    Wu, Meng-Chou; Winfree, William P.; Moore, Jason P.

    2010-01-01

    A recently developed technique is presented for thermographic detection of flaws in composite materials by performing temperature measurements with fiber optic Bragg gratings. Individual optical fibers with multiple Bragg gratings employed as surface temperature sensors were bonded to the surfaces of composites with subsurface defects. The investigated structures included a 10-ply composite specimen with subsurface delaminations of various sizes and depths. Both during and following the application of a thermal heat flux to the surface, the individual Bragg grating sensors measured the temporal and spatial temperature variations. The data obtained from grating sensors were analyzed with thermal modeling techniques of conventional thermography to reveal particular characteristics of the interested areas. Results were compared with the calculations using numerical simulation techniques. Methods and limitations for performing in-situ structural health monitoring are discussed.

  8. Exploring Machine Learning Techniques Using Patient Interactions in Online Health Forums to Classify Drug Safety

    Science.gov (United States)

    Chee, Brant Wah Kwong

    2011-01-01

    This dissertation explores the use of personal health messages collected from online message forums to predict drug safety using natural language processing and machine learning techniques. Drug safety is defined as any drug with an active safety alert from the US Food and Drug Administration (FDA). It is believed that this is the first…

  9. Health equity monitoring for healthcare quality assurance.

    Science.gov (United States)

    Cookson, R; Asaria, M; Ali, S; Shaw, R; Doran, T; Goldblatt, P

    2018-02-01

    Population-wide health equity monitoring remains isolated from mainstream healthcare quality assurance. As a result, healthcare organizations remain ill-informed about the health equity impacts of their decisions - despite becoming increasingly well-informed about quality of care for the average patient. We present a new and improved analytical approach to integrating health equity into mainstream healthcare quality assurance, illustrate how this approach has been applied in the English National Health Service, and discuss how it could be applied in other countries. We illustrate the approach using a key quality indicator that is widely used to assess how well healthcare is co-ordinated between primary, community and acute settings: emergency inpatient hospital admissions for ambulatory care sensitive chronic conditions ("potentially avoidable emergency admissions", for short). Whole-population data for 2015 on potentially avoidable emergency admissions in England were linked with neighborhood deprivation indices. Inequality within the populations served by 209 clinical commissioning groups (CCGs: care purchasing organizations with mean population 272,000) was compared against two benchmarks - national inequality and inequality within ten similar populations - using neighborhood-level models to simulate the gap in indirectly standardized admissions between most and least deprived neighborhoods. The modelled inequality gap for England was 927 potentially avoidable emergency admissions per 100,000 people, implying 263,894 excess hospitalizations associated with inequality. Against this national benchmark, 17% of CCGs had significantly worse-than-benchmark equity, and 23% significantly better. The corresponding figures were 11% and 12% respectively against the similar populations benchmark. Deprivation-related inequality in potentially avoidable emergency admissions varies substantially between English CCGs serving similar populations, beyond expected statistical

  10. Three-Dimensional Health Monitoring of Sandwich Composites, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — This SBIR project delivers a single-chip structural health-monitoring (SHM) system that uses the impedance method to monitor bulk interiors and wave propagation...

  11. Information processing for aerospace structural health monitoring

    Science.gov (United States)

    Lichtenwalner, Peter F.; White, Edward V.; Baumann, Erwin W.

    1998-06-01

    Structural health monitoring (SHM) technology provides a means to significantly reduce life cycle of aerospace vehicles by eliminating unnecessary inspections, minimizing inspection complexity, and providing accurate diagnostics and prognostics to support vehicle life extension. In order to accomplish this, a comprehensive SHM system will need to acquire data from a wide variety of diverse sensors including strain gages, accelerometers, acoustic emission sensors, crack growth gages, corrosion sensors, and piezoelectric transducers. Significant amounts of computer processing will then be required to convert this raw sensor data into meaningful information which indicates both the diagnostics of the current structural integrity as well as the prognostics necessary for planning and managing the future health of the structure in a cost effective manner. This paper provides a description of the key types of information processing technologies required in an effective SHM system. These include artificial intelligence techniques such as neural networks, expert systems, and fuzzy logic for nonlinear modeling, pattern recognition, and complex decision making; signal processing techniques such as Fourier and wavelet transforms for spectral analysis and feature extraction; statistical algorithms for optimal detection, estimation, prediction, and fusion; and a wide variety of other algorithms for data analysis and visualization. The intent of this paper is to provide an overview of the role of information processing for SHM, discuss various technologies which can contribute to accomplishing this role, and present some example applications of information processing for SHM implemented at the Boeing Company.

  12. 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....

  13. Health Informatics via Machine Learning for the Clinical Management of Patients.

    Science.gov (United States)

    Clifton, D A; Niehaus, K E; Charlton, P; Colopy, G W

    2015-08-13

    To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice. We reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring together a broad body of literature, aiming to identify those leading examples of health informatics that have advanced the methodology of machine learning. While individual methods may have further examples that might be added, we have chosen some of the most representative, informative exemplars in each case. Our survey highlights that, while much research is taking place in this high-profile field, examples of those that affect the clinical management of patients are seldom found. We show that substantial progress is being made in terms of methodology, often by data scientists working in close collaboration with clinical groups. Health informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.

  14. Levee Health Monitoring With Radar Remote Sensing

    Science.gov (United States)

    Jones, C. E.; Bawden, G. W.; Deverel, S. J.; Dudas, J.; Hensley, S.; Yun, S.

    2012-12-01

    Remote sensing offers the potential to augment current levee monitoring programs by providing rapid and consistent data collection over large areas irrespective of the ground accessibility of the sites of interest, at repeat intervals that are difficult or costly to maintain with ground-based surveys, and in rapid response to emergency situations. While synthetic aperture radar (SAR) has long been used for subsidence measurements over large areas, applying this technique directly to regional levee monitoring is a new endeavor, mainly because it requires both a wide imaging swath and fine spatial resolution to resolve individual levees within the scene, a combination that has not historically been available. Application of SAR remote sensing directly to levee monitoring has only been attempted in a few pilot studies. Here we describe how SAR remote sensing can be used to assess levee conditions, such as seepage, drawing from the results of two levee studies: one of the Sacramento-San Joaquin Delta levees in California that has been ongoing since July 2009 and a second that covered the levees near Vicksburg, Mississippi, during the spring 2011 floods. These studies have both used data acquired with NASA's UAVSAR L-band synthetic aperture radar, which has the spatial resolution needed for this application (1.7 m single-look), sufficiently wide imaging swath (22 km), and the longer wavelength (L-band, 0.238 m) required to maintain phase coherence between repeat collections over levees, an essential requirement for applying differential interferometry (DInSAR) to a time series of repeated collections for levee deformation measurement. We report the development and demonstration of new techniques that employ SAR polarimetry and differential interferometry to successfully assess levee health through the quantitative measurement of deformation on and near levees and through detection of areas experiencing seepage. The Sacramento-San Joaquin Delta levee study, which covers

  15. Accelerated Aging Experiments for Capacitor Health Monitoring and Prognostics

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper discusses experimental setups for health monitoring and prognostics of electrolytic capacitors under nominal operation and accelerated aging conditions....

  16. Smart health monitoring systems: an overview of design and modeling.

    Science.gov (United States)

    Baig, Mirza Mansoor; Gholamhosseini, Hamid

    2013-04-01

    Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.

  17. Non-Intrusive Battery Health Monitoring

    Directory of Open Access Journals (Sweden)

    Gajewski Laurent

    2017-01-01

    Full Text Available The “Non-intrusive battery health monitoring”, developed by Airbus Defence and Space (ADS in cooperation with the CIRIMAT-CNRS laboratory and supported by CNES, aims at providing a diagnosis of the battery ageing in flight, called State of Health (SOH, using only the post-treatment of the battery telemetries. The battery current and voltage telemetries are used by a signal processing tool on ground to characterize and to model the battery at low frequencies which allows monitoring the evolution of its degradation with great accuracy. The frequential behaviour estimation is based on inherent disturbances on the current during the nominal functioning of the battery. For instance, on-board thermal control or equipment consumption generates random disturbances on battery current around an average current. The battery voltage response to these current random disturbances enables to model the low frequency impedance of the battery by a signal processing tool. The re-created impedance is then compared with the evolution model of the low frequencies impedance as a function of the battery ageing to estimate accurately battery degradation. Hence, this method could be applied to satellites which are already in orbit and whose battery telemetries acquisition system fulfils the constraints determined in the study. This innovative method is an improvement of present state-of-the-art and is important to have a more accurate in-flight knowledge of battery ageing which is crucial for mission and operation planning and also for possible satellite mission extension or deorbitation. This method is patented by Airbus Defence and Space and CNES.

  18. FOREWORD: Structural Health Monitoring and Intelligent Infrastructure

    Science.gov (United States)

    Wu, Zhishen; Fujino, Yozo

    2005-06-01

    This special issue collects together 19 papers that were originally presented at the First International Conference on Structural Health Monitoring and Intelligent Infrastructure (SHMII-1'2003), held in Tokyo, Japan, on 13-15 November 2003. This conference was organized by the Japan Society of Civil Engineers (JSCE) with partial financial support from the Japan Society for the Promotion of Science (JSPS) and the Ministry of Education, Culture, Sport, Science and Technology, Japan. Many related organizations supported the conference. A total of 16 keynote papers including six state-of-the-art reports from different counties, six invited papers and 154 contributed papers were presented at the conference. The conference was attended by a diverse group of about 300 people from a variety of disciplines in academia, industry and government from all over the world. Structural health monitoring (SHM) and intelligent materials, structures and systems have been the subject of intense research and development in the last two decades and, in recent years, an increasing range of applications in infrastructure have been discovered both for existing structures and for new constructions. SHMII-1'2003 addressed progress in the development of building, transportation, marine, underground and energy-generating structures, and other civilian infrastructures that are periodically, continuously and/or actively monitored where there is a need to optimize their performance. In order to focus the current needs on SHM and intelligent technologies, the conference theme was set as 'Structures/Infrastructures Sustainability'. We are pleased to have the privilege to edit this special issue on SHM and intelligent infrastructure based on SHMII-1'2003. We invited some of the presenters to submit a revised/extended version of their paper that was included in the SHMII-1'2003 proceedings for possible publication in the special issue. Each paper included in this special issue was edited with the same

  19. Wireless sensor networks for structural health monitoring

    CERN Document Server

    Cao, Jiannong

    2016-01-01

    This brief covers the emerging area of wireless sensor network (WSN)-based structural health monitoring (SHM) systems, and introduces the authors’ WSN-based platform called SenetSHM. It helps the reader differentiate specific requirements of SHM applications from other traditional WSN applications, and demonstrates how these requirements are addressed by using a series of systematic approaches. The brief serves as a practical guide, explaining both the state-of-the-art technologies in domain-specific applications of WSNs, as well as the methodologies used to address the specific requirements for a WSN application. In particular, the brief offers instruction for problem formulation and problem solving based on the authors’ own experiences implementing SenetSHM. Seven concise chapters cover the development of hardware and software design of SenetSHM, as well as in-field experiments conducted while testing the platform. The brief’s exploration of the SenetSHM platform is a valuable feature for civil engine...

  20. Packaging of structural health monitoring components

    Science.gov (United States)

    Kessler, Seth S.; Spearing, S. Mark; Shi, Yong; Dunn, Christopher T.

    2004-07-01

    Structural Health Monitoring (SHM) technologies have the potential to realize economic benefits in a broad range of commercial and defense markets. Previous research conducted by Metis Design and MIT has demonstrated the ability of Lamb waves methods to provide reliable information regarding the presence, location and type of damage in composite specimens. The present NSF funded program was aimed to study manufacturing, packaging and interface concepts for critical SHM components. The intention is to be able to cheaply manufacture robust actuating/sensing devices, and isolate them from harsh operating environments including natural, mechanical, or electrical extremes. Currently the issues related to SHM system durability have remained undressed. During the course of this research several sets of test devices were fabricated and packaged to protect the piezoelectric component assemblies for robust operation. These assemblies were then tested in hot and wet conditions, as well as in electrically noisy environments. Future work will aim to package the other supporting components such as the battery and wireless chip, as well as integrating all of these components together for operation. SHM technology will enable the reduction or complete elimination of scheduled inspections, and will allow condition-based maintenance for increased reliability and reduced overall life-cycle costs.

  1. Smart sensors for health and environment monitoring

    CERN Document Server

    2015-01-01

    This book covers two most important applications of smart sensors, namely bio-health sensing and environmental monitoring.   The approach taken is holistic and covers the complete scope of the subject matter from the principles of the sensing mechanism, through device physics, circuit and system implementation techniques, and energy issues  to wireless connectivity solutions. It is written at a level suitable mainly for post-graduate level researchers interested in practical applications. The chapters are independent but complementary to each other, and the book works within the wider perspective of essential smart sensors for the Internet of Things (IoT).   This is the second of three books based on the Integrated Smart Sensors research project, which describe the development of innovative devices, circuits, and system-level enabling technologies.  The aim of the project was to develop common platforms on which various devices and sensors can be loaded, and to create systems offering significant improve...

  2. Structural health monitoring for ship structures

    Energy Technology Data Exchange (ETDEWEB)

    Farrar, Charles [Los Alamos National Laboratory; Park, Gyuhae [Los Alamos National Laboratory; Angel, Marian [Los Alamos National Laboratory; Bement, Matthew [Los Alamos National Laboratory; Salvino, Liming [NSWC, CADEROCK

    2009-01-01

    Currently the Office of Naval Research is supporting the development of structural health monitoring (SHM) technology for U.S. Navy ship structures. This application is particularly challenging because of the physical size of these structures, the widely varying and often extreme operational and environmental conditions associated with these ships missions, lack of data from known damage conditions, limited sensing that was not designed specifically for SHM, and the management of the vast amounts of data that can be collected during a mission. This paper will first define a statistical pattern recognition paradigm for SHM by describing the four steps of (1) Operational Evaluation, (2) Data Acquisition, (3) Feature Extraction, and (4) Statistical Classification of Features as they apply to ship structures. Note that inherent in the last three steps of this process are additional tasks of data cleansing, compression, normalization and fusion. The presentation will discuss ship structure SHM challenges in the context of applying various SHM approaches to sea trials data measured on an aluminum multi-hull high-speed ship, the HSV-2 Swift. To conclude, the paper will discuss several outstanding issues that need to be addressed before SHM can make the transition from a research topic to actual field applications on ship structures and suggest approaches for addressing these issues.

  3. Local health department translation processes: potential of machine translation technologies to help meet needs.

    Science.gov (United States)

    Turner, Anne M; Mandel, Hannah; Capurro, Daniel

    2013-01-01

    Limited English proficiency (LEP), defined as a limited ability to read, speak, write, or understand English, is associated with health disparities. Despite federal and state requirements to translate health information, the vast majority of health materials are solely available in English. This project investigates barriers to translation of health information and explores new technologies to improve access to multilingual public health materials. We surveyed all 77 local health departments (LHDs) in the Northwest about translation needs, practices, barriers and attitudes towards machine translation (MT). We received 67 responses from 45 LHDs. Translation of health materials is the principle strategy used by LHDs to reach LEP populations. Cost and access to qualified translators are principle barriers to producing multilingual materials. Thirteen LHDs have used online MT tools. Many respondents expressed concerns about the accuracy of MT. Overall, respondents were positive about its potential use, if low costs and quality could be assured.

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

    International Nuclear Information System (INIS)

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

    2007-01-01

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

  5. 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.

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

    Directory of Open Access Journals (Sweden)

    Dimitrov Vasil

    2017-01-01

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

  7. A synthesis of evaluation monitoring projects by the forest health monitoring program (1998-2007)

    Science.gov (United States)

    William A. Bechtold; Michael J. Bohne; Barbara L. Conkling; Dana L. Friedman

    2012-01-01

    The national Forest Health Monitoring Program of the Forest Service, U.S. Department of Agriculture, has funded over 200 Evaluation Monitoring projects. Evaluation Monitoring is designed to verify and define the extent of deterioration in forest ecosystems where potential problems have been identified. This report is a synthesis of results from over 150 Evaluation...

  8. Structural health monitoring with a wireless vibration sensor network

    NARCIS (Netherlands)

    Basten, T.G.H.; Sas, P; Schiphorst, F.B.A.; Jonckheere, S.; Moens, D.

    2012-01-01

    Advanced maintenance strategies for infrastructure assets such as bridges or off shore wind turbines require actual and reliable information of the maintenance status. Structural health monitoring based on vibration sensing can help in supplying the input needed for structural health monitoring

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

    Science.gov (United States)

    Srinivasan, Deepa

    2013-01-01

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

  10. Structural health monitoring system/method using electroactive polymer fibers

    Science.gov (United States)

    Scott-Carnell, Lisa A. (Inventor); Siochi, Emilie J. (Inventor)

    2013-01-01

    A method for monitoring the structural health of a structure of interest by coupling one or more electroactive polymer fibers to the structure and monitoring the electroactive responses of the polymer fiber(s). Load changes that are experienced by the structure cause changes in the baseline responses of the polymer fiber(s). A system for monitoring the structural health of the structure is also provided.

  11. Health Monitoring System Technology Assessments: Cost Benefits Analysis

    Science.gov (United States)

    Kent, Renee M.; Murphy, Dennis A.

    2000-01-01

    The subject of sensor-based structural health monitoring is very diverse and encompasses a wide range of activities including initiatives and innovations involving the development of advanced sensor, signal processing, data analysis, and actuation and control technologies. In addition, it embraces the consideration of the availability of low-cost, high-quality contributing technologies, computational utilities, and hardware and software resources that enable the operational realization of robust health monitoring technologies. This report presents a detailed analysis of the cost benefit and other logistics and operational considerations associated with the implementation and utilization of sensor-based technologies for use in aerospace structure health monitoring. The scope of this volume is to assess the economic impact, from an end-user perspective, implementation health monitoring technologies on three structures. It specifically focuses on evaluating the impact on maintaining and supporting these structures with and without health monitoring capability.

  12. Interactive machine learning for health informatics: when do we need the human-in-the-loop?

    Science.gov (United States)

    Holzinger, Andreas

    2016-06-01

    Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as "algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human." This "human-in-the-loop" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

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

    Directory of Open Access Journals (Sweden)

    Alessandra Caggiano

    2018-03-01

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

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

    Science.gov (United States)

    Caggiano, Alessandra

    2018-03-09

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

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

    Science.gov (United States)

    2018-01-01

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

  16. Impact of Healthy Vending Machine Options in a Large Community Health Organization.

    Science.gov (United States)

    Grivois-Shah, Ravi; Gonzalez, Juan R; Khandekar, Shashank P; Howerter, Amy L; O'Connor, Patrick A; Edwards, Barbara A

    2017-01-01

    To determine whether increasing the proportion of healthier options in vending machines decreases the amount of calories, fat, sugar, and sodium vended, while maintaining total sales revenue. This study evaluated the impact of altering nutritious options to vending machines throughout the Banner Health organization by comparing vended items' sales and nutrition information over 6 months compared to the same 6 months of the previous year. Twenty-three locations including corporate and patient-care centers. Changing vending machine composition toward more nutritious options. Comparisons of monthly aggregates of sales, units vended, calories, fat, sodium, and sugar vended by site. A pre-post analysis using paired t tests comparing 6 months before implementation to the equivalent 6 months postimplementation. Significant average monthly decreases were seen for calories (16.7%, P = .002), fat (27.4%, P ≤ .0001), sodium (25.9%, P ≤ .0001), and sugar (11.8%, P = .045) vended from 2014 to 2015. Changes in revenue and units vended did not change from 2014 to 2015 ( P = .58 and P = .45, respectively). Increasing the proportion of healthier options in vending machines from 20% to 80% significantly lowered the amount of calories, sodium, fat, and sugar vended, while not reducing units vended or having a negative financial impact.

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

    Directory of Open Access Journals (Sweden)

    Meshcheryakov Yaroslav

    2018-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1998-12-31

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

  19. 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.

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

    Science.gov (United States)

    2017-07-18

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

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

    Science.gov (United States)

    Rahmawati, P.; Prajitno, P.

    2018-04-01

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

  2. The use of the road to health card in monitoring child health | Tarwa ...

    African Journals Online (AJOL)

    The use of the road to health card in monitoring child health. ... The Road to Health Chart (RTHC) provides a simple, cheap, practical and convenient method of monitoring child health. The RTHC could assist ... Conclusions: Many parents believe that the RTHC is only required for Well-baby-clinic visits, not for consultations.

  3. Is it worth changing pattern recognition methods for structural health monitoring?

    Science.gov (United States)

    Bull, L. A.; Worden, K.; Cross, E. J.; Dervilis, N.

    2017-05-01

    The key element of this work is to demonstrate alternative strategies for using pattern recognition algorithms whilst investigating structural health monitoring. This paper looks to determine if it makes any difference in choosing from a range of established classification techniques: from decision trees and support vector machines, to Gaussian processes. Classification algorithms are tested on adjustable synthetic data to establish performance metrics, then all techniques are applied to real SHM data. To aid the selection of training data, an informative chain of artificial intelligence tools is used to explore an active learning interaction between meaningful clusters of data.

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

    CSIR Research Space (South Africa)

    Adeleke, Jude Adekunle

    2017-04-01

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

  5. Promoting health equity: WHO health inequality monitoring at global and national levels

    Directory of Open Access Journals (Sweden)

    Ahmad Reza Hosseinpoor

    2015-09-01

    Full Text Available Background: Health equity is a priority in the post-2015 sustainable development agenda and other major health initiatives. The World Health Organization (WHO has a history of promoting actions to achieve equity in health, including efforts to encourage the practice of health inequality monitoring. Health inequality monitoring systems use disaggregated data to identify disadvantaged subgroups within populations and inform equity-oriented health policies, programs, and practices. Objective: This paper provides an overview of a number of recent and current WHO initiatives related to health inequality monitoring at the global and/or national level. Design: We outline the scope, content, and intended uses/application of the following: Health Equity Monitor database and theme page; State of inequality: reproductive, maternal, newborn, and child health report; Handbook on health inequality monitoring: with a focus on low- and middle-income countries; Health inequality monitoring eLearning module; Monitoring health inequality: an essential step for achieving health equity advocacy booklet and accompanying video series; and capacity building workshops conducted in WHO Member States and Regions. Conclusions: The paper concludes by considering how the work of the WHO can be expanded upon to promote the establishment of sustainable and robust inequality monitoring systems across a variety of health topics among Member States and at the global level.

  6. Promoting health equity: WHO health inequality monitoring at global and national levels.

    Science.gov (United States)

    Hosseinpoor, Ahmad Reza; Bergen, Nicole; Schlotheuber, Anne

    2015-01-01

    Health equity is a priority in the post-2015 sustainable development agenda and other major health initiatives. The World Health Organization (WHO) has a history of promoting actions to achieve equity in health, including efforts to encourage the practice of health inequality monitoring. Health inequality monitoring systems use disaggregated data to identify disadvantaged subgroups within populations and inform equity-oriented health policies, programs, and practices. This paper provides an overview of a number of recent and current WHO initiatives related to health inequality monitoring at the global and/or national level. We outline the scope, content, and intended uses/application of the following: Health Equity Monitor database and theme page; State of inequality: reproductive, maternal, newborn, and child health report; Handbook on health inequality monitoring: with a focus on low- and middle-income countries; Health inequality monitoring eLearning module; Monitoring health inequality: an essential step for achieving health equity advocacy booklet and accompanying video series; and capacity building workshops conducted in WHO Member States and Regions. The paper concludes by considering how the work of the WHO can be expanded upon to promote the establishment of sustainable and robust inequality monitoring systems across a variety of health topics among Member States and at the global level.

  7. Promoting health equity: WHO health inequality monitoring at global and national levels

    Science.gov (United States)

    Hosseinpoor, Ahmad Reza; Bergen, Nicole; Schlotheuber, Anne

    2015-01-01

    Background Health equity is a priority in the post-2015 sustainable development agenda and other major health initiatives. The World Health Organization (WHO) has a history of promoting actions to achieve equity in health, including efforts to encourage the practice of health inequality monitoring. Health inequality monitoring systems use disaggregated data to identify disadvantaged subgroups within populations and inform equity-oriented health policies, programs, and practices. Objective This paper provides an overview of a number of recent and current WHO initiatives related to health inequality monitoring at the global and/or national level. Design We outline the scope, content, and intended uses/application of the following: Health Equity Monitor database and theme page; State of inequality: reproductive, maternal, newborn, and child health report; Handbook on health inequality monitoring: with a focus on low- and middle-income countries; Health inequality monitoring eLearning module; Monitoring health inequality: an essential step for achieving health equity advocacy booklet and accompanying video series; and capacity building workshops conducted in WHO Member States and Regions. Conclusions The paper concludes by considering how the work of the WHO can be expanded upon to promote the establishment of sustainable and robust inequality monitoring systems across a variety of health topics among Member States and at the global level. PMID:26387506

  8. Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records.

    Science.gov (United States)

    Forsyth, Alexander W; Barzilay, Regina; Hughes, Kevin S; Lui, Dickson; Lorenz, Karl A; Enzinger, Andrea; Tulsky, James A; Lindvall, Charlotta

    2018-02-27

    Clinicians document cancer patients' symptoms in free-text format within electronic health record visit notes. Although symptoms are critically important to quality of life and often herald clinical status changes, computational methods to assess the trajectory of symptoms over time are woefully underdeveloped. To create machine learning algorithms capable of extracting patient-reported symptoms from free-text electronic health record notes. The data set included 103,564 sentences obtained from the electronic clinical notes of 2695 breast cancer patients receiving paclitaxel-containing chemotherapy at two academic cancer centers between May 1996 and May 2015. We manually annotated 10,000 sentences and trained a conditional random field model to predict words indicating an active symptom (positive label), absence of a symptom (negative label), or no symptom at all (neutral label). Sentences labeled by human coder were divided into training, validation, and test data sets. Final model performance was determined on 20% test data unused in model development or tuning. The final model achieved precision of 0.82, 0.86, and 0.99 and recall of 0.56, 0.69, and 1.00 for positive, negative, and neutral symptom labels, respectively. The most common positive symptoms were pain, fatigue, and nausea. Machine-based labeling of 103,564 sentences took two minutes. We demonstrate the potential of machine learning to gather, track, and analyze symptoms experienced by cancer patients during chemotherapy. Although our initial model requires further optimization to improve the performance, further model building may yield machine learning methods suitable to be deployed in routine clinical care, quality improvement, and research applications. Copyright © 2018 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

  9. Forest Health Monitoring: national status, trends, and analysis 2014

    Science.gov (United States)

    Kevin M. Potter; Barbara L. Conkling

    2015-01-01

    The annual national report of the Forest Health Monitoring (FHM) Program of the Forest Service, U.S. Department of Agriculture, presents forest health status and trends from a national or multi-State regional perspective using a variety of sources, introduces new techniques for analyzing forest health data, and summarizes results of recently completed Evaluation...

  10. Forest health monitoring: national status, trends, and analysis 2016

    Science.gov (United States)

    Kevin M. Potter; Barbara L. Conkling

    2017-01-01

    The annual national report of the Forest Health Monitoring (FHM) Program of the Forest Service, U.S. Department of Agriculture, presents forest health status and trends from a national or multi-State regional perspective using a variety of sources, introducesnew techniques for analyzing forest health data, and summarizes results of recently completed...

  11. Forest health monitoring: national status, trends, and analysis 2013

    Science.gov (United States)

    Kevin M. Potter; Barbara L. Conkling

    2015-01-01

    The annual national report of the Forest Health Monitoring (FHM) Program of the Forest Service, U.S. Department of Agriculture, presents forest health status and trends from a national or multi-State regional perspective using a variety of sources, introduces new techniques for analyzing forest health data, and summarizes results of recently completed Evaluation...

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

    DEFF Research Database (Denmark)

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

    2010-01-01

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

  13. Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records.

    Science.gov (United States)

    Ogunyemi, Omolola; Kermah, Dulcie

    2015-01-01

    Annual eye examinations are recommended for diabetic patients in order to detect diabetic retinopathy and other eye conditions that arise from diabetes. Medically underserved urban communities in the US have annual screening rates that are much lower than the national average and could benefit from informatics approaches to identify unscreened patients most at risk of developing retinopathy. Using clinical data from urban safety net clinics as well as public health data from the CDC's National Health and Nutrition Examination Survey, we examined different machine learning approaches for predicting retinopathy from clinical or public health data. All datasets utilized exhibited a class imbalance. Classifiers learned on the clinical data were modestly predictive of retinopathy with the best model having an AUC of 0.72, sensitivity of 69.2% and specificity of 55.9%. Classifiers learned on public health data were not predictive of retinopathy. Successful approaches to detecting latent retinopathy using machine learning could help safety net and other clinics identify unscreened patients who are most at risk of developing retinopathy and the use of ensemble classifiers on clinical data shows promise for this purpose.

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

    NARCIS (Netherlands)

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

    2018-01-01

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

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

    OpenAIRE

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

    2008-01-01

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

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

    Science.gov (United States)

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

    2017-11-01

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

  17. Monitoring illicit psychostimulants and related health issues

    NARCIS (Netherlands)

    Brunt, T.M.

    2012-01-01

    Tibor Brunt onderzocht het Drug Informatie en Monitoring Systeem (DIMS), een landelijk netwerk dat de samenstelling van drugs als ecstasy en cocaïne analyseert. Die middelen zijn van gebruikers zelf afkomstig. Het is een effectieve manier om riskante stoffen te detecteren die aan de drugs worden

  18. Multinational surveys for monitoring eHealth policy implementations

    DEFF Research Database (Denmark)

    Gilstad, Heidi; Faxvaag, Arild; Hyppönen, Hannele

    2014-01-01

    Development of multinational variables for monitoring eHealth policy implementations is a complex task and requires multidisciplinary, knowledgebased international collaboration. Experts in an interdisciplinary workshop identified useful data and pitfalls for comparative variable development...

  19. Distributed Rocket Engine Testing Health Monitoring System, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The on-ground and Distributed Rocket Engine Testing Health Monitoring System (DiRETHMS) provides a system architecture and software tools for performing diagnostics...

  20. Distributed Rocket Engine Testing Health Monitoring System, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — Leveraging the Phase I achievements of the Distributed Rocket Engine Testing Health Monitoring System (DiRETHMS) including its software toolsets and system building...

  1. Towards "Zero" False Positive in Structural Health Monitoring

    National Research Council Canada - National Science Library

    Chiu, Wing K; Chang, F. K; Tian, Daniel T

    2007-01-01

    Structural Health Monitoring (SHM) is one aspect of a revolution based on the use of Smart Materials and Structures technologies that have the potential to provide major gains in structural performance and cost-efficient life management...

  2. Human monitoring, smart health and assisted living techniques and technologies

    CERN Document Server

    Longhi, Sauro; Freddi, Alessandro

    2017-01-01

    This book covers the three main scientific and technological areas critical for improving people's quality of life - namely human monitoring, smart health and assisted living - from both the research and development points of view.

  3. Dynamic Analysis with Fibre Optic Sensors for Structural Health Monitoring

    National Research Council Canada - National Science Library

    Paolozzi, Antonio; Gasbarri, Paolo

    2006-01-01

    Structural Health Monitoring (SHM) is a new frontier of non destructing testing. Often SHM is associated with fibre optic sensors whose signals can be used to identify the structure and consequently its damage...

  4. Multidisciplinary health monitoring of a steel bridge deck structure

    NARCIS (Netherlands)

    Pahlavan, P.L.; Pijpers, R.J.M.; Paulissen, J.H.; Hakkesteegt, H.C.; Jansen, T.H.

    2013-01-01

    Fatigue cracks in orthotropic bridge decks are an important cause for the necessary renovation of existing bridges. Parallel utilization of various technologies based on different physical sensing principles can potentially maximize the efficiency of structural health monitoring (SHM) systems for

  5. Design of smart neonatal health monitoring system using SMCC.

    Science.gov (United States)

    De, Debashis; Mukherjee, Anwesha; Sau, Arkaprabha; Bhakta, Ishita

    2017-02-01

    Automated health monitoring and alert system development is a demanding research area today. Most of the currently available monitoring and controlling medical devices are wired which limits freeness of working environment. Wireless sensor network (WSN) is a better alternative in such an environment. Neonatal intensive care unit is used to take care of sick and premature neonates. Hypothermia is an independent risk factor for neonatal mortality and morbidity. To prevent it an automated monitoring system is required. In this Letter, an automated neonatal health monitoring system is designed using sensor mobile cloud computing (SMCC). SMCC is based on WSN and MCC. In the authors' system temperature sensor, acceleration sensor and heart rate measurement sensor are used to monitor body temperature, acceleration due to body movement and heart rate of neonates. The sensor data are stored inside the cloud. The health person continuously monitors and accesses these data through the mobile device using an Android Application for neonatal monitoring. When an abnormal situation arises, an alert is generated in the mobile device of the health person. By alerting health professional using such an automated system, early care is provided to the affected babies and the probability of recovery is increased.

  6. A machine learning-based framework to identify type 2 diabetes through electronic health records.

    Science.gov (United States)

    Zheng, Tao; Xie, Wei; Xu, Liling; He, Xiaoying; Zhang, Ya; You, Mingrong; Yang, Gong; Chen, You

    2017-01-01

    To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g., via Electronic Health Records (EHR)). However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi-automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate. We propose a data informed framework for identifying subjects with and without T2DM from EHR via feature engineering and machine learning. We evaluate and contrast the identification performance of widely-used machine learning models within our framework, including k-Nearest-Neighbors, Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. Our framework was conducted on 300 patient samples (161 cases, 60 controls and 79 unconfirmed subjects), randomly selected from 23,281 diabetes related cohort retrieved from a regional distributed EHR repository ranging from 2012 to 2014. We apply top-performing machine learning algorithms on the engineered features. We benchmark and contrast the accuracy, precision, AUC, sensitivity and specificity of classification models against the state-of-the-art expert algorithm for identification of T2DM subjects. Our results indicate that the framework achieved high identification performances (∼0.98 in average AUC), which are much higher than the state-of-the-art algorithm (0.71 in AUC). Expert algorithm-based identification of T2DM subjects from EHR is often hampered by the high missing rates due to their conservative selection criteria. Our framework leverages machine learning and feature

  7. Adaptive and Online Health Monitoring System for Autonomous Aircraft

    OpenAIRE

    Mokhtar, Maizura; Zapatel-Bayo, Sergio Z.; Hussein, Saed; Howe, Joe M.

    2012-01-01

    Good situation awareness is one of the key attributes required to maintain safe flight, especially for an Unmanned Aerial System (UAS). Good situation awareness can be achieved by incorporating an Adaptive Health Monitoring System (AHMS) to the aircraft. The AHMS monitors the flight outcome or flight behaviours of the aircraft based on its external environmental conditions and the behaviour of its internal systems. The AHMS does this by associating a health value to the aircraft's behaviour b...

  8. Patient monitoring in mobile health: opportunities and challenges.

    Science.gov (United States)

    Mohammadzadeh, Niloofar; Safdari, Reza

    2014-01-01

    In most countries chronic diseases lead to high health care costs and reduced productivity of people in society. The best way to reduce costs of health sector and increase the empowerment of people is prevention of chronic diseases and appropriate health activities management through monitoring of patients. To enjoy the full benefits of E-health, making use of methods and modern technologies is very important. This literature review articles were searched with keywords like Patient monitoring, Mobile Health, and Chronic Disease in Science Direct, Google Scholar and Pub Med databases without regard to the year of publications. Applying remote medical diagnosis and monitoring system based on mobile health systems can help significantly to reduce health care costs, correct performance management particularly in chronic disease management. Also some challenges are in patient monitoring in general and specific aspects like threats to confidentiality and privacy, technology acceptance in general and lack of system interoperability with electronic health records and other IT tools, decrease in face to face communication between doctor and patient, sudden interruptions of telecommunication networks, and device and sensor type in specific aspect. It is obvious identifying the opportunities and challenges of mobile technology and reducing barriers, strengthening the positive points will have a significant role in the appropriate planning and promoting the achievements of the health care systems based on mobile and helps to design a roadmap for improvement of mobile health.

  9. Application of ubiquitous computing in personal health monitoring systems.

    Science.gov (United States)

    Kunze, C; Grossmann, U; Stork, W; Müller-Glaser, K D

    2002-01-01

    A possibility to significantly reduce the costs of public health systems is to increasingly use information technology. The Laboratory for Information Processing Technology (ITIV) at the University of Karlsruhe is developing a personal health monitoring system, which should improve health care and at the same time reduce costs by combining micro-technological smart sensors with personalized, mobile computing systems. In this paper we present how ubiquitous computing theory can be applied in the health-care domain.

  10. Developing Novel Machine Learning Algorithms to Improve Sedentary Assessment for Youth Health Enhancement.

    Science.gov (United States)

    Golla, Gowtham Kumar; Carlson, Jordan A; Huan, Jun; Kerr, Jacqueline; Mitchell, Tarrah; Borner, Kelsey

    2016-10-01

    Sedentary behavior of youth is an important determinant of health. However, better measures are needed to improve understanding of this relationship and the mechanisms at play, as well as to evaluate health promotion interventions. Wearable accelerometers are considered as the standard for assessing physical activity in research, but do not perform well for assessing posture (i.e., sitting vs. standing), a critical component of sedentary behavior. The machine learning algorithms that we propose for assessing sedentary behavior will allow us to re-examine existing accelerometer data to better understand the association between sedentary time and health in various populations. We collected two datasets, a laboratory-controlled dataset and a free-living dataset. We trained machine learning classifiers separately on each dataset and compared performance across datasets. The classifiers predict five postures: sit, stand, sit-stand, stand-sit, and stand\\walk. We compared a manually constructed Hidden Markov model (HMM) with an automated HMM from existing software. The manually constructed HMM gave more F1-Macro score on both datasets.

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

    Science.gov (United States)

    Rose, Sherri

    2018-03-11

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

  12. Impact of Health Care Employees’ Job Satisfaction on Organizational Performance Support Vector Machine Approach

    Directory of Open Access Journals (Sweden)

    CEMIL KUZEY

    2018-01-01

    Full Text Available This study is undertaken to search for key factors that contribute to job satisfaction among health care workers, and also to determine the impact of these underlying dimensions of employee satisfaction on organizational performance. Exploratory Factor Analysis (EFA is applied to initially uncover the key factors, and then, in the next stage of analysis, a popular data mining technique, Support Vector Machine (SVM is employed on a sample of 249 to determine the impact of job satisfaction factors on organizational performance. According to the proposed model, the main factors are revealed to be management’s attitude, pay/reward, job security and colleagues.

  13. m-Health 2.0: New perspectives on mobile health, Machine Learning and Big Data Analytics.

    Science.gov (United States)

    Istepanian, Robert S H; Al-Anzi, Turki

    2018-06-08

    Mobile health (m-Health) has been repeatedly called the biggest technological breakthrough of our modern times. Similarly, the concept of big data in the context of healthcare is considered one of the transformative drivers for intelligent healthcare delivery systems. In recent years, big data has become increasingly synonymous with mobile health, however key challenges of 'Big Data and mobile health', remain largely untackled. This is becoming particularly important with the continued deluge of the structured and unstructured data sets generated on daily basis from the proliferation of mobile health applications within different healthcare systems and products globally. The aim of this paper is of twofold. First we present the relevant big data issues from the mobile health (m-Health) perspective. In particular we discuss these issues from the technological areas and building blocks (communications, sensors and computing) of mobile health and the newly defined (m-Health 2.0) concept. The second objective is to present the relevant rapprochement issues of big m-Health data analytics with m-Health. Further, we also present the current and future roles of machine and deep learning within the current smart phone centric m-health model. The critical balance between these two important areas will depend on how different stakeholder from patients, clinicians, healthcare providers, medical and m-health market businesses and regulators will perceive these developments. These new perspectives are essential for better understanding the fine balance between the new insights of how intelligent and connected the future mobile health systems will look like and the inherent risks and clinical complexities associated with the big data sets and analytical tools used in these systems. These topics will be subject for extensive work and investigations in the foreseeable future for the areas of data analytics, computational and artificial intelligence methods applied for mobile health

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

    Science.gov (United States)

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

    2010-01-01

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

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

    Science.gov (United States)

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

    2018-03-01

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

  17. Physical health care monitoring for people with serious mental illness.

    Science.gov (United States)

    Tosh, Graeme; Clifton, Andrew V; Xia, Jun; White, Margueritte M

    2014-01-17

    Current guidance suggests that we should monitor the physical health of people with serious mental illness, and there has been a significant financial investment over recent years to provide this. To assess the effectiveness of physical health monitoring, compared with standard care for people with serious mental illness. We searched the Cochrane Schizophrenia Group Trials Register (October 2009, update in October 2012), which is based on regular searches of CINAHL, EMBASE, MEDLINE and PsycINFO. All randomised clinical trials focusing on physical health monitoring versus standard care, or comparing i) self monitoring versus monitoring by a healthcare professional; ii) simple versus complex monitoring; iii) specific versus non-specific checks; iv) once only versus regular checks; or v) different guidance materials. Initially, review authors (GT, AC, SM) independently screened the search results and identified three studies as possibly fulfilling the review's criteria. On examination, however, all three were subsequently excluded. Forty-two additional citations were identified in October 2012 and screened by two review authors (JX and MW), 11 of which underwent full screening. No relevant randomised trials which assess the effectiveness of physical health monitoring in people with serious mental illness have been completed. We identified one ongoing study. There is still no evidence from randomised trials to support or refute current guidance and practice. Guidance and practice are based on expert consensus, clinical experience and good intentions rather than high quality evidence.

  18. Structural Health Monitoring of Tall Buildings with Numerical Integrator and Convex-Concave Hull Classification

    Directory of Open Access Journals (Sweden)

    Suresh Thenozhi

    2012-01-01

    Full Text Available An important objective of health monitoring systems for tall buildings is to diagnose the state of the building and to evaluate its possible damage. In this paper, we use our prototype to evaluate our data-mining approach for the fault monitoring. The offset cancellation and high-pass filtering techniques are combined effectively to solve common problems in numerical integration of acceleration signals in real-time applications. The integration accuracy is improved compared with other numerical integrators. Then we introduce a novel method for support vector machine (SVM classification, called convex-concave hull. We use the Jarvis march method to decide the concave (nonconvex hull for the inseparable points. Finally the vertices of the convex-concave hull are applied for SVM training.

  19. Nuclear analysis methods in monitoring occupational health

    International Nuclear Information System (INIS)

    Clayton, E.

    1985-01-01

    With the increasing industrialisation of the world has come an increase in exposure to hazardous chemicals. Their effect on the body depends upon the concentration of the element in the work environment; its chemical form; the possible different routes of intake; and the individual's biological response to the chemical. Nuclear techniques of analysis such as neutron activation analysis (NAA) and proton induced X-ray emission analysis (PIXE), have played an important role in understanding the effects hazardous chemicals can have on occupationally exposed workers. In this review, examples of their application, mainly in monitoring exposure to heavy metals is discussed

  20. Big Data Analysis for Personalized Health Activities: Machine Learning Processing for Automatic Keyword Extraction Approach

    Directory of Open Access Journals (Sweden)

    Jun-Ho Huh

    2018-04-01

    Full Text Available The obese population is increasing rapidly due to the change of lifestyle and diet habits. Obesity can cause various complications and is becoming a social disease. Nonetheless, many obese patients are unaware of the medical treatments that are right for them. Although a variety of online and offline obesity management services have been introduced, they are still not enough to attract the attention of users and are not much of help to solve the problem. Obesity healthcare and personalized health activities are the important factors. Since obesity is related to lifestyle habits, eating habits, and interests, I concluded that the big data analysis of these factors could deduce the problem. Therefore, I collected big data by applying the machine learning and crawling method to the unstructured citizen health data in Korea and the search data of Naver, which is a Korean portal company, and Google for keyword analysis for personalized health activities. It visualized the big data using text mining and word cloud. This study collected and analyzed the data concerning the interests related to obesity, change of interest on obesity, and treatment articles. The analysis showed a wide range of seasonal factors according to spring, summer, fall, and winter. It also visualized and completed the process of extracting the keywords appropriate for treatment of abdominal obesity and lower body obesity. The keyword big data analysis technique for personalized health activities proposed in this paper is based on individual’s interests, level of interest, and body type. Also, the user interface (UI that visualizes the big data compatible with Android and Apple iOS. The users can see the data on the app screen. Many graphs and pictures can be seen via menu, and the significant data values are visualized through machine learning. Therefore, I expect that the big data analysis using various keywords specific to a person will result in measures for personalized

  1. Optimal Sensor Selection for Health Monitoring Systems

    Science.gov (United States)

    Santi, L. Michael; Sowers, T. Shane; Aguilar, Robert B.

    2005-01-01

    Sensor data are the basis for performance and health assessment of most complex systems. Careful selection and implementation of sensors is critical to enable high fidelity system health assessment. A model-based procedure that systematically selects an optimal sensor suite for overall health assessment of a designated host system is described. This procedure, termed the Systematic Sensor Selection Strategy (S4), was developed at NASA John H. Glenn Research Center in order to enhance design phase planning and preparations for in-space propulsion health management systems (HMS). Information and capabilities required to utilize the S4 approach in support of design phase development of robust health diagnostics are outlined. A merit metric that quantifies diagnostic performance and overall risk reduction potential of individual sensor suites is introduced. The conceptual foundation for this merit metric is presented and the algorithmic organization of the S4 optimization process is described. Representative results from S4 analyses of a boost stage rocket engine previously under development as part of NASA's Next Generation Launch Technology (NGLT) program are presented.

  2. Watershed health assessment to monitor land degradation

    Science.gov (United States)

    Hamidreza Sadeghi, Seyed; Hazbavi, Zeinab; Cerdà, Artemi

    2017-04-01

    Land degradation is a worldwide issue that affects the Planet and the fate of the humankind (Cerdà et al., 2009; Choudhury et al., 2016; Fernández et al., 2016; Ferreira et al., 2016). Several processes affect the sustainability of the ecosystems, from soil erosion to soil compation, deforestation, Climate Change or water, soil and air pollution (Sadeghi et al., 2015a; 2015b; Gómez-Acanta et al., 2016; Mengistu et al., 2016; Mukai, 2016). Several ecosystem theories have been presented in the scientific literatures to monitor land degradation (Cerdà et al., 2016; Davudirad et al., 2016; Fava et al., 2016; Mahyou et al., 2016; Soulard et al., 2016). Besides the scientific tasks of improving the indication, the conviction of the potential users to change their concepts toward a higher consideration of ecosystem attributes, and toward a fruitful application of the health or integrity concepts, will be a main task of future activities. Reliability, resilience and vulnerability (R-R-V) indicators are often used in combination for quantifying risk and decision making in many systems. However, the use of hydrological series data for R-R-V computations has been rather limited. Toward this, the overall objective of this paper is to conduct a risk assessment analysis on stream flow discharge from Shazand Watershed located in the south western of Markazi Province in Iran for the period of 1972-2014 using R-R-V indicators. Based on the R-R-V analysis conducted in this study, the stream flow discharge of the study region followed a cyclic pattern with a decreasing trend. The results further showed a decreasing trend in reliability and resilience and an increasing trend in vulnerability in the Shazand Watershed. It may be concluded that the Shazand Watershed was in overall in unhealthy condition from view of stream flow discharge. Acknowledgements This research was funded by the European Union Seventh Framework Programme (FP7/2007-2013) under grant no. 603498 (RECARE Project

  3. Structure health monitoring system using internet and database technologies

    International Nuclear Information System (INIS)

    Kwon, Il Bum; Kim, Chi Yeop; Choi, Man Yong; Lee, Seung Seok

    2003-01-01

    Structural health monitoring system should developed to be based on internet and database technology in order to manage efficiently large structures. This system is operated by internet connected with the side of structures. The monitoring system has some functions: self monitoring, self diagnosis, and self control etc. Self monitoring is the function of sensor fault detection. If some sensors are not normally worked, then this system can detect the fault sensors. Also Self diagnosis function repair the abnormal condition of sensors. And self control is the repair function of the monitoring system. Especially, the monitoring system can identify the replacement of sensors. For further study, the real application test will be performed to check some unconvince.

  4. Structural health monitoring system using internet and database technologies

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Chi Yeop; Choi, Man Yong; Kwon, Il Bum; Lee, Seung Seok [Nonstructive Measurment Lab., KRISS, Daejeon (Korea, Republic of)

    2003-07-01

    Structure health monitoring system should develope to be based on internet and database technology in order to manage efficiency large structures. This system is operated by internet connected with the side of structures. The monitoring system has some functions: self monitoring, self diagnosis, and self control etc. Self monitoring is the function of sensor fault detection. If some sensors are not normally worked, then this system can detect the fault sensors. Also Self diagnosis function repair the abnormal condition of sensors. And self control is the repair function of the monitoring system. Especially, the monitoring system can identify the replacement of sensors. For further study, the real application test will be performed to check some unconviniences.

  5. Structure health monitoring system using internet and database technologies

    Energy Technology Data Exchange (ETDEWEB)

    Kwon, Il Bum; Kim, Chi Yeop; Choi, Man Yong; Lee, Seung Seok [Smart Measurment Group. Korea Resarch Institute of Standards and Science, Saejeon (Korea, Republic of)

    2003-05-15

    Structural health monitoring system should developed to be based on internet and database technology in order to manage efficiently large structures. This system is operated by internet connected with the side of structures. The monitoring system has some functions: self monitoring, self diagnosis, and self control etc. Self monitoring is the function of sensor fault detection. If some sensors are not normally worked, then this system can detect the fault sensors. Also Self diagnosis function repair the abnormal condition of sensors. And self control is the repair function of the monitoring system. Especially, the monitoring system can identify the replacement of sensors. For further study, the real application test will be performed to check some unconvince.

  6. Structural health monitoring system using internet and database technologies

    International Nuclear Information System (INIS)

    Kim, Chi Yeop; Choi, Man Yong; Kwon, Il Bum; Lee, Seung Seok

    2003-01-01

    Structure health monitoring system should develope to be based on internet and database technology in order to manage efficiency large structures. This system is operated by internet connected with the side of structures. The monitoring system has some functions: self monitoring, self diagnosis, and self control etc. Self monitoring is the function of sensor fault detection. If some sensors are not normally worked, then this system can detect the fault sensors. Also Self diagnosis function repair the abnormal condition of sensors. And self control is the repair function of the monitoring system. Especially, the monitoring system can identify the replacement of sensors. For further study, the real application test will be performed to check some unconviniences.

  7. Monitoring intervention coverage in the context of universal health coverage.

    Directory of Open Access Journals (Sweden)

    Ties Boerma

    2014-09-01

    Full Text Available Monitoring universal health coverage (UHC focuses on information on health intervention coverage and financial protection. This paper addresses monitoring intervention coverage, related to the full spectrum of UHC, including health promotion and disease prevention, treatment, rehabilitation, and palliation. A comprehensive core set of indicators most relevant to the country situation should be monitored on a regular basis as part of health progress and systems performance assessment for all countries. UHC monitoring should be embedded in a broad results framework for the country health system, but focus on indicators related to the coverage of interventions that most directly reflect the results of UHC investments and strategies in each country. A set of tracer coverage indicators can be selected, divided into two groups-promotion/prevention, and treatment/care-as illustrated in this paper. Disaggregation of the indicators by the main equity stratifiers is critical to monitor progress in all population groups. Targets need to be set in accordance with baselines, historical rate of progress, and measurement considerations. Critical measurement gaps also exist, especially for treatment indicators, covering issues such as mental health, injuries, chronic conditions, surgical interventions, rehabilitation, and palliation. Consequently, further research and proxy indicators need to be used in the interim. Ideally, indicators should include a quality of intervention dimension. For some interventions, use of a single indicator is feasible, such as management of hypertension; but in many areas additional indicators are needed to capture quality of service provision. The monitoring of UHC has significant implications for health information systems. Major data gaps will need to be filled. At a minimum, countries will need to administer regular household health surveys with biological and clinical data collection. Countries will also need to improve the

  8. Summary Report: Forest Health Monitoring in the South, 1991

    Science.gov (United States)

    William A. Bechtold; William H. Hoffard; Robert L. Anderson

    1992-01-01

    The USDA Forest Service and the U.S. Environmental Protection Agency have launched a joint program to monitor the health of forests iu the United States. The program is still in the initial phases of implementation, but several indicators of forest health are undergoiug development and permanent plots have been established in 12 States. This report contains...

  9. Mobile Patient Monitoring: the MobiHealth System

    NARCIS (Netherlands)

    Konstantas, D.; van Halteren, Aart; Bults, Richard G.A.; Wac, K.E.; Widya, I.A.; Dokovski, N.T.; Jones, Valerie M.; Dokovsky, Nicolai; Koprinkov, G.T.; Herzog, Rainer; Bos, L.; Laxminarayan, S.

    2004-01-01

    The forthcoming wide availability of high bandwidth public wireless networks will give rise to new mobile health care services. Towards this direction the MobiHealth1 project has developed and trialed a highly customisable vital signals’ monitoring system based on a Body Area Network (BAN) and an

  10. Summary of Forest health monitoring: 2006 national technical report

    Science.gov (United States)

    Mark J. Ambrose

    2009-01-01

    Forest Health Monitoring (FHM), together with cooperating researchers both in and outside of the Forest Service, continues to investigate a variety of issues relating to forest health. This report provides some of the latest analyses and results. The broad range of indicators presented demonstrates one reason it can be difficult to draw general conclusions about the...

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

    DEFF Research Database (Denmark)

    Pilny, Lukas; Bissacco, Giuliano

    2015-01-01

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

  12. Monitoring and Benchmarking eHealth in the Nordic Countries.

    Science.gov (United States)

    Nøhr, Christian; Koch, Sabine; Vimarlund, Vivian; Gilstad, Heidi; Faxvaag, Arild; Hardardottir, Gudrun Audur; Andreassen, Hege K; Kangas, Maarit; Reponen, Jarmo; Bertelsen, Pernille; Villumsen, Sidsel; Hyppönen, Hannele

    2018-01-01

    The Nordic eHealth Research Network, a subgroup of the Nordic Council of Ministers eHealth group, is working on developing indicators to monitor progress in availability, use and outcome of eHealth applications in the Nordic countries. This paper reports on the consecutive analysis of National eHealth policies in the Nordic countries from 2012 to 2016. Furthermore, it discusses the consequences for the development of indicators that can measure changes in the eHealth environment arising from the policies. The main change in policies is reflected in a shift towards more stakeholder involvement and intensified focus on clinical infrastructure. This change suggests developing indicators that can monitor understandability and usability of eHealth systems, and the use and utility of shared information infrastructure from the perspective of the end-users - citizens/patients and clinicians in particular.

  13. Health and radiation: Surveillance and monitoring

    International Nuclear Information System (INIS)

    Reitan, J.B.; Langmark, F.

    1988-01-01

    Assuming a zero risk of low-dose radiation would allow society to save a lot of resources currently used in radiation protection. If this assumption should turn out to be wrong, however, the society would face a serious cancer problem within 20-40 years. Thus, the present resources allocated to radiation protection seem justified from an ethical and moral point of view. Such radiation protection should also include monitoring of naturally enhanced radiation and possibilities of contamination, and ecological changes from energy production, waste deposition and fertilizing. The weaker parts of establishing the dose/effect relationship are radiation biology and radiation medicine. Therefore, continued research in these disciplines should be encouraged

  14. 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.

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

    Science.gov (United States)

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

    2010-01-01

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

  16. Modeling workflow to design machine translation applications for public health practice.

    Science.gov (United States)

    Turner, Anne M; Brownstein, Megumu K; Cole, Kate; Karasz, Hilary; Kirchhoff, Katrin

    2015-02-01

    Provide a detailed understanding of the information workflow processes related to translating health promotion materials for limited English proficiency individuals in order to inform the design of context-driven machine translation (MT) tools for public health (PH). We applied a cognitive work analysis framework to investigate the translation information workflow processes of two large health departments in Washington State. Researchers conducted interviews, performed a task analysis, and validated results with PH professionals to model translation workflow and identify functional requirements for a translation system for PH. The study resulted in a detailed description of work related to translation of PH materials, an information workflow diagram, and a description of attitudes towards MT technology. We identified a number of themes that hold design implications for incorporating MT in PH translation practice. A PH translation tool prototype was designed based on these findings. This study underscores the importance of understanding the work context and information workflow for which systems will be designed. Based on themes and translation information workflow processes, we identified key design guidelines for incorporating MT into PH translation work. Primary amongst these is that MT should be followed by human review for translations to be of high quality and for the technology to be adopted into practice. The time and costs of creating multilingual health promotion materials are barriers to translation. PH personnel were interested in MT's potential to improve access to low-cost translated PH materials, but expressed concerns about ensuring quality. We outline design considerations and a potential machine translation tool to best fit MT systems into PH practice. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. Technical Specifications of Structural Health Monitoring for Highway Bridges: New Chinese Structural Health Monitoring Code

    Directory of Open Access Journals (Sweden)

    Fernando Moreu

    2018-03-01

    Full Text Available Governments and professional groups related to civil engineering write and publish standards and codes to protect the safety of critical infrastructure. In recent decades, countries have developed codes and standards for structural health monitoring (SHM. During this same period, rapid growth in the Chinese economy has led to massive development of civil engineering infrastructure design and construction projects. In 2016, the Ministry of Transportation of the People’s Republic of China published a new design code for SHM systems for large highway bridges. This document is the first technical SHM code by a national government that enforces sensor installation on highway bridges. This paper summarizes the existing international technical SHM codes for various countries and compares them with the new SHM code required by the Chinese Ministry of Transportation. This paper outlines the contents of the new Chinese SHM code and explains its relevance for the safety and management of large bridges in China, introducing key definitions of the Chinese–United States SHM vocabulary and their technical significance. Finally, this paper discusses the implications for the design and implementation of a future SHM codes, with suggestions for similar efforts in United States and other countries.

  18. 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.

  19. Machine Learning

    CERN Multimedia

    CERN. Geneva

    2017-01-01

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

  20. Structural health monitoring of grandstands: a review

    Directory of Open Access Journals (Sweden)

    Gómez-Casero Fuentes Miguel Ángel

    2015-01-01

    Full Text Available This article is a state of the art about Grandstands. The Grandstands are slender structures designed to accommodate a large number of people, which are specially under the actions of wind and the human-structure interaction. Over the years, it has been discuss of this topic, although still the number of publications still remain low. The human-structure interaction is a complex issue, where the loads may have different behaviours, depending many factors, including: type of audience (active or passive, public behaviour (jumping, walking, running, clapping, vandal loads, type of event (sports, concerts, meeting, position and posture of the individual, even influences the type of seat (with or without back, stiffness. However, the structure will behave differently when empty or fully occupied. Another load to consider is the wind, especially when the structure has a roof, screens, large-scale advertising, etc. These two types of loads can interact together, which implies an increase in the normal number of load combinations to consider. There are biomechanical models of human behaviour, used for design these types of structures. In addition, there are mathematical models to simulate the behaviour of the Grandstands by numerical methods. In recent years, all these models are throwing good results, against laboratory tests performed. It has also been monitored real Grandstands. This paper compiles all existing information on this topic.

  1. JAXA's activities for environmental health monitoring

    Science.gov (United States)

    Murakami, Hiroshi

    2014-11-01

    In the first ten years after establishment of the Japan Aerospace eXploration Agency (JAXA) in 2003, our focuses were mainly on technical development (hardware and software) and accumulation of application research. In the next decade, we focus more on solution on social issues using innovative space science technology. Currently, JAXA is operating and developing several earth observation satellites and sensors: Greenhouse gases Observing SATellite (GOSAT) "IBUKI", Global Change Observation Mission - Water "SHIZUKU" (GCOM-W), Global Precipitation Measurement/Dual- frequency Precipitation Radar (GPM/DPR), Advanced Land Observing Satellite-2 "DAICHI-2" (ALOS-2), Global Change Observation Mission - Climate (GCOM-C), Earth Cloud, Aerosol and Radiation Explorer (EarthCARE), and GOSAT-2. They will provide essential environmental parameters, such as aerosols, clouds, land vegetation, ocean color, GHGs, and so on. In addition to the above missions, we are studying new instruments (altimeter, LIDAR, detectors, optical components) to obtain new parameters. Our activities will advance to provide essential inputs for diagnosis, prediction, and management of climate change, environmental assessment, and disaster monitoring.

  2. Development of a Plant Health Index Monitor

    International Nuclear Information System (INIS)

    Heo, Gyun Young; An, Sang Ha; Seo, Ho Joon; Kim, Cho

    2010-01-01

    Since 2008, BNF Technology Inc. and Kyung Hee University have developed the 'Plant Health Index (PHI)' which is a software package to detect 'unhealthy conditions' of plant equipment in advance. While the difference between a setpoint and an operational condition is called 'process margin', the residual between an anticipated normal condition and an operational condition is called 'process uncertainty' or 'healthiness' in this study. It is obvious that the anomalies in process uncertainty can be observed earlier than those in process margin, which is the concept of 'early-warning' proposed in the recent condition-based maintenance (CBM) studies. One of the key factors for implementing the early warning capability should be how to expect the anticipated normal conditions using available information. The PHI was developed on the basis of empirical models, and we have published a few papers with regarding to the core technologies of the PHI. However, the overall architecture and features of the PHI have not been introduced to academic area so far. This paper delineates the overview of the PHI, and focuses on the recently developed module, which is the health index generator

  3. Development of a Plant Health Index Monitor

    Energy Technology Data Exchange (ETDEWEB)

    Heo, Gyun Young [Kyung Hee University, Yongin (Korea, Republic of); An, Sang Ha [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of); Seo, Ho Joon [BNF Technology Inc., Daejeon (Korea, Republic of); Kim, Cho [Korea South-East Power Co., Seoul (Korea, Republic of)

    2010-05-15

    Since 2008, BNF Technology Inc. and Kyung Hee University have developed the 'Plant Health Index (PHI)' which is a software package to detect 'unhealthy conditions' of plant equipment in advance. While the difference between a setpoint and an operational condition is called 'process margin', the residual between an anticipated normal condition and an operational condition is called 'process uncertainty' or 'healthiness' in this study. It is obvious that the anomalies in process uncertainty can be observed earlier than those in process margin, which is the concept of 'early-warning' proposed in the recent condition-based maintenance (CBM) studies. One of the key factors for implementing the early warning capability should be how to expect the anticipated normal conditions using available information. The PHI was developed on the basis of empirical models, and we have published a few papers with regarding to the core technologies of the PHI. However, the overall architecture and features of the PHI have not been introduced to academic area so far. This paper delineates the overview of the PHI, and focuses on the recently developed module, which is the health index generator

  4. Regional Geographic Information Systems of Health and Environmental Monitoring

    Directory of Open Access Journals (Sweden)

    Kurolap Semen A.

    2016-12-01

    Full Text Available The article describes a new scientific and methodological approach to designing geographic information systems of health and environmental monitoring for urban areas. Geographic information systems (GIS are analytical tools of the regional health and environmental monitoring; they are used for an integrated assessment of the environmental status of a large industrial centre or a part of it. The authors analyse the environmental situation in Voronezh, a major industrial city, located in the Central Black Earth Region with a population of more than 1 million people. The proposed research methodology is based on modern approaches to the assessment of health risks caused by adverse environmental conditions. The research work was implemented using a GIS and multicriteria probabilistic and statistical evaluation to identify cause-and-effect links, a combination of action and reaction, in the dichotomy ‘environmental factors — public health’. The analysis of the obtained statistical data confirmed an increase in childhood diseases in some areas of the city. Environmentally induced diseases include congenital malformations, tumors, endocrine and urogenital pathologies. The main factors having an adverse impact on health are emissions of carcinogens into the atmosphere and the negative impact of transport on the environment. The authors identify and characterize environmentally vulnerable parts of the city and developed principles of creating an automated system of health monitoring and control of environmental risks. The article offers a number of measures aimed at the reduction of environmental risks, better protection of public health and a more efficient environmental monitoring.

  5. 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.

  6. Power and phase monitoring system for the lower hybrid phased array heating system on ATC machine

    International Nuclear Information System (INIS)

    Reed, B.W.

    1975-01-01

    A four waveguide phased array slow wave structure has been constructed to couple microwave energy into plasma in the ATC Tokamac at Princeton. Theory has indicated that the coupling of power into the plasma column is a strong function of the imposed fourier spectrum at the antenna aperture. To optimize heating, and to verify theoretical results, a precision amplitude and phase monitoring system has been designed and constructed. The system data output is routed to an IBM 1800 computer where the fourier spectrum in n/sub parallel/ space is computed for discrete increments of time during an RF pulse. Computer output data is used to update the adjustment of transmission line parameters in between pulses

  7. System Identification of Wind Turbines for Structural Health Monitoring

    DEFF Research Database (Denmark)

    Perisic, Nevena

    Structural health monitoring is a multi-disciplinary engineering field that should allow the actual wind turbine maintenance programmes to evolve to the next level, hence increasing safety and reliability and decreasing turbines downtime. The main idea is to have a sensing system on the structure...... cases are considered, two practical problems from the wind industry are studied, i.e. monitoring of the gearbox shaft torque and the tower root bending moments. The second part of the thesis is focused on the influence of friction on the health of the wind turbine and on the nonlinear identification...... that monitors the system responses and notifies the operator when damages or degradations have been detected. However, some of the response signals that contain important information about the health of the wind turbine components cannot be directly measured, or measuring them is highly complex and costly...

  8. Smart Materials in Structural Health Monitoring, Control and Biomechanics

    CERN Document Server

    Soh, Chee-Kiong; Bhalla, Suresh

    2012-01-01

    "Smart Materials in Structural Health Monitoring, Control and Biomechanics" presents the latest developments in structural health monitoring, vibration control and biomechanics using smart materials. The book mainly focuses on piezoelectric, fibre optic and ionic polymer metal composite materials. It introduces concepts from the very basics and leads to advanced modelling (analytical/ numerical), practical aspects (including software/ hardware issues) and case studies spanning civil, mechanical and aerospace structures, including bridges, rocks and underground structures. This book is intended for practicing engineers, researchers from academic and R&D institutions and postgraduate students in the fields of smart materials and structures, structural health monitoring, vibration control and biomedical engineering. Professor Chee-Kiong Soh and Associate Professor Yaowen Yang both work at the School of Civil and Environmental Engineering, Nanyang Technological University, Singapore. Dr. Suresh Bhalla is an A...

  9. Structural Health Monitoring Analysis for the Orbiter Wing Leading Edge

    Science.gov (United States)

    Yap, Keng C.

    2010-01-01

    This viewgraph presentation reviews Structural Health Monitoring Analysis for the Orbiter Wing Leading Edge. The Wing Leading Edge Impact Detection System (WLE IDS) and the Impact Analysis Process are also described to monitor WLE debris threats. The contents include: 1) Risk Management via SHM; 2) Hardware Overview; 3) Instrumentation; 4) Sensor Configuration; 5) Debris Hazard Monitoring; 6) Ascent Response Summary; 7) Response Signal; 8) Distribution of Flight Indications; 9) Probabilistic Risk Analysis (PRA); 10) Model Correlation; 11) Impact Tests; 12) Wing Leading Edge Modeling; 13) Ascent Debris PRA Results; and 14) MM/OD PRA Results.

  10. The effect of social demographic factors, snack consumption and vending machine use on oral health of children living in London.

    Science.gov (United States)

    Maliderou, M; Reeves, S; Noble, C

    2006-10-07

    To investigate the effect of socio-economic status, sugar, snack consumption and vending machine use on the prevalence and severity of caries (DMF) in children. An observational study was carried out in a dental practice in inner city London. Sixty children were asked to complete a questionnaire and a three day food and drink diary. After a dental examination the number of decayed (D), missing (M) or filled (F) teeth provided a DMF score. Anova and Pearsons correlations were used to analyse the data statistically. Children from social groups I and II consumed significantly less (P vending machine less often than children from other social groups. Children from Social groups I, II and III had significantly lower DMF scores. The average DMF from social group I children was 0.5 +/- 0.6, whilst group IV children had the greatest incidence and a DMF of 4.6 +/- 0.8. Significant correlations were identified between DMF and sugar, confectionery and crisp consumption and vending machine use, and a negative correlation between DMF and vegetable consumption. Socio-economic status and access to vending machines were found to have a significant effect on sugar intakes, foods choices, and dental health. The removal of vending machines from schools or at least installing 'healthy' vending machines is recommended. Health promotion programmes that account for social groups and snacking habits that are cost effective are required.

  11. Flexible Sensing Electronics for Wearable/Attachable Health Monitoring.

    Science.gov (United States)

    Wang, Xuewen; Liu, Zheng; Zhang, Ting

    2017-07-01

    Wearable or attachable health monitoring smart systems are considered to be the next generation of personal portable devices for remote medicine practices. Smart flexible sensing electronics are components crucial in endowing health monitoring systems with the capability of real-time tracking of physiological signals. These signals are closely associated with body conditions, such as heart rate, wrist pulse, body temperature, blood/intraocular pressure and blood/sweat bio-information. Monitoring such physiological signals provides a convenient and non-invasive way for disease diagnoses and health assessments. This Review summarizes the recent progress of flexible sensing electronics for their use in wearable/attachable health monitoring systems. Meanwhile, we present an overview of different materials and configurations for flexible sensors, including piezo-resistive, piezo-electrical, capacitive, and field effect transistor based devices, and analyze the working principles in monitoring physiological signals. In addition, the future perspectives of wearable healthcare systems and the technical demands on their commercialization are briefly discussed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Applications of fiber optic sensors in concrete structural health monitoring

    Science.gov (United States)

    Dai, Jingyun; Zhang, Wentao; Sun, Baochen; Du, Yanliang

    2007-11-01

    The research of fiber optic extrinsic Fabry-Perot interferometer (EFPI) sensors and their applications in concrete structural health monitoring are presented in this paper. Different types of fiber optic EFPI sensors are designed and fabricated. Experiments are carried out to test the performance of the sensors. The results show that the sensors have good linearity and stability. The applications of the fiber optic EFPI sensors in concrete structural health monitoring are also introduced. Ten fiber optic sensors are embedded into one section of the Liaohe Bridge in Qinghuangdao-Shenyang Railway. Field test demonstrates that the results of fiber optic sensors agree well with conventional strain gauges.

  13. Vibration-based structural health monitoring of harbor caisson structure

    Science.gov (United States)

    Lee, So-Young; Lee, So-Ra; Kim, Jeong-Tae

    2011-04-01

    This study presents vibration-based structural health monitoring method in foundation-structure interface of harbor caisson structure. In order to achieve the objective, the following approaches are implemented. Firstly, vibration-based response analysis method is selected and structural health monitoring (SHM) technique is designed for harbor caisson structure. Secondly, the performance of designed SHM technique for harbor structure is examined by FE analysis. Finally, the applicability of designed SHM technique for harbor structure is evaluated by dynamic tests on a lab-scaled caisson structure.

  14. Integrating Social Media Monitoring Into Public Health Emergency Response Operations.

    Science.gov (United States)

    Hadi, Tamer A; Fleshler, Keren

    2016-10-01

    Social media monitoring for public health emergency response and recovery is an essential response capability for any health department. The value of social media for emergency response lies not only in the capacity to rapidly communicate official and critical incident information, but as a rich source of incoming data that can be gathered to inform leadership decision-making. Social media monitoring is a function that can be formally integrated into the Incident Command System of any response agency. The approach to planning and required resources, such as staffing, logistics, and technology, is flexible and adaptable based on the needs of the agency and size and scope of the emergency. The New York City Department of Health and Mental Hygiene has successfully used its Social Media Monitoring Team during public health emergency responses and planned events including major Ebola and Legionnaires' disease responses. The concepts and implementations described can be applied by any agency, large or small, interested in building a social media monitoring capacity. (Disaster Med Public Health Preparedness. 2016;page 1 of 6).

  15. [Monitoring system on prison health: feasibility and recommendations].

    Science.gov (United States)

    Develay, Aude-Emmanuelle; Verdot, Charlotte; Grémy, Isabelle

    2015-01-01

    This article presents the results of two studies designed to define the feasibility and framework of the future prison health monitoring system in France. The objective of the first study was to obtain the points of view of professionals involved in prison health and the second study was designed to assess the feasibility of using prisoner's medical files for epidemiological purposes. The point of view of various professionals was collected by questionnaire sent to 43 randomly selected prison physicians and by 22 semi-directive interviews. The feasibility study was based on analysis of the medical files of 330 randomly selected prisoners in eleven prisons chosen in order to reflect the diversity of correctional settings and prison populations. Additional interviews were conducted with the medical staff of these prison facilities. There is a consensus on the need to monitor prison health, but there are contrasting views on data collection methods (surveys or routinely collected data]. The feasibility study also showed that the implementation of a prison health monitoring system based on routinely collected data from prisoner's medical records was not feasible at the present time in France. In the light of these findings, it is recommended to initially develop a monitoring system based on regular nationwide surveys, while pursuing computerization and standardization of health data in prison.

  16. A comprehensive health service evaluation and monitoring framework.

    Science.gov (United States)

    Reeve, Carole; Humphreys, John; Wakerman, John

    2015-12-01

    To develop a framework for evaluating and monitoring a primary health care service, integrating hospital and community services. A targeted literature review of primary health service evaluation frameworks was performed to inform the development of the framework specifically for remote communities. Key principles underlying primary health care evaluation were determined and sentinel indicators developed to operationalise the evaluation framework. This framework was then validated with key stakeholders. The framework includes Donabedian's three seminal domains of structure, process and outcomes to determine health service performance. These in turn are dependent on sustainability, quality of patient care and the determinants of health to provide a comprehensive health service evaluation framework. The principles underpinning primary health service evaluation were pertinent to health services in remote contexts. Sentinel indicators were developed to fit the demographic characteristics and health needs of the population. Consultation with key stakeholders confirmed that the evaluation framework was applicable. Data collected routinely by health services can be used to operationalise the proposed health service evaluation framework. Use of an evaluation framework which links policy and health service performance to health outcomes will assist health services to improve performance as part of a continuous quality improvement cycle. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

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

    Science.gov (United States)

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

    2018-01-01

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

  18. Computer Aided Diagnosis for mental health care: On the Clinical Validation of Sensitive Machines

    NARCIS (Netherlands)

    van der Sluis, Frans; Dijkstra, Ton; van den Broek, Egon; Conchon, E.; Correia, C.; Fred, A.; Gamboa, H.

    2012-01-01

    This study explores the feasibility of sensitive machines; that is, machines with empathic abilities, at least to some extent. A signal processing and machine learning pipeline is presented that is used to analyze data from two studies in which 25 Post-Traumatic Stress Disorder (PTSD) patients

  19. Computer aided diagnosis for mental health care : On the clinical validation of sensitive machines

    NARCIS (Netherlands)

    Sluis, F. van der; Dijkstra, T.; Broek, E.L. van den

    2012-01-01

    This study explores the feasibility of sensitive machines; that is, machines with empathic abilities, at least to some extent. A signal processing and machine learning pipeline is presented that is used to analyze data from two studies in which 25 Post-Traumatic Stress Disorder (PTSD) patients

  20. Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning

    OpenAIRE

    Roysden, Nathaniel; Wright, Adam

    2015-01-01

    Mental health problems are an independent predictor of increased healthcare utilization. We created random forest classifiers for predicting two outcomes following a patient’s first behavioral health encounter: decreased utilization by any amount (AUROC 0.74) and ultra-high absolute utilization (AUROC 0.88). These models may be used for clinical decision support by referring providers, to automatically detect patients who may benefit from referral, for cost management, or for risk/protection ...

  1. Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning.

    Science.gov (United States)

    Roysden, Nathaniel; Wright, Adam

    2015-01-01

    Mental health problems are an independent predictor of increased healthcare utilization. We created random forest classifiers for predicting two outcomes following a patient's first behavioral health encounter: decreased utilization by any amount (AUROC 0.74) and ultra-high absolute utilization (AUROC 0.88). These models may be used for clinical decision support by referring providers, to automatically detect patients who may benefit from referral, for cost management, or for risk/protection factor analysis.

  2. Bridge health monitoring with consideration of environmental effects

    International Nuclear Information System (INIS)

    Kim, Yuhee; Kim, Hyunsoo; Shin, Soobong; Park, Jongchil

    2012-01-01

    Reliable response measurements are extremely important for proper bridge health monitoring but incomplete and unreliable data may be acquired due to sensor problems and environmental effects. In the case of a sensor malfunction, parts of the measured data can be missing so that the structural health condition cannot be monitored reliably. This means that the dynamic characteristics of natural frequencies can change as if the structure is damaged due to environmental effects, such as temperature variations. To overcome these problems, this paper proposes a systematic procedure of data analysis to recover missing data and eliminate the environmental effects from the measured data. It also proposed a health index calculated statistically using revised data to evaluate the health condition of a bridge. The proposed method was examined using numerically simulated data with a truss structure and then applied to a set of field data measured from a cable stayed bridge

  3. Bridge health monitoring with consideration of environmental effects

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Yuhee; Kim, Hyunsoo; Shin, Soobong [Inha Univ., Incheon (Korea, Republic of); Park, Jongchil [Korea Expressway Co., (Korea, Republic of)

    2012-12-15

    Reliable response measurements are extremely important for proper bridge health monitoring but incomplete and unreliable data may be acquired due to sensor problems and environmental effects. In the case of a sensor malfunction, parts of the measured data can be missing so that the structural health condition cannot be monitored reliably. This means that the dynamic characteristics of natural frequencies can change as if the structure is damaged due to environmental effects, such as temperature variations. To overcome these problems, this paper proposes a systematic procedure of data analysis to recover missing data and eliminate the environmental effects from the measured data. It also proposed a health index calculated statistically using revised data to evaluate the health condition of a bridge. The proposed method was examined using numerically simulated data with a truss structure and then applied to a set of field data measured from a cable stayed bridge.

  4. Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

    Directory of Open Access Journals (Sweden)

    Hadi Banaee

    2013-12-01

    Full Text Available The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

  5. Structural health monitoring of bridge cables : An overview

    OpenAIRE

    DRISSI HABTI, Monssef; BETTI, Raimondo; YANEV, Bojidar

    2009-01-01

    Bridges are critical components of the civil infrastructure and are normally designed for a long life span. The life span of suspension bridges depends on the health of their cables, which, in turn, is a function of many factors. Therefore, continuous health monitoring (SHM) and regular condition assessment of cables is highly desirable. In this article, some SHM procedures based on direct, indirect non-destructive techniques NDT, and vibration theory are presented.

  6. Health monitoring of civil structures using fiber optic sensors

    International Nuclear Information System (INIS)

    Varma, Veto; Kumar, Praveen; Charan, J.J.; Reddy, G.R.; Vaze, K.K.; Kushwaha, H.S.

    2003-08-01

    During the lifetime of the reactor, the civil structure is subjected to many operational and environmental loads. Hence it is increasingly important to monitor the conditions of the structure and insure its safety and integrity. The conventional gauges have proved to be not sufficiently catering the problem of long term health monitoring of the structure because of its many limitations. Hence it is mandatory to develop a technique for the above purpose. Present study deals with the application of Fiber optic sensors (EFPI strain Gauges) in the civil structure for its health monitoring. Various experiments were undertaken and suitability of sensors was checked. A technique to embed the optical sensor inside the concrete is successfully developed and tested. (author)

  7. Multi-metric model-based structural health monitoring

    Science.gov (United States)

    Jo, Hongki; Spencer, B. F.

    2014-04-01

    ABSTRACT The inspection and maintenance of bridges of all types is critical to the public safety and often critical to the economy of a region. Recent advanced sensor technologies provide accurate and easy-to-deploy means for structural health monitoring and, if the critical locations are known a priori, can be monitored by direct measurements. However, for today's complex civil infrastructure, the critical locations are numerous and often difficult to identify. This paper presents an innovative framework for structural monitoring at arbitrary locations on the structure combining computational models and limited physical sensor information. The use of multi-metric measurements is advocated to improve the accuracy of the approach. A numerical example is provided to illustrate the proposed hybrid monitoring framework, particularly focusing on fatigue life assessment of steel structures.

  8. Model-based health monitoring of hybrid systems

    CERN Document Server

    Wang, Danwei; Low, Chang Boon; Arogeti, Shai

    2013-01-01

    Offers in-depth comprehensive study on health monitoring for hybrid systems Includes new concepts, such as GARR, mode tracking and multiple failure prognosis Contains many examples, making the developed techniques easily understandable and accessible Introduces state-of-the-art algorithms and methodologies from experienced researchers

  9. Monitoring health related quality of life in adolescents with diabetes

    DEFF Research Database (Denmark)

    de Wit, M; Delemarre-van de Waal, Henriette A; Pouwer, F

    2007-01-01

    Particularly in chronic conditions, monitoring health related quality of life (HRQoL) of adolescents in clinical practice is increasingly advocated. We set out to identify and review the clinical utility of available generic and diabetes specific HRQoL questionnaires suitable for use in adolescents...

  10. Forest health monitoring in New England: 1990 annual report

    Science.gov (United States)

    Robert T. Brooks; David R. Dickson; William B. Burkman; Imants Millers; Margaret Miller-Weeks; Ellen Cooter; Luther Smith; Luther Smith

    1992-01-01

    The USDA Forest Service, in cooperation with the U.S. Environmental Protection Agency and the New England State Forestry Agencies initiated field sampling for the Forest Health Monitoring program in 1990. Two hundred and sixty-three permanent sample plots were established. Measurements were taken to characterize the physical conditions of the plots. This publication...

  11. Time-frequency Methods for Structural Health Monitoring

    NARCIS (Netherlands)

    Pyayt, A.L.; Kozionov, A.P.; Mokhov, I.I.; Lang, B.; Meijer, R.J.; Krzhizhanovskaya, V.V.; Sloot, P.M.A.

    2014-01-01

    Detection of early warning signals for the imminent failure of large and complex engineered structures is a daunting challenge with many open research questions. In this paper we report on novel ways to perform Structural Health Monitoring (SHM) of flood protection systems (levees, earthen dikes and

  12. Time-frequency methods for structural health monitoring

    NARCIS (Netherlands)

    Pyayt, A.L.; Kozionov, A.P.; Mokhov, I.I.; Lang, B.; Meijer, R.J.; Krzhizhanovskaya, V.V.; Sloot, P.M.A.

    2014-01-01

    Detection of early warning signals for the imminent failure of large and complex engineered structures is a daunting challenge with many open research questions. In this paper we report on novel ways to perform Structural Health Monitoring (SHM) of flood protection systems (levees, earthen dikes and

  13. Mobile patient monitoring: The MobiHealth system

    NARCIS (Netherlands)

    Wac, K.E.; Bults, Richard G.A.; van Beijnum, Bernhard J.F.; Widya, I.A.; Jones, Valerie M.; Konstantas, D.; Vollenbroek-Hutten, Miriam Marie Rosé; Hermens, Hermanus J.

    2009-01-01

    The emergence of high bandwidth public wireless networks and miniaturized personal mobile devices give rise to new mobile healthcare services. To this end, the MobiHealth system provides highly customizable vital signs tele-monitoring and tele-treatment system based on a body area network (BAN) and

  14. Challenges of monitoring reproductive health services: a case study ...

    African Journals Online (AJOL)

    Challenges of monitoring reproductive health services: a case study of antenatal clinics in Kinondoni municipality, Dar Es Salaam. ... was descriptive cross sectional employing both qualitative and quantitative methods. The sample population included nurse-midwives who manage ANC clinics in Kinondoni Municipality.

  15. Assessing the utilisation of a child health monitoring tool

    African Journals Online (AJOL)

    2017-12-06

    Dec 6, 2017 ... preventive or promotive tool for monitoring child health as neither ... attitudes and practices of both CGs and HCWs relating to these components; and (iii) identify HCWs' perceptions of the barriers .... In posession of old RtHC (n=54) .... number of CGs (16.4%; 409/1 646) knew that a young child should.

  16. Monitoring public health following a major firework factory explosion.

    NARCIS (Netherlands)

    Dirkzwager, A.J.E.; IJzermans, C.J.; Kerssens, J.J.

    2003-01-01

    Background: In May 2000, a firework factory exploded in a residential area in the Netherlands, resulting in 22 deaths, 947 wounded people, and about 2.000 severely damaged houses. Following the explosion, a largescale monitoring study was implemented to examine disaster-related health consequences

  17. Monitoring health status following a major firework factory explosion.

    NARCIS (Netherlands)

    Dirkzwager, A.; IJzermans, J.

    2003-01-01

    In May 2000, a firework factory exploded in a residential area in the Netherlands, resulting in 22 death, 947 wounded people, 500 destroyed houses, and 1.500 severely damaged houses. Following the explosion, a large-scale monitoring study was implemented to investigate disaster-related health

  18. Physical health monitoring in mental health settings: a study exploring mental health nurses' views of their role.

    Science.gov (United States)

    Mwebe, Herbert

    2017-10-01

    To explore nurses' views of their role in the screening and monitoring of the physical care needs of people with serious mental illness in a mental health service provider. There is increasing awareness through research that people with serious mental illness disproportionately experience and die early from physical health conditions. Mental health nurses are best placed as front-line workers to offer screening, monitoring and interventions; however, their views on physical care interventions are not studied often. Qualitative exploratory study. The study was carried out in a mental health inpatient centre in England. Volunteer sampling was adopted for the study with a total target sample of (n = 20) nurses from three inpatient wards. Semistructured interviews were conducted with (n = 10) registered mental health nurses who had consented to take part in the study. Inductive data analysis and theme development were guided by a thematic analytic framework. Participants shared a clear commitment regarding their role regarding physical health screening and monitoring in mental health settings. Four themes emerged as follows: features of current practice and physical health monitoring; perceived barriers to physical health monitoring; education and training needs; and strategies to improve physical health monitoring. Nurses were unequivocal in their resolve to ensure good standard physical health monitoring and screening interventions in practice. However, identified obstacles have to be addressed to ensure that physical health screening and monitoring is integrated adequately in everyday clinical activities. Achieving this would require improvements in nurses' training, and an integrated multiservice and team-working approach. Attending to the physical health needs of people with serious mental illness has been associated with multiple improvements in both mental and physical health; nurses have a vital role to play in identifying and addressing causes of poor

  19. Regulatory measures for occupational health monitoring in BARC facilities

    International Nuclear Information System (INIS)

    Rajdeep; Chattopadhyay, S.

    2017-01-01

    Bhabha Atomic Research Centre (BARC) is the premier organization actively engaged in the research and developmental activities related to nuclear science and technology for the benefit of society and the nation. BARC has various facilities like nuclear fuel fabrication facilities, research reactors, spent fuel storage facilities, nuclear fuel re-cycling facilities, radioactive waste management facilities, machining workshops and various Physics, Chemistry and Biological laboratories. In BARC, aspects related to Occupational Safety and Health (OSH) are given paramount importance. The issues related OSH are subjected to multi-tier review process. BARC Safety Council (BSC) is the apex committee in the three-tier safety and security review framework of BARC. BSC functions as regulatory body for BARC facilities. BSC is responsible for occupational safety and health of employees in BARC facilities

  20. A Battery Health Monitoring Framework for Planetary Rovers

    Science.gov (United States)

    Daigle, Matthew J.; Kulkarni, Chetan Shrikant

    2014-01-01

    Batteries have seen an increased use in electric ground and air vehicles for commercial, military, and space applications as the primary energy source. An important aspect of using batteries in such contexts is battery health monitoring. Batteries must be carefully monitored such that the battery health can be determined, and end of discharge and end of usable life events may be accurately predicted. For planetary rovers, battery health estimation and prediction is critical to mission planning and decision-making. We develop a model-based approach utilizing computaitonally efficient and accurate electrochemistry models of batteries. An unscented Kalman filter yields state estimates, which are then used to predict the future behavior of the batteries and, specifically, end of discharge. The prediction algorithm accounts for possible future power demands on the rover batteries in order to provide meaningful results and an accurate representation of prediction uncertainty. The framework is demonstrated on a set of lithium-ion batteries powering a rover at NASA.

  1. Health disparities monitoring in the U.S.: lessons for monitoring efforts in Israel and other countries.

    Science.gov (United States)

    Abu-Saad, Kathleen; Avni, Shlomit; Kalter-Leibovici, Ofra

    2018-02-28

    Health disparities are a persistent problem in many high-income countries. Health policymakers recognize the need to develop systematic methods for documenting and tracking these disparities in order to reduce them. The experience of the U.S., which has a well-established health disparities monitoring infrastructure, provides useful insights for other countries. This article provides an in-depth review of health disparities monitoring in the U.S. Lessons of potential relevance for other countries include: 1) the integration of health disparities monitoring in population health surveillance, 2) the role of political commitment, 3) use of monitoring as a feedback loop to inform future directions, 4) use of monitoring to identify data gaps, 5) development of extensive cross-departmental cooperation, and 6) exploitation of digital tools for monitoring and reporting. Using Israel as a case in point, we provide a brief overview of the healthcare and health disparities landscape in Israel, and examine how the lessons from the U.S. experience might be applied in the Israeli context. The U.S. model of health disparities monitoring provides useful lessons for other countries with respect to documentation of health disparities and tracking of progress made towards their elimination. Given the persistence of health disparities both in the U.S. and Israel, there is a need for monitoring systems to expand beyond individual- and healthcare system-level factors, to incorporate social and environmental determinants of health as health indicators/outcomes.

  2. System Health Monitoring Using a Novel Method: Security Unified Process

    Directory of Open Access Journals (Sweden)

    Alireza Shameli-Sendi

    2012-01-01

    and change management, and project management. The dynamic dimension, or phases, contains inception, analysis and design, construction, and monitoring. Risk assessment is a major part of the ISMS process. In SUP, we present a risk assessment model, which uses a fuzzy expert system to assess risks in organization. Since, the classification of assets is an important aspect of risk management and ensures that effective protection occurs, a Security Cube is proposed to identify organization assets as an asset classification model. The proposed model leads us to have an offline system health monitoring tool that is really a critical need in any organization.

  3. Wireless connectivity for health and sports monitoring: a review.

    Science.gov (United States)

    Armstrong, S

    2007-05-01

    This is a review of health and sports monitoring research that uses or could benefit from wireless connectivity. New, enabling wireless connectivity standards are evaluated for their suitability, and an assessment of current exploitation of these technologies is summarised. An example of the application is given, highlighting the capabilities of a network of wireless sensors. Issues of timing and power consumption in a battery-powered system are addressed to highlight the benefits networking can provide, and a suggestion of how monitoring different biometric signals might allow one to gain additional information about an athlete or patient is made.

  4. Mobile health-monitoring system through visible light communication.

    Science.gov (United States)

    Tan, Yee-Yong; Chung, Wan-Young

    2014-01-01

    Promising development in the light emitting diode (LED) technology has spurred the interest to adapt LED for both illumination and data transmission. This has fostered the growth of interest in visible light communication (VLC), with on-going research to utilize VLC in various applications. This paper presents a mobile-health monitoring system, where healthcare information such as biomedical signals and patient information are transmitted via the LED lighting. A small and portable receiver module is designed and developed to be attached to the mobile device, providing a seamless monitoring environment. Three different healthcare information including ECG, PPG signals and HL7 text information is transmitted simultaneously, using a single channel VLC. This allows for a more precise and accurate monitoring and diagnosis. The data packet size is carefully designed, to transmit information in a minimal packet error rate. A comprehensive monitoring application is designed and developed through the use of a tablet computer in our study. Monitoring and evaluation such as heart rate and arterial blood pressure measurement can be performed concurrently. Real-time monitoring is demonstrated through experiment, where non-hazardous transmission method can be implemented alongside a portable device for better and safer healthcare service.

  5. Metabolic monitoring in New Zealand district health board mental health services.

    Science.gov (United States)

    Staveley, Aimee; Soosay, Ian; O'Brien, Anthony J

    2017-11-10

    To audit New Zealand district health boards' (DHBs) metabolic monitoring policies in relation to consumers prescribed second-generation antipsychotic medications using a best practice guideline. Metabolic monitoring policies from DHBs and one private clinic were analysed in relation to a best practice standard developed from the current literature and published guidelines relevant to metabolic syndrome. Fourteen of New Zealand's 20 DHBs currently have metabolic monitoring policies for consumers prescribed antipsychotic medication. Two of those policies are consistent with the literature-based guideline. Eight policies include actions to be taken when consumers meet criteria for metabolic syndrome. Four DHBs have systems for measuring their rates of metabolic monitoring. There is no consensus on who is clinically responsible for metabolic monitoring. Metabolic monitoring by mental health services in New Zealand reflects international experience that current levels of monitoring are low and policies are not always in place. Collaboration across the mental health and primary care sectors together with the adoption of a consensus guideline is needed to improve rates of monitoring and reduce current rates of physical health morbidities.

  6. Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

    Directory of Open Access Journals (Sweden)

    Yiqiu Lv

    2013-01-01

    Full Text Available Different approaches for gas path performance estimation of dynamic systems are commonly used, the most common being the variants of the Kalman filter. The extended Kalman filter (EKF method is a popular approach for nonlinear systems which combines the traditional Kalman filtering and linearization techniques to effectively deal with weakly nonlinear and non-Gaussian problems. Its mathematical formulation is based on the assumption that the probability density function (PDF of the state vector can be approximated to be Gaussian. Recent investigations have focused on the particle filter (PF based on Monte Carlo sampling algorithms for tackling strong nonlinear and non-Gaussian models. Considering the aircraft engine is a complicated machine, operating under a harsh environment, and polluted by complex noises, the PF might be an available way to monitor gas path health for aircraft engines. Up to this point in time a number of Kalman filtering approaches have been used for aircraft turbofan engine gas path health estimation, but the particle filters have not been used for this purpose and a systematic comparison has not been published. This paper presents gas path health monitoring based on the PF and the constrained extend Kalman particle filter (cEKPF, and then compares the estimation accuracy and computational effort of these filters to the EKF for aircraft engine performance estimation under rapid faults and general deterioration. Finally, the effects of the constraint mechanism and particle number on the cEKPF are discussed. We show in this paper that the cEKPF outperforms the EKF, PF and EKPF, and conclude that the cEKPF is the best choice for turbofan engine health monitoring.

  7. Development of structural health monitoring techniques using dynamics testing

    Energy Technology Data Exchange (ETDEWEB)

    James, G.H. III [Sandia National Labs., Albuquerque, NM (United States). Experimental Structural Dynamics Dept.

    1996-03-01

    Today`s society depends upon many structures (such as aircraft, bridges, wind turbines, offshore platforms, buildings, and nuclear weapons) which are nearing the end of their design lifetime. Since these structures cannot be economically replaced, techniques for structural health monitoring must be developed and implemented. Modal and structural dynamics measurements hold promise for the global non-destructive inspection of a variety of structures since surface measurements of a vibrating structure can provide information about the health of the internal members without costly (or impossible) dismantling of the structure. In order to develop structural health monitoring for application to operational structures, developments in four areas have been undertaken within this project: operational evaluation, diagnostic measurements, information condensation, and damage identification. The developments in each of these four aspects of structural health monitoring have been exercised on a broad range of experimental data. This experimental data has been extracted from structures from several application areas which include aging aircraft, wind energy, aging bridges, offshore structures, structural supports, and mechanical parts. As a result of these advances, Sandia National Laboratories is in a position to perform further advanced development, operational implementation, and technical consulting for a broad class of the nation`s aging infrastructure problems.

  8. Development of a Data Acquisition Program for the Purpose of Monitoring Processing Statistics Throughout the BaBar Online Computing Infrastructure's Farm Machines

    Energy Technology Data Exchange (ETDEWEB)

    Stonaha, P.

    2004-09-03

    A current shortcoming of the BaBar monitoring system is the lack of systematic gathering, archiving, and access to the running statistics of the BaBar Online Computing Infrastructure's farm machines. Using C, a program has been written to gather the raw data of each machine's running statistics and compute various rates and percentages that can be used for system monitoring. These rates and percentages then can be stored in an EPICS database for graphing, archiving, and future access. Graphical outputs show the reception of the data into the EPICS database. The C program can read if the data are 32- or 64-bit and correct for overflows. This program is not exclusive to BaBar and can be easily modified for any system.

  9. Patient Health Monitoring Using Wireless Body Area Network

    Directory of Open Access Journals (Sweden)

    Hsu Myat Thwe

    2015-06-01

    Full Text Available Abstract Nowadays remote patient health monitoring using wireless technology plays very vigorous role in a society. Wireless technology helps monitoring of physiological parameters like body temperature heart rate respiration blood pressure and ECG. The main aim of this paper is to propose a wireless sensor network system in which both heart rate and body temperature ofmultiplepatients can monitor on PC at the same time via RF network. The proposed prototype system includes two sensor nodes and receiver node base station. The sensor nodes are able to transmit data to receiver using wireless nRF transceiver module.The nRF transceiver module is used to transfer the data from microcontroller to PC and a graphical user interface GUI is developed to display the measured data and save to database. This system can provide very cheaper easier and quick respondent history of patient.

  10. 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.

  11. 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.

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

    Science.gov (United States)

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

    2015-01-01

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

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

    International Nuclear Information System (INIS)

    Roe, S.; Mba, D.

    2009-01-01

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

  14. Monitoring of the state of the paper machine circulation water with a wide-band impedance measurement; Paperikoneen kiertoveden tilan seuranta laajakaistaisella impedanssimittauksella - MPKT 02

    Energy Technology Data Exchange (ETDEWEB)

    Varpula, T [VTT Automation, Espoo (Finland). Measurement Technology

    1999-12-31

    A new measurement method for monitoring the chemical state of the circulation water in the paper machine is proposed and studied. In the method, the electrical properties - conductivity and permittivity - of the water are measured in a wide frequency band: 20 Hz - 10 mhz. Large-molecule organic compounds in the water are expected cause characteristic changes in the dielectric properties of the water. Continuous monitoring of the permittivity in the wide frequency band thus reveals their presence. Various electronic measurement setups for the measurement are constructed and studied by using test samples. If the method turns out to be promising, a prototype device will be made. (orig.)

  15. 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.)

  16. Investigation of Wireless Sensor Networks for Structural Health Monitoring

    Directory of Open Access Journals (Sweden)

    Ping Wang

    2012-01-01

    Full Text Available Wireless sensor networks (WSNs are one of the most able technologies in the structural health monitoring (SHM field. Through intelligent, self-organising means, the contents of this paper will test a variety of different objects and different working principles of sensor nodes connected into a network and integrated with data processing functions. In this paper the key issues of WSN applied in SHM are discussed, including the integration of different types of sensors with different operational modalities, sampling frequencies, issues of transmission bandwidth, real-time ability, and wireless transmitter frequency. Furthermore, the topology, data fusion, integration, energy saving, and self-powering nature of different systems will be investigated. In the FP7 project “Health Monitoring of Offshore Wind Farms,” the above issues are explored.

  17. Space Station Environmental Health System water quality monitoring

    Science.gov (United States)

    Vincze, Johanna E.; Sauer, Richard L.

    1990-01-01

    One of the unique aspects of the Space Station is that it will be a totally encapsulated environment and the air and water supplies will be reclaimed for reuse. The Environmental Health System, a subsystem of CHeCS (Crew Health Care System), must monitor the air and water on board the Space Station Freedom to verify that the quality is adequate for crew safety. Specifically, the Water Quality Subsystem will analyze the potable and hygiene water supplies regularly for organic, inorganic, particulate, and microbial contamination. The equipment selected to perform these analyses will be commercially available instruments which will be converted for use on board the Space Station Freedom. Therefore, the commercial hardware will be analyzed to identify the gravity dependent functions and modified to eliminate them. The selection, analysis, and conversion of the off-the-shelf equipment for monitoring the Space Station reclaimed water creates a challenging project for the Water Quality engineers and scientists.

  18. Ultra low power signal oriented approach for wireless health monitoring.

    Science.gov (United States)

    Marinkovic, Stevan; Popovici, Emanuel

    2012-01-01

    In recent years there is growing pressure on the medical sector to reduce costs while maintaining or even improving the quality of care. A potential solution to this problem is real time and/or remote patient monitoring by using mobile devices. To achieve this, medical sensors with wireless communication, computational and energy harvesting capabilities are networked on, or in, the human body forming what is commonly called a Wireless Body Area Network (WBAN). We present the implementation of a novel Wake Up Receiver (WUR) in the context of standardised wireless protocols, in a signal-oriented WBAN environment and present a novel protocol intended for wireless health monitoring (WhMAC). WhMAC is a TDMA-based protocol with very low power consumption. It utilises WBAN-specific features and a novel ultra low power wake up receiver technology, to achieve flexible and at the same time very low power wireless data transfer of physiological signals. As the main application is in the medical domain, or personal health monitoring, the protocol caters for different types of medical sensors. We define four sensor modes, in which the sensors can transmit data, depending on the sensor type and emergency level. A full power dissipation model is provided for the protocol, with individual hardware and application parameters. Finally, an example application shows the reduction in the power consumption for different data monitoring scenarios.

  19. Self-learning health monitoring algorithm in composite structures

    Science.gov (United States)

    Grassia, Luigi; Iannone, Michele; Califano, America; D'Amore, Alberto

    2018-02-01

    The paper describes a system that it is able of monitoring the health state of a composite structure in real time. The hardware of the system consists of a wire of strain sensors connected to a control unit. The software of the system elaborates the strain data and in real time is able to detect the presence of an eventual damage of the structures monitored with the strain sensors. The algorithm requires as input only the strains of the monitored structured measured on real time, i.e. those strains coming from the deformations of the composite structure due to the working loads. The health monitoring system does not require any additional device to interrogate the structure as often used in the literature, instead it is based on a self-learning procedure. The strain data acquired when the structure is healthy are used to set up the correlations between the strain in different positions of structure by means of neural network. Once the correlations between the strains in different position have been set up, these correlations act as a fingerprint of the healthy structure. In case of damage the correlation between the strains in the position of the structure near the damage will change due to the change of the stiffness of the structure caused by the damage. The developed software is able to recognize the change of the transfer function between the strains and consequently is able to detect the damage.

  20. Monitoring Indoor Air Quality for Enhanced Occupational Health.

    Science.gov (United States)

    Pitarma, Rui; Marques, Gonçalo; Ferreira, Bárbara Roque

    2017-02-01

    Indoor environments are characterized by several pollutant sources. Because people spend more than 90% of their time in indoor environments, several studies have pointed out the impact of indoor air quality on the etiopathogenesis of a wide number of non-specific symptoms which characterizes the "Sick Building Syndrome", involving the skin, the upper and lower respiratory tract, the eyes and the nervous system, as well as many building related diseases. Thus, indoor air quality (IAQ) is recognized as an important factor to be controlled for the occupants' health and comfort. The majority of the monitoring systems presently available is very expensive and only allow to collect random samples. This work describes the system (iAQ), a low-cost indoor air quality monitoring wireless sensor network system, developed using Arduino, XBee modules and micro sensors, for storage and availability of monitoring data on a web portal in real time. Five micro sensors of environmental parameters (air temperature, humidity, carbon monoxide, carbon dioxide and luminosity) were used. Other sensors can be added for monitoring specific pollutants. The results reveal that the system can provide an effective indoor air quality assessment to prevent exposure risk. In fact, the indoor air quality may be extremely different compared to what is expected for a quality living environment. Systems like this would have benefit as public health interventions to reduce the burden of symptoms and diseases related to "sick buildings".

  1. Signature Optical Cues: Emerging Technologies for Monitoring Plant Health

    Directory of Open Access Journals (Sweden)

    Anand K. Asundi

    2008-05-01

    Full Text Available Optical technologies can be developed as practical tools for monitoring plant health by providing unique spectral signatures that can be related to specific plant stresses. Signatures from thermal and fluorescence imaging have been used successfully to track pathogen invasion before visual symptoms are observed. Another approach for noninvasive plant health monitoring involves elucidating the manner with which light interacts with the plant leaf and being able to identify changes in spectral characteristics in response to specific stresses. To achieve this, an important step is to understand the biochemical and anatomical features governing leaf reflectance, transmission and absorption. Many studies have opened up possibilities that subtle changes in leaf reflectance spectra can be analyzed in a plethora of ways for discriminating nutrient and water stress, but with limited success. There has also been interest in developing transgenic phytosensors to elucidate plant status in relation to environmental conditions. This approach involves unambiguous signal creation whereby genetic modification to generate reporter plants has resulted in distinct optical signals emitted in response to specific stressors. Most of these studies are limited to laboratory or controlled greenhouse environments at leaf level. The practical translation of spectral cues for application under field conditions at canopy and regional levels by remote aerial sensing remains a challenge. The movement towards technology development is well exemplified by the Controlled Ecological Life Support System under development by NASA which brings together technologies for monitoring plant status concomitantly with instrumentation for environmental monitoring and feedback control.

  2. [Use of routine data from statutory health insurances for federal health monitoring purposes].

    Science.gov (United States)

    Ohlmeier, C; Frick, J; Prütz, F; Lampert, T; Ziese, T; Mikolajczyk, R; Garbe, E

    2014-04-01

    Federal health monitoring deals with the state of health and the health-related behavior of populations and is used to inform politics. To date, the routine data from statutory health insurances (SHI) have rarely been used for federal health monitoring purposes. SHI routine data enable analyses of disease frequency, risk factors, the course of the disease, the utilization of medical services, and mortality rates. The advantages offered by SHI routine data regarding federal health monitoring are the intersectoral perspective and the nearly complete absence of recall and selection bias in the respective population. Further, the large sample sizes and the continuous collection of the data allow reliable descriptions of the state of health of the insurants, even in cases of multiple stratification. These advantages have to be weighed against disadvantages linked to the claims nature of the data and the high administrative hurdles when requesting the use of SHI routine data. Particularly in view of the improved availability of data from all SHI insurants for research institutions in the context of the "health-care structure law", SHI routine data are an interesting data source for federal health monitoring purposes.

  3. Damage Detection with Streamlined Structural Health Monitoring Data

    OpenAIRE

    Li, Jian; Deng, Jun; Xie, Weizhi

    2015-01-01

    The huge amounts of sensor data generated by large scale sensor networks in on-line structural health monitoring (SHM) systems often overwhelms the systems’ capacity for data transmission and analysis. This paper presents a new concept for an integrated SHM system in which a streamlined data flow is used as a unifying thread to integrate the individual components of on-line SHM systems. Such an integrated SHM system has a few desirable functionalities including embedded sensor data compressio...

  4. A Golden Ticket to Future Occupational and Environmental Health Monitoring

    Science.gov (United States)

    2015-10-01

    a health risk assessment (HRA) of the exposure by considering multiple factors including: threat source, route of exposure ( inhalation , ingestion...contaminants for chemical and particulate inhalational exposures. Measurements of physical exposures are also monitored to include noise, temperature, and...hazards are. Some hazards are always present in very common Air Force 12 processes (i.e. jet fuel in a refueling process), while other hazards are

  5. Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring

    Science.gov (United States)

    2016-02-02

    Virginia 22203 Air Force Research Laboratory Air Force Materiel Command 1 Final Performance Report: AFOSR T.C. Henderson , V.J. Mathews, and D...AFRL-AFOSR-VA-TR-2016-0094 Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring. Thomas Henderson UNIVERSITY OF UTAH SALT...The people who worked on this project include: Thomas C. Henderson , John Mathews, Jingru Zhou, Daimei Zhij, Ahmad Zoubi, Sabita Nahata, Dan Adams

  6. Health Monitoring for Coated Steel Belts in an Elevator System

    Directory of Open Access Journals (Sweden)

    Huaming Lei

    2012-01-01

    Full Text Available This paper presents a method of health monitoring for coated steel belts in an elevator system by measuring the electrical resistance of the ropes embedded in the belt. A model on resistance change caused by fretting wear and stress fatigue has been established. Temperature and reciprocating cycles are also taken into consideration when determining the potential strength degradation of the belts. It is proved by experiments that the method could effectively estimate the health degradation of the most dangerous section as well as other ones along the whole belts.

  7. Structural Health Monitoring for a Z-Type Special Vehicle

    Directory of Open Access Journals (Sweden)

    Chaolin Yuan

    2017-06-01

    Full Text Available Nowadays there exist various kinds of special vehicles designed for some purposes, which are different from regular vehicles in overall dimension and design. In that case, accidents such as overturning will lead to large economical loss and casualties. There are still no technical specifications to follow to ensure the safe operation and driving of these special vehicles. Owing to the poor efficiency of regular maintenance, it is more feasible and effective to apply real-time monitoring during the operation and driving process. In this paper, the fiber Bragg grating (FBG sensors are used to monitor the safety of a z-type special vehicle. Based on the structural features and force distribution, a reasonable structural health monitoring (SHM scheme is presented. Comparing the monitoring results with the finite element simulation results guarantees the accuracy and reliability of the monitoring results. Large amounts of data are collected during the operation and driving progress to evaluate the structural safety condition and provide reference for SHM systems developed for other special vehicles.

  8. Design and Analysis of Architectures for Structural Health Monitoring Systems

    Science.gov (United States)

    Mukkamala, Ravi; Sixto, S. L. (Technical Monitor)

    2002-01-01

    During the two-year project period, we have worked on several aspects of Health Usage and Monitoring Systems for structural health monitoring. In particular, we have made contributions in the following areas. 1. Reference HUMS architecture: We developed a high-level architecture for health monitoring and usage systems (HUMS). The proposed reference architecture is shown. It is compatible with the Generic Open Architecture (GOA) proposed as a standard for avionics systems. 2. HUMS kernel: One of the critical layers of HUMS reference architecture is the HUMS kernel. We developed a detailed design of a kernel to implement the high level architecture.3. Prototype implementation of HUMS kernel: We have implemented a preliminary version of the HUMS kernel on a Unix platform.We have implemented both a centralized system version and a distributed version. 4. SCRAMNet and HUMS: SCRAMNet (Shared Common Random Access Memory Network) is a system that is found to be suitable to implement HUMS. For this reason, we have conducted a simulation study to determine its stability in handling the input data rates in HUMS. 5. Architectural specification.

  9. Smart sensor systems for human health breath monitoring applications.

    Science.gov (United States)

    Hunter, G W; Xu, J C; Biaggi-Labiosa, A M; Laskowski, D; Dutta, P K; Mondal, S P; Ward, B J; Makel, D B; Liu, C C; Chang, C W; Dweik, R A

    2011-09-01

    Breath analysis techniques offer a potential revolution in health care diagnostics, especially if these techniques can be brought into standard use in the clinic and at home. The advent of microsensors combined with smart sensor system technology enables a new generation of sensor systems with significantly enhanced capabilities and minimal size, weight and power consumption. This paper discusses the microsensor/smart sensor system approach and provides a summary of efforts to migrate this technology into human health breath monitoring applications. First, the basic capability of this approach to measure exhaled breath associated with exercise physiology is demonstrated. Building from this foundation, the development of a system for a portable asthma home health care system is described. A solid-state nitric oxide (NO) sensor for asthma monitoring has been identified, and efforts are underway to miniaturize this NO sensor technology and integrate it into a smart sensor system. It is concluded that base platform microsensor technology combined with smart sensor systems can address the needs of a range of breath monitoring applications and enable new capabilities for healthcare.

  10. Remote health monitoring: predicting outcome success based on contextual features for cardiovascular disease.

    Science.gov (United States)

    Alshurafa, Nabil; Eastwood, Jo-Ann; Pourhomayoun, Mohammad; Liu, Jason J; Sarrafzadeh, Majid

    2014-01-01

    Current studies have produced a plethora of remote health monitoring (RHM) systems designed to enhance the care of patients with chronic diseases. Many RHM systems are designed to improve patient risk factors for cardiovascular disease, including physiological parameters such as body mass index (BMI) and waist circumference, and lipid profiles such as low density lipoprotein (LDL) and high density lipoprotein (HDL). There are several patient characteristics that could be determining factors for a patient's RHM outcome success, but these characteristics have been largely unidentified. In this paper, we analyze results from an RHM system deployed in a six month Women's Heart Health study of 90 patients, and apply advanced feature selection and machine learning algorithms to identify patients' key baseline contextual features and build effective prediction models that help determine RHM outcome success. We introduce Wanda-CVD, a smartphone-based RHM system designed to help participants with cardiovascular disease risk factors by motivating participants through wireless coaching using feedback and prompts as social support. We analyze key contextual features that secure positive patient outcomes in both physiological parameters and lipid profiles. Results from the Women's Heart Health study show that health threat of heart disease, quality of life, family history, stress factors, social support, and anxiety at baseline all help predict patient RHM outcome success.

  11. Environment monitoring and residents health condition monitoring of nuclear power plant Bohunice region

    International Nuclear Information System (INIS)

    Letkovicova, M.; Rehak, R.; Stehlikova, B.; Celko, M.; Hraska, S.; Klocok, L.; Kostial, J.; Prikazsky, V.; Vidovic, J.; Zirko, M.; Beno, T.; Mitosinka, J.

    1998-01-01

    The report contents final environment evaluation and selected characteristic of residents health physics of nuclear power plant Bohunice region. Evaluated data were elaborated during analytical period 1993-1997.Task solving which results are documented in this final report was going on between 1996- 1998. The report deals in individual stages with the following: Information obtaining and completing which characterize demographic situation of the area for the 1993-1997 period; Datum obtaining and completing which contain selected health physics characteristics of the area residents; Database structures for individual data archiving from monitoring and collection; Brief description of geographic information system for graphic presentation of evaluation results based on topographic base; Digital mapping structure description; Results and evaluation of radionuclide monitoring in environment performed by Environmental radiation measurements laboratory by the nuclear power plant Bohunice for the 1993-1997 period. Demographic situation evaluation and selected health physics characteristics of the area of nuclear power plant residents for the 1993-1997 period are summarized in the final part of the document. Monitoring results and their evaluation is processed in graph, table, text description and map output forms. Map outputs are processed in the geographic information system Arc View GIS 3.0a environment

  12. Integration of structural health monitoring solutions onto commercial aircraft via the Federal Aviation Administration structural health monitoring research program

    Science.gov (United States)

    Swindell, Paul; Doyle, Jon; Roach, Dennis

    2017-02-01

    The Federal Aviation Administration (FAA) started a research program in structural health monitoring (SHM) in 2011. The program's goal was to understand the technical gaps of implementing SHM on commercial aircraft and the potential effects on FAA regulations and guidance. The program evolved into a demonstration program consisting of a team from Sandia National Labs Airworthiness Assurance NDI Center (AANC), the Boeing Corporation, Delta Air Lines, Structural Monitoring Systems (SMS), Anodyne Electronics Manufacturing Corp (AEM) and the FAA. This paper will discuss the program from the selection of the inspection problem, the SHM system (Comparative Vacuum Monitoring-CVM) that was selected as the inspection solution and the testing completed to provide sufficient data to gain the first approved use of an SHM system for routine maintenance on commercial US aircraft.

  13. Engine health monitoring systems: Tools for improved maintenance management in the 1980's

    Science.gov (United States)

    Kimball, J. C.

    1981-01-01

    The performance monitoring aspect of maintenance, characteristic of the engine health monitoring system are discussed. An overview of the system activities is presented and a summary of programs for improved monitoring in the 1980's are discussed.

  14. Wireless Zigbee strain gage sensor system for structural health monitoring

    Science.gov (United States)

    Ide, Hiroshi; Abdi, Frank; Miraj, Rashid; Dang, Chau; Takahashi, Tatsuya; Sauer, Bruce

    2009-05-01

    A compact cell phone size radio frequency (ZigBee) wireless strain measurement sensor system to measure the structural strain deformation was developed. The developed system provides an accurate strain measurement data stream to the Internet for further Diagnostic and Prognostic (DPS) correlation. Existing methods of structural measurement by strain sensors (gauges) do not completely satisfy problems posed by continuous structural health monitoring. The need for efficient health monitoring methods with real-time requirements to bidirectional data flow from sensors and to a commanding device is becoming critical for keeping our daily life safety. The use of full-field strain measurement techniques could reduce costly experimental programs through better understanding of material behavior. Wireless sensor-network technology is a monitoring method that is estimated to grow rapidly providing potential for cost savings over traditional wired sensors. The many of currently available wireless monitoring methods have: the proactive and constant data rate character of the data streams rather than traditional reactive, event-driven data delivery; mostly static node placement on structures with limited number of nodes. Alpha STAR Electronics' wireless sensor network system, ASWN, addresses some of these deficiencies, making the system easier to operate. The ASWN strain measurement system utilizes off-the-shelf sensors, namely strain gauges, with an analog-to-digital converter/amplifier and ZigBee radio chips to keep cost lower. Strain data is captured by the sensor, converted to digital form and delivered to the ZigBee radio chip, which in turn broadcasts the information using wireless protocols to a Personal Data Assistant (PDA) or Laptop/Desktop computers. From here, data is forwarded to remote computers for higher-level analysis and feedback using traditional cellular and satellite communication or the Ethernet infrastructure. This system offers a compact size, lower cost

  15. Continuous health monitoring of Graphite Epoxy Motorcases (GEM)

    Science.gov (United States)

    Finlayson, Richard D.; Schaafsma, David T.; Shen, H. Warren; Carlos, Mark F.; Miller, Ronnie K.; Shepherd, Brent

    2001-07-01

    Following the explosion of Delta 241 (IIR-1) on January 17th, 1997, the failure investigation board concluded that the Graphite Epoxy Motorcases (GEM's) should be inspected for damage just prior to launch. Subsequent investigations and feedback from industry led to an Aerospace Corporation proposal to instrument the entire fleet of GEM's with a continuous health monitoring system. The period of monitoring would extend from the initial acceptance testing through final erection on the launch pad. As this proposal demonstrates, (along with the increasing use of advanced composite materials in aircraft, automobiles, military hardware, and aerospace components such as rocket motorcases) a sizable need for composite health assessment measures exist. Particularly where continuous monitoring is required for the detection of damage from impacts and other sources of high mechanical and thermal stresses. Even low-momentum impacts can lead to barely visible impact damage (BVID), corresponding to a significant weakening of the composite. This damage, undetectable by visual inspection, can in turn lead to sudden and catastrophic failure when the material is subjected to a normal operating load. There is perhaps no system with as much potential for truly catastrophic failure as a rocket motor. We will present an update on our ongoing efforts with the United States Air Force Delta II Program Office, and The Aerospace Corporation. This will cover the development of a local, portable, surface-mounted, fiberoptic sensor based impact damage monitor designed to operate on a Delta II GEM during transport, storage, and handling. This system is designed to continuously monitor the GEMs, to communicate wirelessly with base stations and maintenance personnel, to operate autonomously for extended periods, and to fit unobtrusively on the GEM itself.

  16. Health Monitoring of Composite Material Structures using a Vibrometry Technique

    Science.gov (United States)

    Schulz, Mark J.

    1997-01-01

    Large composite material structures such as aircraft and Reusable Launch Vehicles (RLVS) operate in severe environments comprised of vehicle dynamic loads, aerodynamic loads, engine vibration, foreign object impact, lightning strikes, corrosion, and moisture absorption. These structures are susceptible to damage such as delamination, fiber breaking/pullout, matrix cracking, and hygrothermal strain. To ensure human safety and load-bearing integrity, these structures must be inspected to detect and locate often invisible damage and faults before becoming catastrophic. Moreover, nearly all future structures will need some type of in-service inspection technique to increase their useful life and reduce maintenance and overall costs. Possible techniques for monitoring the health and indicating damage on composite structures include: c-scan, thermography, acoustic emissions using piezoceramic actuators or fiber-optic wires with gratings, laser ultrasound, shearography, holography, x-ray, and others. These techniques have limitations in detecting damage that is beneath the surface of the structure, far away from a sensor location, or during operation of the vehicle. The objective of this project is to develop a more global method for damage detection that is based on structural dynamics principles, and can inspect for damage when the structure is subjected to vibratory loads to expose faults that may not be evident by static inspection. A Transmittance Function Monitoring (TFM) method is being developed in this project for ground-based inspection and operational health monitoring of large composite structures as a RLV. A comparison of the features of existing health monitoring approaches and the proposed TFM method is given.

  17. A qualitative review for wireless health monitoring system

    Science.gov (United States)

    Arshad, Atika; Fadzil Ismail, Ahmad; Khan, Sheroz; Zahirul Alam, A. H. M.; Tasnim, Rumana; Samnan Haider, Syed; Shobaki, Mohammed M.; Shahid, Zeeshan

    2013-12-01

    A proliferating interest has been being observed over the past years in accurate wireless system development in order to monitor incessant human activities in health care centres. Furthermore because of the swelling number of elderly population and the inadequate number of competent staffs for nursing homes there is a big market petition for health care monitoring system. In order to detect human researchers developed different methods namely which include Field Identification technique, Visual Sensor Network, radar detection, e-mobile techniques and so on. An all-encompassing overview of the non-wired human detection application advancement is presented in this paper. Inductive links are used for human detection application while wiring an electronic system has become impractical in recent times. Keeping in mind the shortcomings, an Inductive Intelligent Sensor (IIS) has been proposed as a novel human monitoring system for future implementation. The proposed sensor works towards exploring the signature signals of human body movement and size. This proposed sensor is fundamentally based on inductive loop that senses the presence and a passing human resulting an inductive change.

  18. A bio-inspired memory model for structural health monitoring

    Science.gov (United States)

    Zheng, Wei; Zhu, Yong

    2009-04-01

    Long-term structural health monitoring (SHM) systems need intelligent management of the monitoring data. By analogy with the way the human brain processes memories, we present a bio-inspired memory model (BIMM) that does not require prior knowledge of the structure parameters. The model contains three time-domain areas: a sensory memory area, a short-term memory area and a long-term memory area. First, the initial parameters of the structural state are specified to establish safety criteria. Then the large amount of monitoring data that falls within the safety limits is filtered while the data outside the safety limits are captured instantly in the sensory memory area. Second, disturbance signals are distinguished from danger signals in the short-term memory area. Finally, the stable data of the structural balance state are preserved in the long-term memory area. A strategy for priority scheduling via fuzzy c-means for the proposed model is then introduced. An experiment on bridge tower deformation demonstrates that the proposed model can be applied for real-time acquisition, limited-space storage and intelligent mining of the monitoring data in a long-term SHM system.

  19. A bio-inspired memory model for structural health monitoring

    International Nuclear Information System (INIS)

    Zheng, Wei; Zhu, Yong

    2009-01-01

    Long-term structural health monitoring (SHM) systems need intelligent management of the monitoring data. By analogy with the way the human brain processes memories, we present a bio-inspired memory model (BIMM) that does not require prior knowledge of the structure parameters. The model contains three time-domain areas: a sensory memory area, a short-term memory area and a long-term memory area. First, the initial parameters of the structural state are specified to establish safety criteria. Then the large amount of monitoring data that falls within the safety limits is filtered while the data outside the safety limits are captured instantly in the sensory memory area. Second, disturbance signals are distinguished from danger signals in the short-term memory area. Finally, the stable data of the structural balance state are preserved in the long-term memory area. A strategy for priority scheduling via fuzzy c-means for the proposed model is then introduced. An experiment on bridge tower deformation demonstrates that the proposed model can be applied for real-time acquisition, limited-space storage and intelligent mining of the monitoring data in a long-term SHM system

  20. Monitoring of health and environment by National Uranium Company (NUC)

    International Nuclear Information System (INIS)

    Georgescu, D.P.; Banciu, O

    1998-01-01

    Among the activities of geological survey, exploitation and processing of radioactive ore performed by National Uranium Company (NUC) a major attention is paid to personnel medical monitoring, to influences on the public health in the affected zones and also to the impact on environment, based on specific criteria and accomplished by medical and technical institutions having an adequate profile, in conformity with the enforced laws and with recommendations of international authorities on this field. Health monitoring of the active and retired personnel and of population from the affected sites by the NUC activities is done on the basis of a program established in co-operation with the Work Protection Department and the management of the company's subunits. The methodology used at present has the following three stages: 1. Periodical medical examination of the personnel including all the compulsory investigations requested by the Ministry of Health; 2. Annual epidemiology descriptive studies concerning the analysis of the personnel health state; 3. Analytical epidemiologic studies (retrospective and prospective) having the aim of surveying the radiation effects on the human target organs of the exposed personnel and also the impact on the public health in the influenced zones. At present the incidence of professional diseases liked to uranium is no longer a problem. Attention has to be focused to the diseases due to microclimate, noise, intensive physical effort and stress (non-specific chronic breathing diseases, arterial high blood pressure, heart diseases, digestive diseases and neuroses). The paper presents also the environmental factors investigated in connection with the importance which they have in radioactive contamination: air, water, soil, sediments, vegetation, and agricultural products. There are given the results of the tests performed on 25,000 samples and from more then 20,000 radiometric measurements performed between 1975 - 1997 in each subunit of

  1. Remote health monitoring for elderly through interactive television

    Science.gov (United States)

    2012-01-01

    Background Providing remote health monitoring to specific groups of patients represents an issue of great relevance for the national health systems, because of the costs related to moving health operators, the time spent to reach remote sites, and the high number of people needing health assistance. At the same time, some assistance activities, like those related to chronical diseases, may be satisfied through a remote interaction with the patient, without a direct medical examination. Methods Moving from this considerations, our paper proposes a system architecture for the provisioning of remote health assistance to older adults, based on a blind management of a network of wireless medical devices, and an interactive TV Set Top Box for accessing health related data. The selection of TV as the interface between the user and the system is specifically targeted to older adults. Due to the private nature of the information exchanged, a certified procedure is implemented for data delivery, through the use of non conditional smart cards. All these functions may be accomplished through a proper design of the system management, and a suitable interactive application. Results The interactive application acting as the interface between the user and the system on the TV monitor has been evaluated able to help readability and clear understanding of the contents and functions proposed. Thanks to the limited amount of data to transfer, even a Set Top Box equipped with a traditional PSTN modem may be used to support the proposed service at a basic level; more advanced features, like audio/video connection, may be activated if the Set Top Box enables a broadband connection (e.g. ADSL). Conclusions The proposed layered architecture for a remote health monitoring system can be tailored to address a wide range of needs, according with each patient’s conditions and capabilities. The system exploits the potentialities offered by Digital Television receivers, a friendly MHP interface

  2. Wake-up transceivers for structural health monitoring of bridges

    Science.gov (United States)

    Kumberg, T.; Kokert, J.; Younesi, V.; Koenig, S.; Reindl, L. M.

    2016-04-01

    In this article we present a wireless sensor network to monitor the structural health of a large-scale highway bridge in Germany. The wireless sensor network consists of several sensor nodes that use wake-up receivers to realize latency free and low-power communication. The sensor nodes are either equipped with very accurate tilt sensor developed by Northrop Grumman LITEF GmbH or with a Novatel OEM615 GNSS receiver. Relay nodes are required to forward measurement data to a base station located on the bridge. The base station is a gateway that transmits the local measurement data to a remote server where it can be further analyzed and processed. Further on, we present an energy harvesting system to supply the energy demanding GNSS sensor nodes to realize long term monitoring.

  3. Performance Health Monitoring of Large-Scale Systems

    Energy Technology Data Exchange (ETDEWEB)

    Rajamony, Ram [IBM Research, Austin, TX (United States)

    2014-11-20

    This report details the progress made on the ASCR funded project Performance Health Monitoring for Large Scale Systems. A large-­scale application may not achieve its full performance potential due to degraded performance of even a single subsystem. Detecting performance faults, isolating them, and taking remedial action is critical for the scale of systems on the horizon. PHM aims to develop techniques and tools that can be used to identify and mitigate such performance problems. We accomplish this through two main aspects. The PHM framework encompasses diagnostics, system monitoring, fault isolation, and performance evaluation capabilities that indicates when a performance fault has been detected, either due to an anomaly present in the system itself or due to contention for shared resources between concurrently executing jobs. Software components called the PHM Control system then build upon the capabilities provided by the PHM framework to mitigate degradation caused by performance problems.

  4. Printing of microstructure strain sensor for structural health monitoring

    Science.gov (United States)

    Le, Minh Quyen; Ganet, Florent; Audigier, David; Capsal, Jean-Fabien; Cottinet, Pierre-Jean

    2017-05-01

    Recent advances in microelectronics and materials should allow the development of integrated sensors with transduction properties compatible with being printed directly onto a 3D substrate, especially metallic and polymer substrates. Inorganic and organic electronic materials in microstructured and nanostructured forms, intimately integrated in ink, offer particularly attractive characteristics, with realistic pathways to sophisticated embodiments. Here, we report on these strategies and demonstrate the potential of 3D-printed microelectronics based on a structural health monitoring (SHM) application for the precision weapon systems. We show that our printed sensors can be employed in non-invasive, high-fidelity and continuous strain monitoring of handguns, making it possible to implement printed sensors on a 3D substrate in either SHM or remote diagnostics. We propose routes to commercialization and novel device opportunities and highlight the remaining challenges for research.

  5. Rate-based structural health monitoring using permanently installed sensors

    Science.gov (United States)

    Corcoran, Joseph

    2017-09-01

    Permanently installed sensors are becoming increasingly ubiquitous, facilitating very frequent in situ measurements and consequently improved monitoring of `trends' in the observed system behaviour. It is proposed that this newly available data may be used to provide prior warning and forecasting of critical events, particularly system failure. Numerous damage mechanisms are examples of positive feedback; they are `self-accelerating' with an increasing rate of damage towards failure. The positive feedback leads to a common time-response behaviour which may be described by an empirical relation allowing prediction of the time to criticality. This study focuses on Structural Health Monitoring of engineering components; failure times are projected well in advance of failure for fatigue, creep crack growth and volumetric creep damage experiments. The proposed methodology provides a widely applicable framework for using newly available near-continuous data from permanently installed sensors to predict time until failure in a range of application areas including engineering, geophysics and medicine.

  6. Probabilistic Structural Health Monitoring of the Orbiter Wing Leading Edge

    Science.gov (United States)

    Yap, Keng C.; Macias, Jesus; Kaouk, Mohamed; Gafka, Tammy L.; Kerr, Justin H.

    2011-01-01

    A structural health monitoring (SHM) system can contribute to the risk management of a structure operating under hazardous conditions. An example is the Wing Leading Edge Impact Detection System (WLEIDS) that monitors the debris hazards to the Space Shuttle Orbiter s Reinforced Carbon-Carbon (RCC) panels. Since Return-to-Flight (RTF) after the Columbia accident, WLEIDS was developed and subsequently deployed on board the Orbiter to detect ascent and on-orbit debris impacts, so as to support the assessment of wing leading edge structural integrity prior to Orbiter re-entry. As SHM is inherently an inverse problem, the analyses involved, including those performed for WLEIDS, tend to be associated with significant uncertainty. The use of probabilistic approaches to handle the uncertainty has resulted in the successful implementation of many development and application milestones.

  7. Forest health monitoring in the United States: focus on national reports

    Science.gov (United States)

    Kurt Riitters; Kevin Potter

    2013-01-01

    The health and sustainability of United States forests have been monitored for many years from several different perspectives. The national Forest Health Monitoring (FHM) Program was established in 1990 by Federal and State agencies to develop a national system for monitoring and reporting on the status and trends of forest ecosystem health. We describe and illustrate...

  8. Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques.

    Directory of Open Access Journals (Sweden)

    Shirin Enshaeifar

    Full Text Available The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM. TIHM is a technology assisted monitoring system that uses Internet of Things (IoT enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients' routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.

  9. A Low Cost Sensor Controller for Health Monitoring

    Science.gov (United States)

    Birbas, M.; Petrellis, N.; Gioulekas, F.

    2015-09-01

    Aging population can benefit from health care systems that allow their health and daily life to be monitored by expert medical staff. Blood pressure, temperature measurements or more advanced tests like Electrocardiograms (ECG) can be ordered through such a healthcare system while urgent situations can be detected and alleviated on time. The results of these tests can be stored with security in a remote cloud or database. Such systems are often used to monitor non-life threatening patient health problems and their advantage in lowering the cost of the healthcare services is obvious. A low cost commercial medical sensor kit has been used in the present work, trying to improve the accuracy and stability of the sensor measurements, the power consumption, etc. This Sensor Controller communicates with a Gateway installed in the patient's residence and a tablet or smart phone used for giving instructions to the patient through a comprehensive user interface. A flexible communication protocol has been defined supporting any short or long term sensor sampling scenario. The experimental results show that it is possible to achieve low power consumption by applying apropriate sleep intervals to the Sensor Controller and by deactivating periodically some of its functionality.

  10. A Simple Demonstration of Concrete Structural Health Monitoring Framework

    Energy Technology Data Exchange (ETDEWEB)

    Mahadevan, Sankaran [Idaho National Lab. (INL), Idaho Falls, ID (United States); Agarwal, Vivek [Idaho National Lab. (INL), Idaho Falls, ID (United States); Cai, Guowei [Idaho National Lab. (INL), Idaho Falls, ID (United States); Nath, Paromita [Idaho National Lab. (INL), Idaho Falls, ID (United States); Bao, Yanqing [Idaho National Lab. (INL), Idaho Falls, ID (United States); Bru Brea, Jose Maria [Idaho National Lab. (INL), Idaho Falls, ID (United States); Koester, David [Idaho National Lab. (INL), Idaho Falls, ID (United States); Adams, Douglas [Idaho National Lab. (INL), Idaho Falls, ID (United States); Kosson, David [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2015-03-01

    Assessment and management of aging concrete structures in nuclear power plants require a more systematic approach than simple reliance on existing code margins of safety. Structural health monitoring of concrete structures aims to understand the current health condition of a structure based on heterogeneous measurements to produce high confidence actionable information regarding structural integrity that supports operational and maintenance decisions. This ongoing research project is seeking to develop a probabilistic framework for health diagnosis and prognosis of aging concrete structures in a nuclear power plant subjected to physical, chemical, environment, and mechanical degradation. The proposed framework consists of four elements—damage modeling, monitoring, data analytics, and uncertainty quantification. This report describes a proof-of-concept example on a small concrete slab subjected to a freeze-thaw experiment that explores techniques in each of the four elements of the framework and their integration. An experimental set-up at Vanderbilt University’s Laboratory for Systems Integrity and Reliability is used to research effective combination of full-field techniques that include infrared thermography, digital image correlation, and ultrasonic measurement. The measured data are linked to the probabilistic framework: the thermography, digital image correlation data, and ultrasonic measurement data are used for Bayesian calibration of model parameters, for diagnosis of damage, and for prognosis of future damage. The proof-of-concept demonstration presented in this report highlights the significance of each element of the framework and their integration.

  11. INTEROPERABLE FRAMEWORK SOLUTION TO ICU HEALTH CARE MONITORING

    Directory of Open Access Journals (Sweden)

    Shola Usha Rani

    2015-03-01

    Full Text Available An interoperable telehealth system provides an independent healthcare solution for better management of health and wellness. It allows people to manage their heart disease and diabetes etc. by sending their health parameters like blood pressure, heart rate, glucose levels, temperature, weight, respiration from remote place to health professional, and get real-time feedback on their condition. Here different medical devices are connected to the patient for monitoring. Each kind of device is manufactured by different vendors. And each device information and communication requires different installation and network design. It causes design complexities and network overheads when moving patients for diagnosis examinations. This problem will be solved by interoperability among devices. The ISO/IEEE 11073 is an international standard which produces interoperable hospital information system solution to medical devices. One such type of integrated environment that requires the integration of medical devices is ICU (Intensive Care Unit. This paper presents the issues for ICU monitoring system and framework solution for it.

  12. Structural Health Monitoring of Nuclear Spent Fuel Storage Facilities

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Lingyu

    2018-04-10

    Interim storage of spent nuclear fuel from reactor sites has gained additional importance and urgency for resolving waste-management-related technical issues. To ensure that nuclear power remains clean energy, monitoring has been identified by DOE as a high priority cross-cutting need, necessary to determine and predict the degradation state of the systems, structures, and components (SSCs) important to safety (ITS). Therefore, nondestructive structural condition monitoring becomes a need to be installed on existing or to be integrated into future storage system to quantify the state of health or to guarantee the safe operation of nuclear power plants (NPPs) during their extended life span. In this project, the lead university and the collaborating national laboratory teamed to develop a nuclear structural health monitoring (n-SHM) system based on in-situ piezoelectric sensing technologies that can monitor structural degradation and aging for nuclear spent fuel DCSS and similar structures. We also aimed to identify and quantify possible influences of nuclear spent fuel environment (temperature and radiation) to the piezoelectric sensor system and come up with adequate solutions and guidelines therefore. We have therefore developed analytical model for piezoelectric based n-SHM methods, with considerations of temperature and irradiation influence on the model of sensing and algorithms in acoustic emission (AE), guided ultrasonic waves (GUW), and electromechanical impedance spectroscopy (EMIS). On the other side, experimentally the temperature and irradiation influence on the piezoelectric sensors and sensing capabilities were investigated. Both short-term and long-term irradiation investigation with our collaborating national laboratory were performed. Moreover, we developed multi-modal sensing, validated in laboratory setup, and conducted the testing on the We performed multi-modal sensing development, verification and validation tests on very complex structures

  13. ChemAND - a system health monitor for plant chemistry

    Energy Technology Data Exchange (ETDEWEB)

    Turner, C.W.; Mitchel, G.R.; Tosello, G.; Balakrishnan, P.V.; McKay, G.; Thompson, M. [Atomic Energy of Canada Limited, Chalk River, Ontario (Canada); Dundar, Y.; Bergeron, M.; Laporte, R. [Hydro-Quebec, Groupe Chimie, Centrale Nucleaire Gentilly-2, Gentilly, Quebec (Canada)

    2001-03-01

    Effective management of plant systems throughout their lifetime requires much more than data acquisition and display - it requires that the plant's system health be continually monitored and managed. AECL has developed a System Health Monitor called ChemAND for CANDU plant chemistry. ChemAND, a Chemistry ANalysis and Diagnostic system, monitors key chemistry parameters in the heat transport system, moderator-cover gas, annulus gas, and the steam cycle during full-power operation. These parameters can be used as inputs to models that calculate the effect of current plant operating conditions on the present and future health of the system. Chemistry data from each of the systems are extracted on a regular basis from the plant's Historical Data Server and are sorted according to function, e.g., indicators for condenser in-leakage, air in-leakage, heavy water leakage into the annulus gas, fuel failure, etc. Each parameter is conveniently displayed and is trended along with its alarm limits. ChemAND currently includes two analytical models developed for the balance-of-plant. The first model, ChemSolv, calculates crevice chemistry conditions in the steam generator (SG) from either the SG blowdown chemistry conditions or from a simulated condenser leak. This information can be used by plant staff to evaluate the susceptibility of the SG tubes to crevice corrosion. ChemSolv also calculates chemistry conditions throughout the steam-cycle system as determined by the transport of volatile species such as ammonia, hydrazine, morpholine, and oxygen. The second model, SLUDGE, calculates the deposit loading and distribution in the SG as a function of time, based on concentrations of corrosion product in the final feedwater for both normal and start-up conditions. Operations personnel can use this information to predict where to inspect and when to clean. (author)

  14. Monitoring health interventions--who's afraid of LQAS?

    Science.gov (United States)

    Pezzoli, Lorenzo; Kim, Sung Hye

    2013-11-08

    Lot quality assurance sampling (LQAS) is used to evaluate health services. Subunits of a population (lots) are accepted or rejected according to the number of failures in a random sample (N) of a given lot. If failures are greater than decision value (d), we reject the lot and recommend corrective actions in the lot (i.e. intervention area); if they are equal to or less than d, we accept it. We used LQAS to monitor coverage during the last 3 days of a meningitis vaccination campaign in Niger. We selected one health area (lot) per day reporting the lowest administrative coverage in the previous 2 days. In the sampling plan we considered: N to be small enough to allow us to evaluate one lot per day, deciding to sample 16 individuals from the selected villages of each health area, using probability proportionate to population size; thresholds and d to vary according to administrative coverage reported; α ≤5% (meaning that, if we would have conducted the survey 100 times, we would have accepted the lot up to five times when real coverage was at an unacceptable level) and β ≤20% (meaning that we would have rejected the lot up to 20 times, when real coverage was equal or above the satisfactory level). We classified all three lots as with the acceptable coverage. LQAS appeared to be a rapid, simple, and statistically sound method for in-process coverage assessment. We encourage colleagues in the field to consider using LQAS in complement with other monitoring techniques such as house-to-house monitoring.

  15. Monitoring health interventions – who's afraid of LQAS?

    Directory of Open Access Journals (Sweden)

    Lorenzo Pezzoli

    2013-11-01

    Full Text Available Lot quality assurance sampling (LQAS is used to evaluate health services. Subunits of a population (lots are accepted or rejected according to the number of failures in a random sample (N of a given lot. If failures are greater than decision value (d, we reject the lot and recommend corrective actions in the lot (i.e. intervention area; if they are equal to or less than d, we accept it. We used LQAS to monitor coverage during the last 3 days of a meningitis vaccination campaign in Niger. We selected one health area (lot per day reporting the lowest administrative coverage in the previous 2 days. In the sampling plan we considered: N to be small enough to allow us to evaluate one lot per day, deciding to sample 16 individuals from the selected villages of each health area, using probability proportionate to population size; thresholds and d to vary according to administrative coverage reported; α≤5% (meaning that, if we would have conducted the survey 100 times, we would have accepted the lot up to five times when real coverage was at an unacceptable level and β≤20% (meaning that we would have rejected the lot up to 20 times, when real coverage was equal or above the satisfactory level. We classified all three lots as with the acceptable coverage. LQAS appeared to be a rapid, simple, and statistically sound method for in-process coverage assessment. We encourage colleagues in the field to consider using LQAS in complement with other monitoring techniques such as house-to-house monitoring.

  16. Monitoring health interventions – who's afraid of LQAS?

    Science.gov (United States)

    Pezzoli, Lorenzo; Kim, Sung Hye

    2013-01-01

    Lot quality assurance sampling (LQAS) is used to evaluate health services. Subunits of a population (lots) are accepted or rejected according to the number of failures in a random sample (N) of a given lot. If failures are greater than decision value (d), we reject the lot and recommend corrective actions in the lot (i.e. intervention area); if they are equal to or less than d, we accept it. We used LQAS to monitor coverage during the last 3 days of a meningitis vaccination campaign in Niger. We selected one health area (lot) per day reporting the lowest administrative coverage in the previous 2 days. In the sampling plan we considered: N to be small enough to allow us to evaluate one lot per day, deciding to sample 16 individuals from the selected villages of each health area, using probability proportionate to population size; thresholds and d to vary according to administrative coverage reported; α ≤5% (meaning that, if we would have conducted the survey 100 times, we would have accepted the lot up to five times when real coverage was at an unacceptable level) and β ≤20% (meaning that we would have rejected the lot up to 20 times, when real coverage was equal or above the satisfactory level). We classified all three lots as with the acceptable coverage. LQAS appeared to be a rapid, simple, and statistically sound method for in-process coverage assessment. We encourage colleagues in the field to consider using LQAS in complement with other monitoring techniques such as house-to-house monitoring. PMID:24206650

  17. ChemAND - a system health monitor for plant chemistry

    International Nuclear Information System (INIS)

    Turner, C.W.; Mitchell, G.R.; Tosello, G.; Balakrishnan, P.V.; McKay, G.; Thompson, M.; Dundar, Y.; Bergeron, M.; Laporte, R.

    2001-01-01

    Effective management of plant systems throughout their lifetime requires much more than data acquisition and display-it requires that the plant's system health be continually monitored and managed. AECL has developed a System Health Monitor called ChemAND for CANDU plant chemistry. ChemAND, a Chemistry ANalysis and Diagnostic system, monitors key chemistry parameters in the heat transport system, moderator-cover gas, annulus gas, and the steam cycle during full-power operation. These parameters can be used as inputs to models that calculate the effect of current plant operating conditions on the present and future health of the system. Chemistry data from each of the systems are extracted on a regular basis from the plant's Historical Data Server and are sorted according to function, e.g., indicators for condenser in-leakage, air in-leakage, heavy water leakage into the annulus gas, fuel failure, etc. Each parameter is conveniently displayed and is trended along with its alarm limits. ChemAND currently includes two analytical models developed for the balance-of-plant. The first model, ChemSolv, calculates crevice chemistry conditions in the steam generator (SG) from either the SG blowdown chemistry conditions or from a simulated condenser leak. This information can be used by plant staff to evaluate the susceptibility of the SG tubes to crevice corrosion. ChemSolv also calculates chemistry conditions throughout the steam cycle system, as determined by the transport of volatile species such as ammonia, hydrazine, morpholine, and oxygen. The second model, SLUDGE, calculates the deposit loading and distribution in the SG as a function of time, based on concentrations of corrosion product in the final feedwater for both normal and start-up conditions. Operations personnel can use this information to predict where to inspect and when to clean. (author)

  18. ChemAND - a system health monitor for plant chemistry

    International Nuclear Information System (INIS)

    Turner, C.W.; Mitchel, G.R.; Tosello, G.; Balakrishnan, P.V.; McKay, G.; Thompson, M.; Dundar, Y.; Bergeron, M.; Laporte, R.

    2001-03-01

    Effective management of plant systems throughout their lifetime requires much more than data acquisition and display - it requires that the plant's system health be continually monitored and managed. AECL has developed a System Health Monitor called ChemAND for CANDU plant chemistry. ChemAND, a Chemistry ANalysis and Diagnostic system, monitors key chemistry parameters in the heat transport system, moderator-cover gas, annulus gas, and the steam cycle during full-power operation. These parameters can be used as inputs to models that calculate the effect of current plant operating conditions on the present and future health of the system. Chemistry data from each of the systems are extracted on a regular basis from the plant's Historical Data Server and are sorted according to function, e.g., indicators for condenser in-leakage, air in-leakage, heavy water leakage into the annulus gas, fuel failure, etc. Each parameter is conveniently displayed and is trended along with its alarm limits. ChemAND currently includes two analytical models developed for the balance-of-plant. The first model, ChemSolv, calculates crevice chemistry conditions in the steam generator (SG) from either the SG blowdown chemistry conditions or from a simulated condenser leak. This information can be used by plant staff to evaluate the susceptibility of the SG tubes to crevice corrosion. ChemSolv also calculates chemistry conditions throughout the steam-cycle system as determined by the transport of volatile species such as ammonia, hydrazine, morpholine, and oxygen. The second model, SLUDGE, calculates the deposit loading and distribution in the SG as a function of time, based on concentrations of corrosion product in the final feedwater for both normal and start-up conditions. Operations personnel can use this information to predict where to inspect and when to clean. (author)

  19. A STUDY ON HEALTH MONITORING SYSTEM: RECENT ADVANCEMENTS

    Directory of Open Access Journals (Sweden)

    Atika Arshad

    2014-12-01

    Full Text Available ABSTRACT: A proliferating interest has been observed over the past years in the development of an accurate system for monitoring continuous human activities in the health care sectors, especially for the elderly. This paper conducts a survey of the various techniques and methods that are proposed to monitor the movements and activities of the elderly people. These techniques promise a useful and dependable detection system to give support and lessen the medical expenses of health care for the elderly. The detection approaches are divided into five main categories: wearable device based, wireless based, ambience device based, vision based and floor sensor / electric field sensors based. These techniques have focused on the pros and cons of the existing methods for recognizing the prospective scope of research in the domain of health monitoring systems. Apart from highlighting and analyzing the features of the existing techniques, perspectives on probable future studies have been detailed. ABSTRAK: Dewasa ini, pembangunan sistem yang tepat untuk memantau aktiviti berterusan terutamanya dalam sektor kesihatan warga tua mula mendapat tempat. Kaji selidik telah dijalankan dengan pelbagai teknik dan kaedah untuk meninjau pergerakan dan aktiviti golongan warga tua. Kaedah-kaedah ini memberikan sistem pengesanan yang berguna dan dipercayai untuk memberikan sokongan serta mengurangkan kos perubatan kesihatan bagi golongan tua. Pendekatan pengesanan dibahagikan kepada lima kategori utama; alatan yang dapat dipakai, alatan tanpa wayar, alatan berdasarkan persekitaran, alatan berasaskan penglihatan dan alatan berdasarkan pengesan pada lantai / medan elektrik.  Teknik-teknik ini memfokuskan kepada pro dan kontra kaedah yang sedia ada untuk mengenalpasti skop prospektif penyelidikan dalam domain sistem pengawasan kesihatan.  Selain daripada mengetengah dan menganalisa ciri-ciri teknik yang sedia ada, perspektif kajian akan datang juga diperincikan.KEYWORDS: health

  20. Structural Health Monitoring of Bridges with Fiber Bragg Grating Sensors

    Directory of Open Access Journals (Sweden)

    Francisco Navarro-Henríquez

    2014-11-01

    Systems with fiber optic sensors FBG (Fiber Bragg Grating are consolidated in the Structural Health Monitoring (SMH of bridges, Nondestructive Testing (NDT static and dynamic measurements of deformation, displacement, deflection, temperature and vibration. This article provides a brief introduction to the technology and the fundamentals of fiber optic sensors, also present comparative advantages over its traditional counterpart is presented. Their characteristics are described and measurement graphics are presented as an application example of the FBG sensors. Finally, some key aspects to consider for proper use in the field are mentioned.

  1. Monitoring health interventions – who's afraid of LQAS?

    OpenAIRE

    Lorenzo Pezzoli; Sung Hye Kim

    2013-01-01

    Lot quality assurance sampling (LQAS) is used to evaluate health services. Subunits of a population (lots) are accepted or rejected according to the number of failures in a random sample (N) of a given lot. If failures are greater than decision value (d), we reject the lot and recommend corrective actions in the lot (i.e. intervention area); if they are equal to or less than d, we accept it. We used LQAS to monitor coverage during the last 3 days of a meningitis vaccination campaign in Niger....

  2. Northern Rivers Basins human health monitoring program : report

    International Nuclear Information System (INIS)

    Gabos, S.

    1999-04-01

    The Northern River Basins Human Health Monitoring Program was established in 1994 to investigate the possible relationships between various environmental risk factors and the health of northern residents in the province. This report presents the initial analysis of the health program and examines the differences in health outcomes across the province and compares the Northern Rivers Basin Study (NRBS) area with the other areas of the province. A series of maps and graphs showed the prevalence of certain diseases and disorders within the Peace and Athabasca river basins. The focus of the report was on reproductive health, congenital anomalies, respiratory ailments, circulatory diseases, gastrointestinal disorders, endocrine and metabolic disorders, and neurocognitive disorders. The study showed that compared to other areas of the province, the NRBS area had higher incidences of endometriosis, selected congenital anomalies, bronchitis, pneumonia, peptic ulcers and epilepsy. There were three potential exposure pathways to environmental contaminants. These were through ingestion of water or food, inhalation of air and through dermal exposure. refs., tabs., figs

  3. Northern Rivers Basins human health monitoring program : report

    Energy Technology Data Exchange (ETDEWEB)

    Gabos, S. [Alberta Health, Edmonton, AB (Canada). Health Surveillance

    1999-04-01

    The Northern River Basins Human Health Monitoring Program was established in 1994 to investigate the possible relationships between various environmental risk factors and the health of northern residents in the province. This report presents the initial analysis of the health program and examines the differences in health outcomes across the province and compares the Northern Rivers Basin Study (NRBS) area with the other areas of the province. A series of maps and graphs showed the prevalence of certain diseases and disorders within the Peace and Athabasca river basins. The focus of the report was on reproductive health, congenital anomalies, respiratory ailments, circulatory diseases, gastrointestinal disorders, endocrine and metabolic disorders, and neurocognitive disorders. The study showed that compared to other areas of the province, the NRBS area had higher incidences of endometriosis, selected congenital anomalies, bronchitis, pneumonia, peptic ulcers and epilepsy. There were three potential exposure pathways to environmental contaminants. These were through ingestion of water or food, inhalation of air and through dermal exposure. refs., tabs., figs.

  4. Remote Monitoring of the Structural Health of Hydrokinetic Composite Turbine Blades

    Energy Technology Data Exchange (ETDEWEB)

    J.L. Rovey

    2012-09-21

    A health monitoring approach is investigated for hydrokinetic turbine blade applications. In-service monitoring is critical due to the difficult environment for blade inspection and the cost of inspection downtime. Composite blade designs have advantages that include long life in marine environments and great control over mechanical properties. Experimental strain characteristics are determined for static loads and free-vibration loads. These experiments are designed to simulate the dynamic characteristics of hydrokinetic turbine blades. Carbon/epoxy symmetric composite laminates are manufactured using an autoclave process. Four-layer composite beams, eight-layer composite beams, and two-dimensional eight-layer composite blades are instrumented for strain. Experimental results for strain measurements from electrical resistance gages are validated with theoretical characteristics obtained from in-house finite-element analysis for all sample cases. These preliminary tests on the composite samples show good correlation between experimental and finite-element strain results. A health monitoring system is proposed in which damage to a composite structure, e.g. delamination and fiber breakage, causes changes in the strain signature behavior. The system is based on embedded strain sensors and embedded motes in which strain information is demodulated for wireless transmission. In-service monitoring is critical due to the difficult environment for blade inspection and the cost of inspection downtime. Composite blade designs provide a medium for embedding sensors into the blades for in-situ health monitoring. The major challenge with in-situ health monitoring is transmission of sensor signals from the remote rotating reference frame of the blade to the system monitoring station. In the presented work, a novel system for relaying in-situ blade health measurements in hydrokinetic systems is described and demonstrated. An ultrasonic communication system is used to transmit

  5. SHARP - Automated monitoring of spacecraft health and status

    Science.gov (United States)

    Atkinson, David J.; James, Mark L.; Martin, R. G.

    1990-01-01

    Briefly discussed here are the spacecraft and ground systems monitoring process at the Jet Propulsion Laboratory (JPL). Some of the difficulties associated with the existing technology used in mission operations are highlighted. A new automated system based on artificial intelligence technology is described which seeks to overcome many of these limitations. The system, called the Spacecraft Health Automated Reasoning Prototype (SHARP), is designed to automate health and status analysis for multi-mission spacecraft and ground data systems operations. The system has proved to be effective for detecting and analyzing potential spacecraft and ground systems problems by performing real-time analysis of spacecraft and ground data systems engineering telemetry. Telecommunications link analysis of the Voyager 2 spacecraft was the initial focus for evaluation of the system in real-time operations during the Voyager spacecraft encounter with Neptune in August 1989.

  6. SHARP: Automated monitoring of spacecraft health and status

    Science.gov (United States)

    Atkinson, David J.; James, Mark L.; Martin, R. Gaius

    1991-01-01

    Briefly discussed here are the spacecraft and ground systems monitoring process at the Jet Propulsion Laboratory (JPL). Some of the difficulties associated with the existing technology used in mission operations are highlighted. A new automated system based on artificial intelligence technology is described which seeks to overcome many of these limitations. The system, called the Spacecraft Health Automated Reasoning Prototype (SHARP), is designed to automate health and status analysis for multi-mission spacecraft and ground data systems operations. The system has proved to be effective for detecting and analyzing potential spacecraft and ground systems problems by performing real-time analysis of spacecraft and ground data systems engineering telemetry. Telecommunications link analysis of the Voyager 2 spacecraft was the initial focus for evaluation of the system in real-time operations during the Voyager spacecraft encounter with Neptune in August 1989.

  7. Biosecurity and Health Monitoring at the Zebrafish International Resource Center.

    Science.gov (United States)

    Murray, Katrina N; Varga, Zoltán M; Kent, Michael L

    2016-07-01

    The Zebrafish International Resource Center (ZIRC) is a repository and distribution center for mutant, transgenic, and wild-type zebrafish. In recent years annual imports of new zebrafish lines to ZIRC have increased tremendously. In addition, after 15 years of research, we have identified some of the most virulent pathogens affecting zebrafish that should be avoided in large production facilities, such as ZIRC. Therefore, while importing a high volume of new lines we prioritize safeguarding the health of our in-house fish colony. Here, we describe the biosecurity and health-monitoring program implemented at ZIRC. This strategy was designed to prevent introduction of new zebrafish pathogens, minimize pathogens already present in the facility, and ensure a healthy zebrafish colony for in-house uses and shipment to customers.

  8. Machine Learning Takes on Health Care: Leonard D'Avolio's Cyft Employs Big Data to Benefit Patients and Providers.

    Science.gov (United States)

    Mertz, Leslie

    2018-01-01

    When Leonard D'Avolio (Figure 1) was working on his Ph.D. degree in biomedical informatics, he saw the power of machine learning in transforming multiple industries; health care, however, was not among them. "The reason that Amazon, Netflix, and Google have transformed their industries is because they have embedded learning throughout every aspect of what they do. If we could prove that is possible in health care too, I thought we would have the potential to have a huge impact," he says.

  9. Historic Bim: a New Repository for Structural Health Monitoring

    Science.gov (United States)

    Banfi, F.; Barazzetti, L.; Previtali, M.; Roncoroni, F.

    2017-05-01

    Recent developments in Building Information Modelling (BIM) technologies are facilitating the management of historic complex structures using new applications. This paper proposes a generative method combining the morphological and typological aspects of the historic buildings (H-BIM), with a set of monitoring information. This combination of 3D digital survey, parametric modelling and monitoring datasets allows for the development of a system for archiving and visualizing structural health monitoring (SHM) data (Fig. 1). The availability of a BIM database allows one to integrate a different kind of data stored in different ways (e.g. reports, tables, graphs, etc.) with a representation directly connected to the 3D model of the structure with appropriate levels of detail (LoD). Data can be interactively accessed by selecting specific objects of the BIM, i.e. connecting the 3D position of the sensors installed with additional digital documentation. Such innovative BIM objects, which form a new BIM family for SHM, can be then reused in other projects, facilitating data archiving and exploitation of data acquired and processed. The application of advanced modeling techniques allows for the reduction of time and costs of the generation process, and support cooperation between different disciplines using a central workspace. However, it also reveals new challenges for parametric software and exchange formats. The case study presented is the medieval bridge Azzone Visconti in Lecco (Italy), in which multi-temporal vertical movements during load testing were integrated into H-BIM.

  10. HISTORIC BIM: A NEW REPOSITORY FOR STRUCTURAL HEALTH MONITORING

    Directory of Open Access Journals (Sweden)

    F. Banfi

    2017-05-01

    Full Text Available Recent developments in Building Information Modelling (BIM technologies are facilitating the management of historic complex structures using new applications. This paper proposes a generative method combining the morphological and typological aspects of the historic buildings (H-BIM, with a set of monitoring information. This combination of 3D digital survey, parametric modelling and monitoring datasets allows for the development of a system for archiving and visualizing structural health monitoring (SHM data (Fig. 1. The availability of a BIM database allows one to integrate a different kind of data stored in different ways (e.g. reports, tables, graphs, etc. with a representation directly connected to the 3D model of the structure with appropriate levels of detail (LoD. Data can be interactively accessed by selecting specific objects of the BIM, i.e. connecting the 3D position of the sensors installed with additional digital documentation. Such innovative BIM objects, which form a new BIM family for SHM, can be then reused in other projects, facilitating data archiving and exploitation of data acquired and processed. The application of advanced modeling techniques allows for the reduction of time and costs of the generation process, and support cooperation between different disciplines using a central workspace. However, it also reveals new challenges for parametric software and exchange formats. The case study presented is the medieval bridge Azzone Visconti in Lecco (Italy, in which multi-temporal vertical movements during load testing were integrated into H-BIM.

  11. PSYCHE: personalised monitoring systems for care in mental health.

    Science.gov (United States)

    Paradiso, R; Bianchi, A M; Lau, K; Scilingo, E P

    2010-01-01

    One of the areas of great demand for the need of continuous monitoring, patient participation and medical prediction is that of mood disorders, more specifically bipolar disorders. Due to the unpredictable and episodic nature of bipolar disorder, it is necessary to take the traditional standard procedures of mood assessment through the administration of rating scales and questionnaires and integrate this with tangible data found in emerging research on central and peripheral changes in brain function that may be associated to the clinical status and response to treatment throughout the course of bipolar disorder. This paper presents PSYCHE system, a personal, cost-effective, multi-parametric monitoring system based on textile platforms and portable sensing devices for the long term and short term acquisition of data from selected class of patients affected by mood disorders. The acquired data will be processed and analyzed in the established platform that takes into account the Electronic Health Records (EHR) of the patient, a personalized data referee system, as well as medical analysis in order to verify the diagnosis and help in prognosis of the illness. Constant feedback and monitoring will be used to manage the illness, to give patients support, to facilitate interaction between patient and physician as well as to alert professionals in case of patients relapse and depressive or manic episodes income, as the ultimate goal is to identify signal trends indicating detection and prediction of critical events.

  12. Structural health monitoring feature design by genetic programming

    International Nuclear Information System (INIS)

    Harvey, Dustin Y; Todd, Michael D

    2014-01-01

    Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems. (paper)

  13. Passive and Active Sensing Technologies for Structural Health Monitoring

    Science.gov (United States)

    Do, Richard

    A combination of passive and active sensing technologies is proposed as a structural health monitoring solution for several applications. Passive sensing is differentiated from active sensing in that with the former, no energy is intentionally imparted into the structure under test; sensors are deployed in a pure detection mode for collecting data mined for structural health monitoring purposes. In this thesis, passive sensing using embedded fiber Bragg grating optical strain gages was used to detect varying degrees of impact damage using two different classes of features drawn from traditional spectral analysis and auto-regressive time series modeling. The two feature classes were compared in detail through receiver operating curve performance analysis. The passive detection problem was then augmented with an active sensing system using ultrasonic guided waves (UGWs). This thesis considered two main challenges associated with UGW SHM including in-situ wave propagation property determination and thermal corruption of data. Regarding determination of wave propagation properties, of which dispersion characteristics are the most important, a new dispersion curve extraction method called sparse wavenumber analysis (SWA) was experimentally validated. Also, because UGWs are extremely sensitive to ambient temperature changes on the structure, it significantly affects the wave propagation properties by causing large errors in the residual error in the processing of the UGWs from an array. This thesis presented a novel method that compensates for uniform temperature change by considering the magnitude and phase of the signal separately and applying a scalable transformation.

  14. 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.

  15. Health monitoring of pipeline girth weld using empirical mode decomposition

    Science.gov (United States)

    Rezaei, Davood; Taheri, Farid

    2010-05-01

    In the present paper the Hilbert-Huang transform (HHT), as a time-series analysis technique, has been combined with a local diagnostic approach in an effort to identify flaws in pipeline girth welds. This method is based on monitoring the free vibration signals of the pipe at its healthy and flawed states, and processing the signals through the HHT and its associated signal decomposition technique, known as empirical mode decomposition (EMD). The EMD method decomposes the vibration signals into a collection of intrinsic mode functions (IMFs). The deviations in structural integrity, measured from a healthy-state baseline, are subsequently evaluated by two damage sensitive parameters. The first is a damage index, referred to as the EM-EDI, which is established based on an energy comparison of the first or second IMF of the vibration signals, before and after occurrence of damage. The second parameter is the evaluation of the lag in instantaneous phase, a quantity derived from the HHT. In the developed methodologies, the pipe's free vibration is monitored by piezoceramic sensors and a laser Doppler vibrometer. The effectiveness of the proposed techniques is demonstrated through a set of numerical and experimental studies on a steel pipe with a mid-span girth weld, for both pressurized and nonpressurized conditions. To simulate a crack, a narrow notch is cut on one side of the girth weld. Several damage scenarios, including notches of different depths and at various locations on the pipe, are investigated. Results from both numerical and experimental studies reveal that in all damage cases the sensor located at the notch vicinity could successfully detect the notch and qualitatively predict its severity. The effect of internal pressure on the damage identification method is also monitored. Overall, the results are encouraging and promise the effectiveness of the proposed approaches as inexpensive systems for structural health monitoring purposes.

  16. Epidemiologic monitoring of possible health reactions of waste water reuse

    Energy Technology Data Exchange (ETDEWEB)

    Frerichs, R.R.

    1984-01-27

    The possible health effects of consuming ground water partially recharged with recycled waste water were monitored in a long-term study of residents of several communities in eastern Los Angeles County, California. In three phases of ecologic studies, health measures were compared among residents of two recycled water areas (high and low concentration) and two control areas. Included were measures of mortality, reportable illnesses, adverse birth outcomes, and incident cases of cancer. While significant differences were noted among the four study areas when comparing several health outcomes, none of the differences were in a direction to suggest a dose-response relationship between reclaimed water consumption and disease. To supplement findings of the ecologic studies, a household survey was conducted of approximately 2,500 women, half residing in the high recycled water area and half in the control area. The survey provided increased information on reproductive outcomes and on excess effects after controlling for important potential confounding factors such as cigarette use and alcohol consumption. The results of both the ecologic studies and the household survey provide no indication that recycled water has a noticeable harmful effect on the health of a population exposed for nearly two decades.

  17. 75 FR 52504 - Notice of Request for Approval of an Information Collection; National Animal Health Monitoring...

    Science.gov (United States)

    2010-08-26

    ...; National Animal Health Monitoring System; Dairy Heifer Raiser 2010 Study AGENCY: Animal and Plant Health... Service's intention to initiate an information collection to support the National Animal Health Monitoring... Warnken, Management and Program Analyst, Centers for Epidemiology and Animal Health, VS, APHIS, 2150...

  18. Health Monitoring System Based on Intra-Body Communication

    Science.gov (United States)

    Razak, A. H. A.; Ibrahim, I. W.; Ayub, A. H.; Amri, M. F.; Hamzi, M. H.; Halim, A. K.; Ahmad, A.; Junid, S. A. M. Al

    2015-11-01

    This paper presents a model of a Body Area Network (BAN) health monitoring system based on Intra-Body Communication. Intra-body Communication (IBC) is a communication technique that uses the human body as a medium for electrical signal communication. One of the visions in the health care industry is to provide autonomous and continuous self and the remote health monitoring system. This can be achieved via BAN, LAN and WAN integration. The BAN technology itself consists of short range data communication modules, sensors, controller and actuators. The information can be transmitted to the LAN and WAN via the RF technology such as Bluetooth, ZigBee and ANT. Although the implementations of RF communication have been successful, there are still limitations in term of power consumption, battery lifetime, interferences and signal attenuations. One of the solutions for Medical Body Area Network (MBANs) to overcome these issues is by using an IBC technique because it can operate at lower frequencies and power consumption compared to the existing techniques. The first objective is to design the IBC's transmitter and receiver modules using the off the shelf components. The specifications of the modules such as frequency, data rate, modulation and demodulation coding system were defined. The individual module were designed and tested separately. The modules was integrated as an IBC system and tested for functionality then was implemented on PCB. Next objective is to model and implement the digital parts of the transmitter and receiver modules on the Altera's FPGA board. The digital blocks were interfaced with the FPGA's on board modules and the discrete components. The signals that have been received from the transmitter were converted into a proper waveform and it can be viewed via external devices such as oscilloscope and Labview. The signals such as heartbeats or pulses can also be displayed on LCD. In conclusion, the IBC project presents medical health monitoring model

  19. Health technology assessment to optimize health technology utilization: using implementation initiatives and monitoring processes.

    Science.gov (United States)

    Frønsdal, Katrine B; Facey, Karen; Klemp, Marianne; Norderhaug, Inger Natvig; Mørland, Berit; Røttingen, John-Arne

    2010-07-01

    The way in which a health technology is used in any particular health system depends on the decisions and actions of a variety of stakeholders, the local culture, and context. In 2009, the HTAi Policy Forum considered how health technology assessment (HTA) could be improved to optimize the use of technologies (in terms of uptake, change in use, or disinvestment) in such complex systems. In scoping, it was agreed to focus on initiatives to implement evidence-based guidance and monitoring activities. A review identified systematic reviews of implementation initiatives and monitoring activities. A two-day deliberative workshop was held to discuss key papers, members' experiences, and collectively address key questions. This consensus paper was developed by email and finalized at a postworkshop meeting. Evidence suggests that the impact and use of HTA could be increased by ensuring timely delivery of relevant reports to clearly determined policy receptor (decision-making) points. To achieve this, the breadth of assessment, implementation initiatives such as incentives and targeted, intelligent dissemination of HTA result, needs to be considered. HTA stakeholders undertake a variety of monitoring activities, which could inform optimal use of a technology. However, the quality of these data varies and is often not submitted to an HTA. Monitoring data should be sufficiently robust so that they can be used in HTA to inform optimal use of technology. Evidence-based implementation initiatives should be developed for HTA, to better inform decision makers at all levels in a health system about the optimal use of technology.

  20. Machine medical ethics

    CERN Document Server

    Pontier, Matthijs

    2015-01-01

    The essays in this book, written by researchers from both humanities and sciences, describe various theoretical and experimental approaches to adding medical ethics to a machine in medical settings. Medical machines are in close proximity with human beings, and getting closer: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. In such contexts, machines are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for e...

  1. The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors.

    Science.gov (United States)

    Dobkin, Bruce H; Dorsch, Andrew

    2011-01-01

    Mobile health tools that enable clinicians and researchers to monitor the type, quantity, and quality of everyday activities of patients and trial participants have long been needed to improve daily care, design more clinically meaningful randomized trials of interventions, and establish cost-effective, evidence-based practices. Inexpensive, unobtrusive wireless sensors, including accelerometers, gyroscopes, and pressure-sensitive textiles, combined with Internet-based communications and machine-learning algorithms trained to recognize upper- and lower-extremity movements, have begun to fulfill this need. Continuous data from ankle triaxial accelerometers, for example, can be transmitted from the home and community via WiFi or a smartphone to a remote data analysis server. Reports can include the walking speed and duration of every bout of ambulation, spatiotemporal symmetries between the legs, and the type, duration, and energy used during exercise. For daily care, this readily accessible flow of real-world information allows clinicians to monitor the amount and quality of exercise for risk factor management and compliance in the practice of skills. Feedback may motivate better self-management as well as serve home-based rehabilitation efforts. Monitoring patients with chronic diseases and after hospitalization or the start of new medications for a decline in daily activity may help detect medical complications before rehospitalization becomes necessary. For clinical trials, repeated laboratory-quality assessments of key activities in the community, rather than by clinic testing, self-report, and ordinal scales, may reduce the cost and burden of travel, improve recruitment and retention, and capture more reliable, valid, and responsive ratio-scaled outcome measures that are not mere surrogates for changes in daily impairment, disability, and functioning.

  2. Scoping review: national monitoring frameworks for social determinants of health and health equity

    Directory of Open Access Journals (Sweden)

    Leo Pedrana

    2016-02-01

    Full Text Available Background: The strategic importance of monitoring social determinants of health (SDH and health equity and inequity has been a central focus in global discussions around the 2011 Rio Political Declaration on SDH and the Millennium Development Goals. This study is part of the World Health Organization (WHO equity-oriented analysis of linkages between health and other sectors (EQuAL project, which aims to define a framework for monitoring SDH and health equity. Objectives: This review provides a global summary and analysis of the domains and indicators that have been used in recent studies covering the SDH. These studies are considered here within the context of indicators proposed by the WHO EQuAL project. The objectives are as follows: to describe the range of international and national studies and the types of indicators most frequently used; report how they are used in causal explanation of the SDH; and identify key priorities and challenges reported in current research for national monitoring of the SDH. Design: We conducted a scoping review of published SDH studies in the PubMed® database to obtain evidence of socio-economic indicators. We evaluated, selected, and extracted data from national scale studies published from 2004 to 2014. The research included papers published in English, Italian, French, Portuguese, and Spanish. Results: The final sample consisted of 96 articles. SDH monitoring is well reported in the scientific literature independent of the economic level of the country and magnitude of deprivation in population groups. The research methods were mostly quantitative and many papers used multilevel and multivariable statistical analyses and indexes to measure health inequalities and SDH. In addition to the usual economic indicators, a high number of socio-economic indicators were used. The indicators covered a broad range of social dimensions, which were given consideration within and across different social groups. Many

  3. Scoping review: national monitoring frameworks for social determinants of health and health equity.

    Science.gov (United States)

    Pedrana, Leo; Pamponet, Marina; Walker, Ruth; Costa, Federico; Rasella, Davide

    2016-01-01

    The strategic importance of monitoring social determinants of health (SDH) and health equity and inequity has been a central focus in global discussions around the 2011 Rio Political Declaration on SDH and the Millennium Development Goals. This study is part of the World Health Organization (WHO) equity-oriented analysis of linkages between health and other sectors (EQuAL) project, which aims to define a framework for monitoring SDH and health equity. This review provides a global summary and analysis of the domains and indicators that have been used in recent studies covering the SDH. These studies are considered here within the context of indicators proposed by the WHO EQuAL project. The objectives are as follows: to describe the range of international and national studies and the types of indicators most frequently used; report how they are used in causal explanation of the SDH; and identify key priorities and challenges reported in current research for national monitoring of the SDH. We conducted a scoping review of published SDH studies in the PubMed(®) database to obtain evidence of socio-economic indicators. We evaluated, selected, and extracted data from national scale studies published from 2004 to 2014. The research included papers published in English, Italian, French, Portuguese, and Spanish. The final sample consisted of 96 articles. SDH monitoring is well reported in the scientific literature independent of the economic level of the country and magnitude of deprivation in population groups. The research methods were mostly quantitative and many papers used multilevel and multivariable statistical analyses and indexes to measure health inequalities and SDH. In addition to the usual economic indicators, a high number of socio-economic indicators were used. The indicators covered a broad range of social dimensions, which were given consideration within and across different social groups. Many indicators included in the WHO EQuAL framework were not

  4. Detection of correct and incorrect measurements in real-time continuous glucose monitoring systems by applying a postprocessing support vector machine.

    Science.gov (United States)

    Leal, Yenny; Gonzalez-Abril, Luis; Lorencio, Carol; Bondia, Jorge; Vehi, Josep

    2013-07-01

    Support vector machines (SVMs) are an attractive option for detecting correct and incorrect measurements in real-time continuous glucose monitoring systems (RTCGMSs), because their learning mechanism can introduce a postprocessing strategy for imbalanced datasets. The proposed SVM considers the geometric mean to obtain a more balanced performance between sensitivity and specificity. To test this approach, 23 critically ill patients receiving insulin therapy were monitored over 72 h using an RTCGMS, and a dataset of 537 samples, classified according to International Standards Organization (ISO) criteria (372 correct and 165 incorrect measurements), was obtained. The results obtained were promising for patients with septic shock or with sepsis, for which the proposed system can be considered as reliable. However, this approach cannot be considered suitable for patients without sepsis.

  5. The influence of health system organizational structure and culture on integration of health services: the example of HIV service monitoring in South Africa.

    Science.gov (United States)

    Kawonga, Mary; Blaauw, Duane; Fonn, Sharon

    2016-11-01

    Administrative integration of disease control programmes (DCPs) within the district health system has been a health sector reform priority in South Africa for two decades. The reforms entail district managers assuming authority for the planning and monitoring of DCPs in districts, with DCP managers providing specialist support. There has been little progress in achieving this, and a dearth of research exploring why. Using a case study of HIV programme monitoring and evaluation (M&E), this article explores whether South Africa's health system is configured to support administrative integration. The article draws on data from document reviews and interviews with 54 programme and district managers in two of nine provinces, exploring their respective roles in decision-making regarding HIV M&E system design and in using HIV data for monitoring uptake of HIV interventions in districts. Using Mintzberg's configurations framework, we describe three organizational parameters: (a) extent of centralization (whether district managers play a role in decisions regarding the design of the HIV M&E system); (b) key part of the organization (extent to which sub-national programme managers vs district managers play the central role in HIV monitoring in districts); and (c) coordination mechanisms used (whether highly formalized and rules-based or more output-based to promote agency). We find that the health system can be characterized as Mintzberg's machine bureaucracy. It is centralized and highly formalized with structures, management styles and practices that promote programme managers as lead role players in the monitoring of HIV interventions within districts. This undermines policy objectives of district managers assuming this leadership role. Our study enhances the understanding of organizational factors that may limit the success of administrative integration reforms and suggests interventions that may mitigate this. © The Author 2016. Published by Oxford University Press in

  6. ChemANDTM - a system health monitor for plant chemistry

    International Nuclear Information System (INIS)

    Turner, C.W.; Mitchel, G.R.; Balakrishnan, P.V.; Tosello, G.

    1999-07-01

    Effective management of plant systems throughout their lifetime requires much more than data acquisition and display - it requires that the plant's system health be continually monitored and managed. AECL has developed a System Health Monitor called ChemAND for CANDU plant chemistry. ChemAND, a Chemistry ANalysis and Diagnostic system, monitors key chemistry parameters in the heat transport system, moderator-cover gas, annulus gas, and the steam cycle during full-power operation and feeds these parameters to models that calculate the effect of current plant operating conditions on the present and future health of the system. Chemistry data from each of the systems are extracted on a regular basis from the plant's Historical Data Server and are sorted according to function, e.g., indicators for condenser in-leakage, air in-leakage, heavy water leakage into the annulus gas, fuel failure, etc. Each parameter is conveniently displayed and is trended along with its alarm limits. ChemAND currently has two analytical models developed for the balance-of-plant. CHEMSOLV calculates crevice chemistry conditions in the steam generator (SG) from either the SG blowdown chemistry conditions or from a simulated condenser leak. This information will be used by operations personnel to evaluate the potential for SG tube corrosion in the crevice region. CHEMSOLV also calculates chemistry conditions throughout the steam-cycle system, as determined by the transport of volatile species such as ammonia, hydrazine, morpholine, and oxygen. A second model, SLUDGE, calculates the deposit loading in the SG as a function of time, based on concentrations of corrosion product in the final feedwater and plant operating conditions. Operations personnel can use this information to predict where to inspect and when to clean. In a future development, SLUDGE will track deposit loading arising from start-up crud bursts and will be used in conjunction with the thermohydraulics code, THIRST, to predict

  7. Inspection of Piezoceramic Transducers Used for Structural Health Monitoring

    Directory of Open Access Journals (Sweden)

    Inka Mueller

    2017-01-01

    Full Text Available The use of piezoelectric wafer active sensors (PWAS for structural health monitoring (SHM purposes is state of the art for acousto-ultrasonic-based methods. For system reliability, detailed information about the PWAS itself is necessary. This paper gives an overview on frequent PWAS faults and presents the effects of these faults on the wave propagation, used for active acousto-ultrasonics-based SHM. The analysis of the wave field is based on velocity measurements using a laser Doppler vibrometer (LDV. New and established methods of PWAS inspection are explained in detail, listing advantages and disadvantages. The electro-mechanical impedance spectrum as basis for these methods is discussed for different sensor faults. This way this contribution focuses on a detailed analysis of PWAS and the need of their inspection for an increased reliability of SHM systems.

  8. Health monitoring technology for alumina-fiber-reinforced plastic

    International Nuclear Information System (INIS)

    Aoyama, Hiroshi; Watanabe, Hiroyuki; Terai, Motoaki

    1998-01-01

    Formally, we developed new load-support systems that consists of a biconical, alumina-fiber-reinforced plastic (ERP) structure for the superconducting magnet. Safe operation of the superconducting magnet will be jeopardized if the mechanical condition of the load-support system begins to degrade. One of the factors that evaluate the soundness of the superconducting magnet is the stiffness of the load-support system. Here, it is important to know the relation between the degradation of the stiffness and the growth of defects. For this purpose, firstly, a fatigue test of the load-support system was carried out, and the various defects (matrix cracking and delamination of FRP laminates) were observed during this fatigue testing. Finally, we proposed the application of two non-destructive-evaluation (NDE) methods for the health monitoring of alumina/epoxy load-support systems. (author)

  9. Biological monitoring of toxic metals - steel workers respiratory health survey

    International Nuclear Information System (INIS)

    Pinheiro, T.; Almeida, A. Bugalho de; Alves, L.; Freitas, M.C.; Moniz, D.; Alvarez, E.; Monteiro, P.; Reis, M.

    1999-01-01

    The aim of this work is to search for respiratory system aggressors to which workers are submitted in their labouring activity. Workers from one sector of a steel plant in Portugal, Siderurgia Nacional (SN), were selected according to the number of years of exposure and labouring characteristics. The work reports on blood elemental content alterations and lung function tests to determine an eventual bronchial hyper-reactivity. Aerosol samples collected permit an estimate of indoor air quality and airborne particulate matter characterisation to further check whether the elemental associations and alterations found in blood may derive from exposure. Blood and aerosol elemental composition was determined by PIXE and INAA. Respiratory affections were verified for 24% of the workers monitored. There are indications that the occurrence of affections can be associated with the total working years. The influence of long-term exposure, health status parameters, and lifestyle factors in blood elemental variations found was investigated

  10. Fiber Optic Thermal Health Monitoring of Aerospace Structures and Materials

    Science.gov (United States)

    Wu, Meng-Chou; Winfree, William P.; Allison, Sidney G.

    2009-01-01

    A new technique is presented for thermographic detection of flaws in materials and structures by performing temperature measurements with fiber Bragg gratings. Individual optical fibers with multiple Bragg gratings employed as surface temperature sensors were bonded to the surfaces of structures with subsurface defects or thickness variations. Both during and following the application of a thermal heat flux to the surface, the individual Bragg grating sensors measured the temporal and spatial temperature variations. The investigated structures included a 10-ply composite specimen with subsurface delaminations of various sizes and depths. The data obtained from grating sensors were further analyzed with thermal modeling to reveal particular characteristics of the interested areas. These results were found to be consistent with those from conventional thermography techniques. Limitations of the technique were investigated using both experimental and numerical simulation techniques. Methods for performing in-situ structural health monitoring are discussed.

  11. Assessment of an Anomaly Detector for Jet Engine Health Monitoring

    Directory of Open Access Journals (Sweden)

    Sebastien Borguet

    2011-01-01

    Full Text Available The goal of module performance analysis is to reliably assess the health of the main components of an aircraft engine. A predictive maintenance strategy can leverage this information to increase operability and safety as well as to reduce costs. Degradation undergone by an engine can be divided into gradual deterioration and accidental events. Kalman filters have proven very efficient at tracking progressive deterioration but are poor performers in the face of abrupt events. Adaptive estimation is considered as an appropriate solution to this deficiency. This paper reports the evaluation of the detection capability of an adaptive diagnosis tool on the basis of simulated scenarios that may be encountered during the operation of a commercial turbofan engine. The diagnosis tool combines a Kalman filter and a secondary system that monitors the residuals. This auxiliary component implements a generalised likelihood ratio test in order to detect abrupt events.

  12. Review on pressure sensors for structural health monitoring

    Science.gov (United States)

    Sikarwar, Samiksha; Satyendra; Singh, Shakti; Yadav, Bal Chandra

    2017-12-01

    This paper reports the state of art in a variety of pressure and the detailed study of various matrix based pressure sensors. The performances of the bridges, buildings, etc. are threatened by earthquakes, material degradations, and other environmental effects. Structural health monitoring (SHM) is crucial to protect the people and also for assets planning. This study is a contribution in developing the knowledge about self-sensing smart materials and structures for the construction industry. It deals with the study of self-sensing as well as mechanical and electrical properties of different matrices based on pressure sensors. The relationships among the compression, tensile strain, and crack length with electrical resistance change are also reviewed.

  13. Time-Frequency Methods for Structural Health Monitoring

    Directory of Open Access Journals (Sweden)

    Alexander L. Pyayt

    2014-03-01

    Full Text Available Detection of early warning signals for the imminent failure of large and complex engineered structures is a daunting challenge with many open research questions. In this paper we report on novel ways to perform Structural Health Monitoring (SHM of flood protection systems (levees, earthen dikes and concrete dams using sensor data. We present a robust data-driven anomaly detection method that combines time-frequency feature extraction, using wavelet analysis and phase shift, with one-sided classification techniques to identify the onset of failure anomalies in real-time sensor measurements. The methodology has been successfully tested at three operational levees. We detected a dam leakage in the retaining dam (Germany and “strange” behaviour of sensors installed in a Boston levee (UK and a Rhine levee (Germany.

  14. Protocol of specific health monitoring: ionizing radiation, 11 years later

    International Nuclear Information System (INIS)

    Castillejo Puertas, F. M.

    2016-01-01

    Since the approval on November 11 t h 2003 of the Protocol of Specific Health Monitoring for Workers Exposed to Ionizing Radiation a study has been carried out to discover its effectiveness. These areas were examined: the daily practice od accupational medicine and, in particular, its specific task in the application of the different clinical/labour criteria for workers exposed to ionizing radiation or at risk of radioactive contamination; the degree of its uses as well as the updates and improvements. For that purpose, a descriptive bibliographic revision has been used for the last 11 years. The results revealed the lack of updates of the Protocol as well as the few usable objective criteria, when the clinical/labour aptitudes are reflected upon. (Author)

  15. Motion Artifact Quantification and Sensor Fusion for Unobtrusive Health Monitoring

    Directory of Open Access Journals (Sweden)

    Christoph Hoog Antink

    2017-12-01

    Full Text Available Sensors integrated into objects of everyday life potentially allow unobtrusive health monitoring at home. However, since the coupling of sensors and subject is not as well-defined as compared to a clinical setting, the signal quality is much more variable and can be disturbed significantly by motion artifacts. One way of tackling this challenge is the combined evaluation of multiple channels via sensor fusion. For robust and accurate sensor fusion, analyzing the influence of motion on different modalities is crucial. In this work, a multimodal sensor setup integrated into an armchair is presented that combines capacitively coupled electrocardiography, reflective photoplethysmography, two high-frequency impedance sensors and two types of ballistocardiography sensors. To quantify motion artifacts, a motion protocol performed by healthy volunteers is recorded with a motion capture system, and reference sensors perform cardiorespiratory monitoring. The shape-based signal-to-noise ratio SNR S is introduced and used to quantify the effect on motion on different sensing modalities. Based on this analysis, an optimal combination of sensors and fusion methodology is developed and evaluated. Using the proposed approach, beat-to-beat heart-rate is estimated with a coverage of 99.5% and a mean absolute error of 7.9 ms on 425 min of data from seven volunteers in a proof-of-concept measurement scenario.

  16. Motion Artifact Quantification and Sensor Fusion for Unobtrusive Health Monitoring

    Science.gov (United States)

    Hoog Antink, Christoph; Schulz, Florian; Walter, Marian

    2017-01-01

    Sensors integrated into objects of everyday life potentially allow unobtrusive health monitoring at home. However, since the coupling of sensors and subject is not as well-defined as compared to a clinical setting, the signal quality is much more variable and can be disturbed significantly by motion artifacts. One way of tackling this challenge is the combined evaluation of multiple channels via sensor fusion. For robust and accurate sensor fusion, analyzing the influence of motion on different modalities is crucial. In this work, a multimodal sensor setup integrated into an armchair is presented that combines capacitively coupled electrocardiography, reflective photoplethysmography, two high-frequency impedance sensors and two types of ballistocardiography sensors. To quantify motion artifacts, a motion protocol performed by healthy volunteers is recorded with a motion capture system, and reference sensors perform cardiorespiratory monitoring. The shape-based signal-to-noise ratio SNRS is introduced and used to quantify the effect on motion on different sensing modalities. Based on this analysis, an optimal combination of sensors and fusion methodology is developed and evaluated. Using the proposed approach, beat-to-beat heart-rate is estimated with a coverage of 99.5% and a mean absolute error of 7.9 ms on 425 min of data from seven volunteers in a proof-of-concept measurement scenario. PMID:29295594

  17. Motion Artifact Quantification and Sensor Fusion for Unobtrusive Health Monitoring.

    Science.gov (United States)

    Hoog Antink, Christoph; Schulz, Florian; Leonhardt, Steffen; Walter, Marian

    2017-12-25

    Sensors integrated into objects of everyday life potentially allow unobtrusive health monitoring at home. However, since the coupling of sensors and subject is not as well-defined as compared to a clinical setting, the signal quality is much more variable and can be disturbed significantly by motion artifacts. One way of tackling this challenge is the combined evaluation of multiple channels via sensor fusion. For robust and accurate sensor fusion, analyzing the influence of motion on different modalities is crucial. In this work, a multimodal sensor setup integrated into an armchair is presented that combines capacitively coupled electrocardiography, reflective photoplethysmography, two high-frequency impedance sensors and two types of ballistocardiography sensors. To quantify motion artifacts, a motion protocol performed by healthy volunteers is recorded with a motion capture system, and reference sensors perform cardiorespiratory monitoring. The shape-based signal-to-noise ratio SNR S is introduced and used to quantify the effect on motion on different sensing modalities. Based on this analysis, an optimal combination of sensors and fusion methodology is developed and evaluated. Using the proposed approach, beat-to-beat heart-rate is estimated with a coverage of 99.5% and a mean absolute error of 7.9 ms on 425 min of data from seven volunteers in a proof-of-concept measurement scenario.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2000-04-01

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

  19. Radiation health consequences for astronauts: mechanisms, monitoring and prevention

    Science.gov (United States)

    Neyfakh, E.

    During space flights crews are exposed chronically to uneven irradiation of enhanced bioefficiency following with significant elevation for chromosomal aberrations as minimum. To protect in space rationally monitoring and preventing of health radiogenic individual primary consequences for astronauts are of high importance. Majority of Chernobyl-touched population has some common etiologic radiogenic mechanisms and radioloads with astronauts ones during long-term missions and former is able to be used well as the close ground-level model. Primary radiogenic deviations. Two radiogenic pathologies as lipoperoxic ( LP ) stress with coupled deficits for essential bioantioxidants ( BAO ) were typical for chronic low-dose Chernobyl-touched contingents. When BAO expenditure had led to their subnormal levels, radiogenic free radical chain -b ranched LP processes occurred in vivo hyperbolically. Catabolites and their free radicals of the abnormal LP cascade are known to be toxic, mutagenic / carcinogenic and teratogenic factors as such, as they are for retinol and tocopherol deficiencies. Both coupled pathogenic factors interrelated synergistically. Simultaneous dysbalances for LP and / or BAO systems were evaluated as the cause and markers for metabolic disregulations. Human LP stress was proved to be the most radiosensible known marker to mo nitor least invasively of blood microsamples in a ground lab via the developed PC Program. But for capsule conditions the best approach is assumed to be LP monitoring via skin ultraweak green-blue chemiluminescence ( CL ) caused by recombination of peroxyl radicals. CL from surfaces of organs was embedded first ( E. Neyfakh, 1964 - 71 ) to reflect their internal LP velocities in vivo and it is the non-invasive on-line simple method of the highest sensitivity, supplying with data transmissible to the ground directly. Related deviations. a) Radiogenic hypermutagenesis: LP catabolites and their free radicals are responsible for direct DNA

  20. Electromagnetic fields and health impact: measurements, monitoring and environmental indicators

    International Nuclear Information System (INIS)

    Lubritto, C.; Vetromile, C.; Petraglia, A.; Racioppoli, M.; D'Onofrio, A.

    2008-01-01

    Full text: During the last 10 years there has been a remarkable growth of the attention for problems related to the electromagnetic pollution, motivated by the alert connected to potential risk for the health of persons and due to the increasing diffusion of Bats for mobile telecommunication as EMF sources. Many projects are being realized about the environmental and health impact of electromagnetic field and an important social role is played by specific actions to minimize the risk perception of the population. This study aims to find an innovative approach to these problems through the use of a system of continuous time monitoring of the electromagnetic fields and the individuation of appropriate environmental indicators. The proposed system monitors the electromagnetic fields continuously over time, and is already operating in many southern Italian cities. It works in a very efficient way as a mean for: a) Info to the citizens, thanks to diffusion of daily collected data on Internet Web; b) Control for local administrations and Authorities, due to capability of the system itself to alert when measured values exceed the limits reported by the Italian laws; c) Planning, for the implementation of : 1) New procedures agreed among local environmental control agency, local administrations and mobile Companies for network planning and management of alarm situations; 2) New local guidelines documents concerning the installation and operation of telecommunications apparatus. Moreover, starting from the general principles of the Strategic Environmental Evaluation (VAS), the environmental impacts of EMS field is studied. Based on the model DPSIR (Drivers, Pressure, State, Impacts, Responses), 12 environmental indicators have been chosen providing an immediate and understandable tool to obtain very important information on electromagnetic pollution generated by radio-telecommunication systems. The selected environmental indicators have been applied to 11 cities of the

  1. Monitoring of health care personnel employee and occupational health immunization program practices in the United States.

    Science.gov (United States)

    Carrico, Ruth M; Sorrells, Nikka; Westhusing, Kelly; Wiemken, Timothy

    2014-01-01

    Recent studies have identified concerns with various elements of health care personnel immunization programs, including the handling and management of the vaccine. The purpose of this study was to assess monitoring processes that support evaluation of the care of vaccines in health care settings. An 11-question survey instrument was developed for use in scripted telephone surveys. State health departments in all 50 states in the United States and the District of Columbia were the target audience for the surveys. Data from a total of 47 states were obtained and analyzed. No states reported an existing monitoring process for evaluation of health care personnel immunization programs in their states. Our assessment indicates that vaccine evaluation processes for health care facilities are rare to nonexistent in the United States. Identifying existing practice gaps and resultant opportunities for improvements may be an important safety initiative that protects patients and health care personnel. Copyright © 2014 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.

  2. Health Monitoring Survey of Bell 412EP Transmissions

    Science.gov (United States)

    Tucker, Brian E.; Dempsey, Paula J.

    2016-01-01

    Health and usage monitoring systems (HUMS) use vibration-based Condition Indicators (CI) to assess the health of helicopter powertrain components. A fault is detected when a CI exceeds its threshold value. The effectiveness of fault detection can be judged on the basis of assessing the condition of actual components from fleet aircraft. The Bell 412 HUMS-equipped helicopter is chosen for such an evaluation. A sample of 20 aircraft included 12 aircraft with confirmed transmission and gearbox faults (detected by CIs) and eight aircraft with no known faults. The associated CI data is classified into "healthy" and "faulted" populations based on actual condition and these populations are compared against their CI thresholds to quantify the probability of false alarm and the probability of missed detection. Receiver Operator Characteristic analysis is used to optimize thresholds. Based on the results of the analysis, shortcomings in the classification method are identified for slow-moving CI trends. Recommendations for improving classification using time-dependent receiver-operator characteristic methods are put forth. Finally, lessons learned regarding OEM-operator communication are presented.

  3. Distributed Health Monitoring System for Reusable Liquid Rocket Engines

    Science.gov (United States)

    Lin, C. F.; Figueroa, F.; Politopoulos, T.; Oonk, S.

    2009-01-01

    The ability to correctly detect and identify any possible failure in the systems, subsystems, or sensors within a reusable liquid rocket engine is a major goal at NASA John C. Stennis Space Center (SSC). A health management (HM) system is required to provide an on-ground operation crew with an integrated awareness of the condition of every element of interest by determining anomalies, examining their causes, and making predictive statements. However, the complexity associated with relevant systems, and the large amount of data typically necessary for proper interpretation and analysis, presents difficulties in implementing complete failure detection, identification, and prognostics (FDI&P). As such, this paper presents a Distributed Health Monitoring System for Reusable Liquid Rocket Engines as a solution to these problems through the use of highly intelligent algorithms for real-time FDI&P, and efficient and embedded processing at multiple levels. The end result is the ability to successfully incorporate a comprehensive HM platform despite the complexity of the systems under consideration.

  4. Prognostics and Health Monitoring: Application to Electric Vehicles

    Science.gov (United States)

    Kulkarni, Chetan S.

    2017-01-01

    As more and more autonomous electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of remaining useful life of the systemssubsystems, specifically the electrical powertrain. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle.Our research approach is to develop a system level health monitoring safety indicator either to the pilotautopilot for the electric vehicles which runs estimation and prediction algorithms to estimate remaining useful life of the vehicle e.g. determine state-of-charge in batteries. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

  5. Intelligent Wireless Sensor Networks for System Health Monitoring

    Science.gov (United States)

    Alena, Rick

    2011-01-01

    Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network (PAN) standard are finding increasing use in the home automation and emerging smart energy markets. The network and application layers, based on the ZigBee 2007 Standard, provide a convenient framework for component-based software that supports customer solutions from multiple vendors. WSNs provide the inherent fault tolerance required for aerospace applications. The Discovery and Systems Health Group at NASA Ames Research Center has been developing WSN technology for use aboard aircraft and spacecraft for System Health Monitoring of structures and life support systems using funding from the NASA Engineering and Safety Center and Exploration Technology Development and Demonstration Program. This technology provides key advantages for low-power, low-cost ancillary sensing systems particularly across pressure interfaces and in areas where it is difficult to run wires. Intelligence for sensor networks could be defined as the capability of forming dynamic sensor networks, allowing high-level application software to identify and address any sensor that joined the network without the use of any centralized database defining the sensors characteristics. The IEEE 1451 Standard defines methods for the management of intelligent sensor systems and the IEEE 1451.4 section defines Transducer Electronic Datasheets (TEDS), which contain key information regarding the sensor characteristics such as name, description, serial number, calibration information and user information such as location within a vehicle. By locating the TEDS information on the wireless sensor itself and enabling access to this information base from the application software, the application can identify the sensor unambiguously and interpret and present the sensor data stream without reference to any other information. The application software is able to read the status of each sensor module, responding in real-time to changes of

  6. Ensembles of novelty detection classifiers for structural health monitoring using guided waves

    Science.gov (United States)

    Dib, Gerges; Karpenko, Oleksii; Koricho, Ermias; Khomenko, Anton; Haq, Mahmoodul; Udpa, Lalita

    2018-01-01

    Guided wave structural health monitoring uses sparse sensor networks embedded in sophisticated structures for defect detection and characterization. The biggest challenge of those sensor networks is developing robust techniques for reliable damage detection under changing environmental and operating conditions (EOC). To address this challenge, we develop a novelty classifier for damage detection based on one class support vector machines. We identify appropriate features for damage detection and introduce a feature aggregation method which quadratically increases the number of available training observations. We adopt a two-level voting scheme by using an ensemble of classifiers and predictions. Each classifier is trained on a different segment of the guided wave signal, and each classifier makes an ensemble of predictions based on a single observation. Using this approach, the classifier can be trained using a small number of baseline signals. We study the performance using Monte-Carlo simulations of an analytical model and data from impact damage experiments on a glass fiber composite plate. We also demonstrate the classifier performance using two types of baseline signals: fixed and rolling baseline training set. The former requires prior knowledge of baseline signals from all EOC, while the latter does not and leverages the fact that EOC vary slowly over time and can be modeled as a Gaussian process.

  7. Hybrid Nanostructured Textile Bioelectrode for Unobtrusive Health Monitoring

    Science.gov (United States)

    Rai, Pratyush

    Coronary heart disease, cardiovascular diseases and strokes are the leading causes of mortality in United States of America. Timely point-of-care health diagnostics and therapeutics for person suffering from these diseases can save thousands of lives. However, lack of accessible minimally intrusive health monitoring systems makes timely diagnosis difficult and sometimes impossible. To remedy this problem, a textile based nano-bio-sensor was developed and evaluated in this research. The sensor was made of novel array of vertically standing nanostructures that are conductive nano-fibers projecting from a conductive fabric. These sensor electrodes were tested for the quality of electrical contact that they made with the skin based on the fundamental skin impedance model and electromagnetic theory. The hybrid nanostructured dry electrodes provided large surface area and better contact with skin that improved electrode sensitivity and reduced the effect of changing skin properties, which are the problems usually faced by conventional dry textile electrodes. The dry electrodes can only register strong physiological signals because of high background noise levels, thus limiting the use of existing dry electrodes to heart rate measurement and respiration. Therefore, dry electrode systems cannot be used for recording complete ECG waveform, EEG or measurement of bioimpedance. Because of their improved sensitivity these hybrid nanostructured dry electrodes can be applied to measurement of ECG and bioimpedance with very low baseline noise. These textile based electrodes can be seamlessly integrated into garments of daily use such as vests and bra. In combination with embedded wireless network device that can communicate with smart phone, laptop or GPRS, they can function as wearable wireless health diagnostic systems.

  8. Policy based Agents in Wireless Body Sensor Mesh Networks for Patient Health Monitoring

    OpenAIRE

    Kevin Miller; Suresh Sankaranarayanan

    2009-01-01

    There is presently considerable research interest in using wireless and mobile technologies in patient health monitoring particularly in hospitals and nursing homes. For health monitoring,, an intelligent agent based hierarchical architecture has already been published by one of the authors of this paper. Also, the technique of monitoring and notifying the health of patients using an intelligent agent, to the concerned hospital personnel, has also been proposed. We now present the details of ...

  9. Synergistic combination of systems for structural health monitoring and earthquake early warning for structural health prognosis and diagnosis

    Science.gov (United States)

    Wu, Stephen; Beck, James L.

    2012-04-01

    Earthquake early warning (EEW) systems are currently operating nationwide in Japan and are in beta-testing in California. Such a system detects an earthquake initiation using online signals from a seismic sensor network and broadcasts a warning of the predicted location and magnitude a few seconds to a minute or so before an earthquake hits a site. Such a system can be used synergistically with installed structural health monitoring (SHM) systems to enhance pre-event prognosis and post-event diagnosis of structural health. For pre-event prognosis, the EEW system information can be used to make probabilistic predictions of the anticipated damage to a structure using seismic loss estimation methodologies from performance-based earthquake engineering. These predictions can support decision-making regarding the activation of appropriate mitigation systems, such as stopping traffic from entering a bridge that has a predicted high probability of damage. Since the time between warning and arrival of the strong shaking is very short, probabilistic predictions must be rapidly calculated and the decision making automated for the mitigation actions. For post-event diagnosis, the SHM sensor data can be used in Bayesian updating of the probabilistic damage predictions with the EEW predictions as a prior. Appropriate Bayesian methods for SHM have been published. In this paper, we use pre-trained surrogate models (or emulators) based on machine learning methods to make fast damage and loss predictions that are then used in a cost-benefit decision framework for activation of a mitigation measure. A simple illustrative example of an infrastructure application is presented.

  10. Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms

    Science.gov (United States)

    Pham, Lien T. H.; Brabyn, Lars

    2017-06-01

    Mangrove forests are well-known for their provision of ecosystem services and capacity to reduce carbon dioxide concentrations in the atmosphere. Mapping and quantifying mangrove biomass is useful for the effective management of these forests and maximizing their ecosystem service performance. The objectives of this research were to model, map, and analyse the biomass change between 2000 and 2011 of mangrove forests in the Cangio region in Vietnam. SPOT 4 and 5 images were used in conjunction with object-based image analysis and machine learning algorithms. The study area included natural and planted mangroves of diverse species. After image preparation, three different mangrove associations were identified using two levels of image segmentation followed by a Support Vector Machine classifier and a range of spectral, texture and GIS information for classification. The overall classification accuracy for the 2000 and 2011 images were 77.1% and 82.9%, respectively. Random Forest regression algorithms were then used for modelling and mapping biomass. The model that integrated spectral, vegetation association type, texture, and vegetation indices obtained the highest accuracy (R2adj = 0.73). Among the different variables, vegetation association type was the most important variable identified by the Random Forest model. Based on the biomass maps generated from the Random Forest, total biomass in the Cangio mangrove forest increased by 820,136 tons over this period, although this change varied between the three different mangrove associations.

  11. Flexible High Energy-Conversion Sensing Materials for Structural Health Monitoring, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The applicant is developing flexible highly-efficient piezoelectric materials for use in structural health monitoring (SHM) as contemplated in the solicitation...

  12. Structural Health Monitoring with Fiber Bragg Grating and Piezo Arrays, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — IFOS and its research institute collaborator, Washington State University (WSU), have demonstrated feasibility of a structural health monitoring (SHM) system for...

  13. Highly Reliable Structural Health Monitoring of Smart Composite Vanes for Jet Engine, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Intelligent Fiber Optic Systems and Auburn University propose a Fiber Bragg Grating (FBG) integrated Structural Health Monitoring (SHM) sensor system capable of...

  14. Very High Frequency Monitoring System for Engine Gearbox and Generator Health Management (Postprint)

    National Research Council Canada - National Science Library

    Watson, Matthew J; Byington, Carl S; Behbahani, Alireza

    2007-01-01

    .... These gas turbine engine vibration monitoring technologies will address existing operation and maintenance goals for current military system and prognostics health management algorithms for advanced engines...

  15. Very High Frequency Monitoring System for Engine Gearbox and Generator Health Management (Postprint)

    National Research Council Canada - National Science Library

    Watson, Matthew J; Byington, Carl S; Behbahani, Alireza

    2007-01-01

    ...) vibration monitoring system that integrates various vibro-acoustic data with intelligent feature extraction and fault isolation algorithms to effectively assess engine gearbox and generator health...

  16. An Ultrasonic Wireless Sensor Network for Data Communication and Structural Health Monitoring, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Typical Structural Health Monitoring (SHM) uses embedded ultrasonic transducers exclusively for non-destructive evaluation (NDE) purposes, whereas data transfer is...

  17. Energy Harvesting for Aerospace Structural Health Monitoring Systems

    International Nuclear Information System (INIS)

    Pearson, M R; Eaton, M J; Pullin, R; Featherston, C A; Holford, K M

    2012-01-01

    Recent research into damage detection methodologies, embedded sensors, wireless data transmission and energy harvesting in aerospace environments has meant that autonomous structural health monitoring (SHM) systems are becoming a real possibility. The most promising system would utilise wireless sensor nodes that are able to make decisions on damage and communicate this wirelessly to a central base station. Although such a system shows great potential and both passive and active monitoring techniques exist for detecting damage in structures, powering such wireless sensors nodes poses a problem. Two such energy sources that could be harvested in abundance on an aircraft are vibration and thermal gradients. Piezoelectric transducers mounted to the surface of a structure can be utilised to generate power from a dynamic strain whilst thermoelectric generators (TEG) can be used to generate power from thermal gradients. This paper reports on the viability of these two energy sources for powering a wireless SHM system from vibrations ranging from 20 to 400Hz and thermal gradients up to 50°C. Investigations showed that using a single vibrational energy harvester raw power levels of up to 1mW could be generated. Further numerical modelling demonstrated that by optimising the position and orientation of the vibrational harvester greater levels of power could be achieved. However using commercial TEGs average power levels over a flight period between 5 to 30mW could be generated. Both of these energy harvesting techniques show a great potential in powering current wireless SHM systems where depending on the complexity the power requirements range from 1 to 180mW.

  18. Compressive sensing based wireless sensor for structural health monitoring

    Science.gov (United States)

    Bao, Yuequan; Zou, Zilong; Li, Hui

    2014-03-01

    Data loss is a common problem for monitoring systems based on wireless sensors. Reliable communication protocols, which enhance communication reliability by repetitively transmitting unreceived packets, is one approach to tackle the problem of data loss. An alternative approach allows data loss to some extent and seeks to recover the lost data from an algorithmic point of view. Compressive sensing (CS) provides such a data loss recovery technique. This technique can be embedded into smart wireless sensors and effectively increases wireless communication reliability without retransmitting the data. The basic idea of CS-based approach is that, instead of transmitting the raw signal acquired by the sensor, a transformed signal that is generated by projecting the raw signal onto a random matrix, is transmitted. Some data loss may occur during the transmission of this transformed signal. However, according to the theory of CS, the raw signal can be effectively reconstructed from the received incomplete transformed signal given that the raw signal is compressible in some basis and the data loss ratio is low. This CS-based technique is implemented into the Imote2 smart sensor platform using the foundation of Illinois Structural Health Monitoring Project (ISHMP) Service Tool-suite. To overcome the constraints of limited onboard resources of wireless sensor nodes, a method called random demodulator (RD) is employed to provide memory and power efficient construction of the random sampling matrix. Adaptation of RD sampling matrix is made to accommodate data loss in wireless transmission and meet the objectives of the data recovery. The embedded program is tested in a series of sensing and communication experiments. Examples and parametric study are presented to demonstrate the applicability of the embedded program as well as to show the efficacy of CS-based data loss recovery for real wireless SHM systems.

  19. Active sensors for health monitoring of aging aerospace structures

    Science.gov (United States)

    Giurgiutiu, Victor; Redmond, James M.; Roach, Dennis P.; Rackow, Kirk

    2000-06-01

    A project to develop non-intrusive active sensors that can be applied on existing aging aerospace structures for monitoring the onset and progress of structural damage (fatigue cracks and corrosion) is presented. The state of the art in active sensors structural health monitoring and damage detection is reviewed. Methods based on (a) elastic wave propagation and (b) electro-mechanical (E/M) impedance technique are cited and briefly discussed. The instrumentation of these specimens with piezoelectric active sensors is illustrated. The main detection strategies (E/M impedance for local area detection and wave propagation for wide area interrogation) are discussed. The signal processing and damage interpretation algorithms are tuned to the specific structural interrogation method used. In the high frequency E/M impedance approach, pattern recognition methods are used to compare impedance signatures taken at various time intervals and to identify damage presence and progression from the change in these signatures. In the wave propagation approach, the acousto- ultrasonic methods identifying additional reflection generated from the damage site and changes in transmission velocity and phase are used. Both approaches benefit from the use of artificial intelligence neural networks algorithms that can extract damage features based on a learning process. Design and fabrication of a set of structural specimens representative of aging aerospace structures is presented. Three built-up specimens, (pristine, with cracks, and with corrosion damage) are used. The specimen instrumentation with active sensors fabricated at the University of South Carolina is illustrated. Preliminary results obtained with the E/M impedance method on pristine and cracked specimens are presented.

  20. Monitoring metabolic health of dairy cattle in the transition period.

    Science.gov (United States)

    LeBlanc, Stephen

    2010-01-01

    This paper reviews the importance of energy metabolism in transition dairy cows, its associations with disease and reproduction, and strategies for monitoring cows under field conditions during this critical time. Essentially all dairy cattle experience a period of insulin resistance, reduced feed intake, negative energy balance, hypocalcemia, reduced immune function, and bacterial contamination of the uterus soon before, or in the weeks after calving. One-third of dairy cows may be affected by some form of metabolic or infectious disease in early lactation. Routine, proactive actions, observations, or analysis are intended to accurately and efficiently provide early detection of problems, to provide an opportunity for investigation and intervention in order to limit the consequences and costs of health problems and reduced animal performance or welfare. Methods of early detection include monitoring of disease and culling records, feed intake, milk production, body condition, and simple metabolic tests. Methods, strategies, and interpretation of measurement of peripartum concentrations of non-esterified fatty acids (NEFA) and beta-hydroxybutyrate (BHB) as indicators of aspects of energy status and disease risk are reviewed. High NEFA (> 0.4 mmol/l) in the last 7 to 10 days before expected calving is associated with increased risk of displaced abomasum (DA), retained placenta, culling before 60 days in milk, and less milk production in the first 4 months of lactation. Subclinical ketosis (serum BHB >1200 to 1400 micromol/l) in the first or second week after calving is associated with increased risk of DA, metritis, clinical ketosis, endometritis, prolonged postpartum anovulation, increased severity of mastitis, and lower milk production in early lactation. There are several validated and practical tools for cow-side measurement of ketosis.

  1. Integration of structural health monitoring and asset management.

    Science.gov (United States)

    2012-08-01

    This project investigated the feasibility and potential benefits of the integration of infrastructure monitoring systems into enterprise-scale transportation management systems. An infrastructure monitoring system designed for bridges was implemented...

  2. Machine Shop Grinding Machines.

    Science.gov (United States)

    Dunn, James

    This curriculum manual is one in a series of machine shop curriculum manuals intended for use in full-time secondary and postsecondary classes, as well as part-time adult classes. The curriculum can also be adapted to open-entry, open-exit programs. Its purpose is to equip students with basic knowledge and skills that will enable them to enter the…

  3. Simulation tools for guided wave based structural health monitoring

    Science.gov (United States)

    Mesnil, Olivier; Imperiale, Alexandre; Demaldent, Edouard; Baronian, Vahan; Chapuis, Bastien

    2018-04-01

    Structural Health Monitoring (SHM) is a thematic derived from Non Destructive Evaluation (NDE) based on the integration of sensors onto or into a structure in order to monitor its health without disturbing its regular operating cycle. Guided wave based SHM relies on the propagation of guided waves in plate-like or extruded structures. Using piezoelectric transducers to generate and receive guided waves is one of the most widely accepted paradigms due to the low cost and low weight of those sensors. A wide range of techniques for flaw detection based on the aforementioned setup is available in the literature but very few of these techniques have found industrial applications yet. A major difficulty comes from the sensitivity of guided waves to a substantial number of parameters such as the temperature or geometrical singularities, making guided wave measurement difficult to analyze. In order to apply guided wave based SHM techniques to a wider spectrum of applications and to transfer those techniques to the industry, the CEA LIST develops novel numerical methods. These methods facilitate the evaluation of the robustness of SHM techniques for multiple applicative cases and ease the analysis of the influence of various parameters, such as sensors positioning or environmental conditions. The first numerical tool is the guided wave module integrated to the commercial software CIVA, relying on a hybrid modal-finite element formulation to compute the guided wave response of perturbations (cavities, flaws…) in extruded structures of arbitrary cross section such as rails or pipes. The second numerical tool is based on the spectral element method [2] and simulates guided waves in both isotropic (metals) and orthotropic (composites) plate like-structures. This tool is designed to match the widely accepted sparse piezoelectric transducer array SHM configuration in which each embedded sensor acts as both emitter and receiver of guided waves. This tool is under development and

  4. Actively cooled plasma facing components qualification, commissioning and health monitoring

    International Nuclear Information System (INIS)

    Escourbiac, F.; Durocher, A.; Grosman, A.; Courtois, X.; Farjon, J.-L.; Schlosser, J.; Merola, M.; Tivey, R.

    2006-01-01

    In modern steady state magnetic fusion devices, actively cooled plasma facing components (PFC) have to handle heat fluxes in the range of 10-20 MW/m 2 . This generates a number of engineering constraints: the armour materials must be refractory and compatible with plasma wall interaction requirements (low sputtering and/or low atomic number); the heat sink must offer high thermal conductivity, high mechanical resistance and sufficient ductility; the component cooling system -which is generally based on the circulation of pressurized water in the PFC's heat sink - must offer high thermal heat transfer efficiency. Furthermore, the assembling of the refractory armour material onto the metallic heat sink causes generic difficulties strongly depending on thermo-mechanical properties of materials and design requirements. Life time of the PFC during plasma operation are linked to their manufacturing quality, in particular they are reduced by the possible presence of flaw assembling. The fabrication of PFC in an industrial frame including their qualification and their commissioning - which consists in checking the manufacturing quality during and at the end of manufacture - is a real challenge. From experience gained at Tore Supra on carbon fibre composite flat tiles technology components, it was assessed that a set of qualifications activities must be operated during R(and)D and manufacturing phases. Dedicated Non Destructive Technique (NDT) based on advanced active infrared thermography was developed for this purpose, afterwards, correlations between NDT, high heat flux testing and thermomechanical modelling were performed to analyse damage detection and propagation, and define an acceptance criteria valuable for industrial application. Health monitoring using lock-in technique was also recently operated in-situ of the Tore Supra tokamak for detection of possible defect propagation during operations, presence of acoustic precursor for critical heat flux detection induced

  5. Structural health monitoring of compression connectors for overhead transmission lines

    Science.gov (United States)

    Wang, Hong; Wang, Jy-An John; Swindeman, Joseph P.; Ren, Fei; Chan, John

    2017-04-01

    Two-stage aluminum conductor steel-reinforced (ACSR) compression connectors are extensively used in US overhead transmission lines. The connectors are made by crimping a steel sleeve onto a steel core and an aluminum sleeve over electrical conducting aluminum strands. The connectors are designed to operate at temperatures up to 125°C, but their performance is increasingly degrading because of overloading of lines. Currently, electric utilities conduct routine line inspections using thermal and electrical measurements, but these methods do not provide information about the structural integrity of connectors. In this work, structural health monitoring (SHM) of compression connectors was studied using electromechanical impedance (EMI) analysis. Lead zirconate titanate (PZT)-5A was identified as a smart material for SHM. A flexible high-temperature bonding layer was used to address challenges in PZT integration due to a significant difference in the coefficients of thermal expansion of PZT and the aluminum substrate. The steel joint on the steel core was investigated because it is responsible for the ultimate tensile strength of the connector. Tensile testing was used to induce structural damage to the joint, or steel core pullout, and thermal cycling introduced additional structural perturbations. EMI measurements were conducted between the tests. The root mean square deviation (RMSD) of EMI was identified as a damage index. The use of steel joints has been shown to enable SHM under simulated conditions. The EMI signature is sensitive to variations in structural conditions. RMSD can be correlated to the structural health of a connector and has potential for use in the SHM and structural integrity evaluation.

  6. Multidisciplinary training program to create new breed of radiation monitor: the health and safety technician

    International Nuclear Information System (INIS)

    Vance, W.F.

    1979-01-01

    A multidiscipline training program established to create a new monitor, theHealth and Safety Technician, is described. The training program includes instruction in fire safety, explosives safety, industrial hygiene, industrial safety, health physics, and general safety practices

  7. 75 FR 52711 - Notice of Request for Approval of an Information Collection; National Animal Health Monitoring...

    Science.gov (United States)

    2010-08-27

    ...In accordance with the Paperwork Reduction Act of 1995, this notice announces the Animal and Plant Health Inspection Service's intention to initiate an information collection to support the National Animal Health Monitoring System Sheep 2011 Study.

  8. 76 FR 13969 - Notice of Request for Approval of an Information Collection; National Animal Health Monitoring...

    Science.gov (United States)

    2011-03-15

    ...In accordance with the Paperwork Reduction Act, this notice announces the Animal and Plant Health Inspection Service's intention to initiate an information collection to support the research and development phase of surveys entitled National Animal Health Monitoring System needs assessments.

  9. 76 FR 28414 - Notice of Request for Approval of an Information Collection; National Animal Health Monitoring...

    Science.gov (United States)

    2011-05-17

    ...In accordance with the Paperwork Reduction Act of 1995, this notice announces the Animal and Plant Health Inspection Service's intention to initiate Emergency Epidemiologic Investigations, an information collection to support the National Animal Health Monitoring System.

  10. Energy Harvesting for Structural Health Monitoring Sensor Networks

    Energy Technology Data Exchange (ETDEWEB)

    Park, G.; Farrar, C. R.; Todd, M. D.; Hodgkiss, T.; Rosing, T.

    2007-02-26

    This report has been developed based on information exchanges at a 2.5-day workshop on energy harvesting for embedded structural health monitoring (SHM) sensing systems that was held June 28-30, 2005, at Los Alamos National Laboratory. The workshop was hosted by the LANL/UCSD Engineering Institute (EI). This Institute is an education- and research-focused collaboration between Los Alamos National Laboratory (LANL) and the University of California, San Diego (UCSD), Jacobs School of Engineering. A Statistical Pattern Recognition paradigm for SHM is first presented and the concept of energy harvesting for embedded sensing systems is addressed with respect to the data acquisition portion of this paradigm. Next, various existing and emerging sensing modalities used for SHM and their respective power requirements are summarized, followed by a discussion of SHM sensor network paradigms, power requirements for these networks and power optimization strategies. Various approaches to energy harvesting and energy storage are discussed and limitations associated with the current technology are addressed. This discussion also addresses current energy harvesting applications and system integration issues. The report concludes by defining some future research directions and possible technology demonstrations that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes.

  11. Development of blood extraction system for health monitoring system

    Science.gov (United States)

    Tsuchiya, Kazuyoshi; Nakanishi, Naoyuki; Nakamachi, Eiji

    2004-03-01

    The purpose of this research is to develop the compact human blood sampling device applied for a health monitoring system(HMS), which is called "Mobile Hospital". The HMS consists of (1) a micro electrical pumping system for blood extraction, (2) a bio-sensor to detect and evaluate an amount of Glucose, Cholesterol and Urea in extracted blood, by using enzyme such as Glucoseoxidase (GOD), Cholesteroloxidase and Urease. The mechanical design elements of the device are bio-compatible microneedle, indentation unit using a shape memory alloy(SMA) actuator and pumping unit using a piezoelectric microactuator. The design concept is the biomimetic micromachine of female mosquito"s blood sampling mechanism. The performances of the main mechanical elements such as indentation force of the microneedle, actual stroke of the indentation unit using a SMA actuator and liquid sampling ability of the pumping unit using PZT piezoelectric microactuator were measured. The 3 mm stroke of the indentation load generated by SMA actuator was 0.8mN. The amount of imitation blood extracted by using bimorph PZT actuators was about 0.5 microliters for 10 sec. A 60-micrometer outer diameter and 25-micrometer inner diameter Titanium microneedle, which size is same as female mosquito"s labium, was produced by sputter deposition.

  12. Structural Health Monitoring of Transport Aircraft with Fuzzy Logic Modeling

    Directory of Open Access Journals (Sweden)

    Ray C. Chang

    2013-01-01

    Full Text Available A structural health monitoring method based on the concept of static aeroelasticity is presented in this paper. This paper focuses on the estimation of these aeroelastic effects on older transport aircraft, in particular the structural components that are most affected, in severe atmospheric turbulence. Because the structural flexibility properties are mostly unknown to aircraft operators, only the trend, not the magnitude, of these effects is estimated. For this purpose, one useful concept in static aeroelastic effects for conventional aircraft structures is that under aeroelastic deformation the aerodynamic center should move aft. This concept is applied in the present paper by using the fuzzy-logic aerodynamic models. A twin-jet transport aircraft in severe atmospheric turbulence involving plunging motion is examined. It is found that the pitching moment derivatives in cruise with moderate to severe turbulence in transonic flight indicate some degree of abnormality in the stabilizer (i.e., the horizontal tail. Therefore, the horizontal tail is the most severely affected structural component of the aircraft probably caused by vibration under the dynamic loads induced by turbulence.

  13. A wireless laser displacement sensor node for structural health monitoring.

    Science.gov (United States)

    Park, Hyo Seon; Kim, Jong Moon; Choi, Se Woon; Kim, Yousok

    2013-09-30

    This study describes a wireless laser displacement sensor node that measures displacement as a representative damage index for structural health monitoring (SHM). The proposed measurement system consists of a laser displacement sensor (LDS) and a customized wireless sensor node. Wireless communication is enabled by a sensor node that consists of a sensor module, a code division multiple access (CDMA) communication module, a processor, and a power module. An LDS with a long measurement distance is chosen to increase field applicability. For a wireless sensor node driven by a battery, we use a power control module with a low-power processor, which facilitates switching between the sleep and active modes, thus maximizing the power consumption efficiency during non-measurement and non-transfer periods. The CDMA mode is also used to overcome the limitation of communication distance, which is a challenge for wireless sensor networks and wireless communication. To evaluate the reliability and field applicability of the proposed wireless displacement measurement system, the system is tested onsite to obtain the required vertical displacement measurements during the construction of mega-trusses and an edge truss, which are the primary structural members in a large-scale irregular building currently under construction. The measurement values confirm the validity of the proposed wireless displacement measurement system and its potential for use in safety evaluations of structural elements.

  14. Simultaneous excitation system for efficient guided wave structural health monitoring

    Science.gov (United States)

    Hua, Jiadong; Michaels, Jennifer E.; Chen, Xin; Lin, Jing

    2017-10-01

    Many structural health monitoring systems utilize guided wave transducer arrays for defect detection and localization. Signals are usually acquired using the ;pitch-catch; method whereby each transducer is excited in turn and the response is received by the remaining transducers. When extensive signal averaging is performed, the data acquisition process can be quite time-consuming, especially for metallic components that require a low repetition rate to allow signals to die out. Such a long data acquisition time is particularly problematic if environmental and operational conditions are changing while data are being acquired. To reduce the total data acquisition time, proposed here is a methodology whereby multiple transmitters are simultaneously triggered, and each transmitter is driven with a unique excitation. The simultaneously transmitted waves are captured by one or more receivers, and their responses are processed by dispersion-compensated filtering to extract the response from each individual transmitter. The excitation sequences are constructed by concatenating a series of chirps whose start and stop frequencies are randomly selected from a specified range. The process is optimized using a Monte-Carlo approach to select sequences with impulse-like autocorrelations and relatively flat cross-correlations. The efficacy of the proposed methodology is evaluated by several metrics and is experimentally demonstrated with sparse array imaging of simulated damage.

  15. A Model-Driven Framework to Develop Personalized Health Monitoring

    Directory of Open Access Journals (Sweden)

    Algimantas Venčkauskas

    2016-07-01

    Full Text Available Both distributed healthcare systems and the Internet of Things (IoT are currently hot topics. The latter is a new computing paradigm to enable advanced capabilities in engineering various applications, including those for healthcare. For such systems, the core social requirement is the privacy/security of the patient information along with the technical requirements (e.g., energy consumption and capabilities for adaptability and personalization. Typically, the functionality of the systems is predefined by the patient’s data collected using sensor networks along with medical instrumentation; then, the data is transferred through the Internet for treatment and decision-making. Therefore, systems creation is indeed challenging. In this paper, we propose a model-driven framework to develop the IoT-based prototype and its reference architecture for personalized health monitoring (PHM applications. The framework contains a multi-layered structure with feature-based modeling and feature model transformations at the top and the application software generation at the bottom. We have validated the framework using available tools and developed an experimental PHM to test some aspects of the functionality of the reference architecture in real time. The main contribution of the paper is the development of the model-driven computational framework with emphasis on the synergistic effect of security and energy issues.

  16. Cooperative wireless network control based health and activity monitoring system.

    Science.gov (United States)

    Prakash, R; Ganesh, A Balaji; Girish, Siva V

    2016-10-01

    A real-time cooperative communication based wireless network is presented for monitoring health and activity of an end-user in their environment. The cooperative communication offers better energy consumption and also an opportunity to aware the current location of a user non-intrusively. The link between mobile sensor node and relay node is dynamically established by using Received Signal Strength Indicator (RSSI) and Link Quality Indicator (LQI) based on adaptive relay selection scheme. The study proposes a Linear Acceleration based Transmission Power Decision Control (LA-TPDC) algorithm to further enhance the energy efficiency of cooperative communication. Further, the occurrences of false alarms are carefully prevented by introducing three stages of sequential warning system. The real-time experiments are carried-out by using the nodes, namely mobile sensor node, relay nodes and a destination node which are indigenously developed by using a CC430 microcontroller integrated with an in-built transceiver at 868 MHz. The wireless node performance characteristics, such as energy consumption, Signal-Noise ratio (SNR), Bit Error Rate (BER), Packet Delivery Ratio (PDR) and transmission offset are evaluated for all the participated nodes. The experimental results observed that the proposed linear acceleration based transmission power decision control algorithm almost doubles the battery life time than energy efficient conventional cooperative communication.

  17. Real-Time and Secure Wireless Health Monitoring

    Science.gov (United States)

    Dağtaş, S.; Pekhteryev, G.; Şahinoğlu, Z.; Çam, H.; Challa, N.

    2008-01-01

    We present a framework for a wireless health monitoring system using wireless networks such as ZigBee. Vital signals are collected and processed using a 3-tiered architecture. The first stage is the mobile device carried on the body that runs a number of wired and wireless probes. This device is also designed to perform some basic processing such as the heart rate and fatal failure detection. At the second stage, further processing is performed by a local server using the raw data transmitted by the mobile device continuously. The raw data is also stored at this server. The processed data as well as the analysis results are then transmitted to the service provider center for diagnostic reviews as well as storage. The main advantages of the proposed framework are (1) the ability to detect signals wirelessly within a body sensor network (BSN), (2) low-power and reliable data transmission through ZigBee network nodes, (3) secure transmission of medical data over BSN, (4) efficient channel allocation for medical data transmission over wireless networks, and (5) optimized analysis of data using an adaptive architecture that maximizes the utility of processing and computational capacity at each platform. PMID:18497866

  18. Structural Health Monitoring under Nonlinear Environmental or Operational Influences

    Directory of Open Access Journals (Sweden)

    Jyrki Kullaa

    2014-01-01

    Full Text Available Vibration-based structural health monitoring is based on detecting changes in the dynamic characteristics of the structure. It is well known that environmental or operational variations can also have an influence on the vibration properties. If these effects are not taken into account, they can result in false indications of damage. If the environmental or operational variations cause nonlinear effects, they can be compensated using a Gaussian mixture model (GMM without the measurement of the underlying variables. The number of Gaussian components can also be estimated. For the local linear components, minimum mean square error (MMSE estimation is applied to eliminate the environmental or operational influences. Damage is detected from the residuals after applying principal component analysis (PCA. Control charts are used for novelty detection. The proposed approach is validated using simulated data and the identified lowest natural frequencies of the Z24 Bridge under temperature variation. Nonlinear models are most effective if the data dimensionality is low. On the other hand, linear models often outperform nonlinear models for high-dimensional data.

  19. Bridges analysis, design, structural health monitoring, and rehabilitation

    CERN Document Server

    Bakht, Baidar

    2015-01-01

    This book offers a valuable guide for practicing bridge engineers and graduate students in structural engineering; its main purpose is to present the latest concepts in bridge engineering in fairly easy-to-follow terms. The book provides details of easy-to-use computer programs for: ·      Analysing slab-on-girder bridges for live load distribution. ·      Analysing slab and other solid bridge components for live load distribution. ·      Analysing and designing concrete deck slab overhangs of girder bridges under vehicular loads. ·      Determining the failure loads of concrete deck slabs of girder bridges under concentrated wheel loads. In addition, the book includes extensive chapters dealing with the design of wood bridges and soil-steel bridges. Further, a unique chapter on structural health monitoring (SHM) will help bridge engineers determine the actual load carrying capacities of bridges, as opposed to their perceived analytical capacities. The chapter addressing structures...

  20. Damage Detection with Streamlined Structural Health Monitoring Data

    Directory of Open Access Journals (Sweden)

    Jian Li

    2015-04-01

    Full Text Available The huge amounts of sensor data generated by large scale sensor networks in on-line structural health monitoring (SHM systems often overwhelms the systems’ capacity for data transmission and analysis. This paper presents a new concept for an integrated SHM system in which a streamlined data flow is used as a unifying thread to integrate the individual components of on-line SHM systems. Such an integrated SHM system has a few desirable functionalities including embedded sensor data compression, interactive sensor data retrieval, and structural knowledge discovery, which aim to enhance the reliability, efficiency, and robustness of on-line SHM systems. Adoption of this new concept will enable the design of an on-line SHM system with more uniform data generation and data handling capacity for its subsystems. To examine this concept in the context of vibration-based SHM systems, real sensor data from an on-line SHM system comprising a scaled steel bridge structure and an on-line data acquisition system with remote data access was used in this study. Vibration test results clearly demonstrated the prominent performance characteristics of the proposed integrated SHM system including rapid data access, interactive data retrieval and knowledge discovery of structural conditions on a global level.

  1. Damage detection with streamlined structural health monitoring data.

    Science.gov (United States)

    Li, Jian; Deng, Jun; Xie, Weizhi

    2015-04-15

    The huge amounts of sensor data generated by large scale sensor networks in on-line structural health monitoring (SHM) systems often overwhelms the systems' capacity for data transmission and analysis. This paper presents a new concept for an integrated SHM system in which a streamlined data flow is used as a unifying thread to integrate the individual components of on-line SHM systems. Such an integrated SHM system has a few desirable functionalities including embedded sensor data compression, interactive sensor data retrieval, and structural knowledge discovery, which aim to enhance the reliability, efficiency, and robustness of on-line SHM systems. Adoption of this new concept will enable the design of an on-line SHM system with more uniform data generation and data handling capacity for its subsystems. To examine this concept in the context of vibration-based SHM systems, real sensor data from an on-line SHM system comprising a scaled steel bridge structure and an on-line data acquisition system with remote data access was used in this study. Vibration test results clearly demonstrated the prominent performance characteristics of the proposed integrated SHM system including rapid data access, interactive data retrieval and knowledge discovery of structural conditions on a global level.

  2. Social [and health] relevance of psychotropic substances monitoring in air

    International Nuclear Information System (INIS)

    Cecinato, Angelo; Balducci, Catia; Mollica, Roberto; Serpelloni, Giovanni

    2013-01-01

    Drug abuse assessment methods based on measuring illicit substances in waste waters are consolidated. The approach of ambient air monitoring looks questionable, nonetheless it can be explored if the variables determining the drug burdens are accounted for, or suitable co-contaminants are adopted to normalize concentrations to environmental and human contours. The general approach linking the airborne drug concentrations to consumption is presented and the case of cocaine is discussed according to measurements conducted in Italy. The cocaine/nicotine concentration ratio, identified as the most suitable tool, fitted well with anti-drug Police operations and people noticed for drug-related crimes, and with the abuse prevalence estimated in the cities investigated. According to that, the conversion factors of drug concentrations into prevalence estimates seem assessable, provided sufficient databases over space and time are collected. Further investigations are necessary to understand if airborne drugs cause adverse sanitary effects. -- Highlights: •The drug contents in the air were discussed to draw information about abuse prevalence. •The time and site drug modulations were compared to those of the airborne toxicants. •Nicotine looks suitable to normalize the cocaine concentrations to human and environmental contours. •The health impact of illicit and licit drugs onto non-abusers is still insufficiently understood. -- The airborne cocaine/nicotine concentration ratio looks a promising tool to estimate the cocaine abuse prevalence

  3. Phase Space Dissimilarity Measures for Structural Health Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Bubacz, Jacob A [ORNL; Chmielewski, Hana T [ORNL; Pape, Alexander E [ORNL; Depersio, Andrew J [ORNL; Hively, Lee M [ORNL; Abercrombie, Robert K [ORNL; Boone, Shane [ORNL

    2011-11-01

    A novel method for structural health monitoring (SHM), known as the Phase Space Dissimilarity Measures (PSDM) approach, is proposed and developed. The patented PSDM approach has already been developed and demonstrated for a variety of equipment and biomedical applications. Here, we investigate SHM of bridges via analysis of time serial accelerometer measurements. This work has four aspects. The first is algorithm scalability, which was found to scale linearly from one processing core to four cores. Second, the same data are analyzed to determine how the use of the PSDM approach affects sensor placement. We found that a relatively low-density placement sufficiently captures the dynamics of the structure. Third, the same data are analyzed by unique combinations of accelerometer axes (vertical, longitudinal, and lateral with respect to the bridge) to determine how the choice of axes affects the analysis. The vertical axis is found to provide satisfactory SHM data. Fourth, statistical methods were investigated to validate the PSDM approach for this application, yielding statistically significant results.

  4. Active Wireless System for Structural Health Monitoring Applications.

    Science.gov (United States)

    Perera, Ricardo; Pérez, Alberto; García-Diéguez, Marta; Zapico-Valle, José Luis

    2017-12-11

    The use of wireless sensors in Structural Health Monitoring (SHM) has increased significantly in the last years. Piezoelectric-based lead zirconium titanate (PZT) sensors have been on the rise in SHM due to their superior sensing abilities. They are applicable in different technologies such as electromechanical impedance (EMI)-based SHM. This work develops a flexible wireless smart sensor (WSS) framework based on the EMI method using active sensors for full-scale and autonomous SHM. In contrast to passive sensors, the self-sensing properties of the PZTs allow interrogating with or exciting a structure when desired. The system integrates the necessary software and hardware within a service-oriented architecture approach able to provide in a modular way the services suitable to satisfy the key requirements of a WSS. The framework developed in this work has been validated on different experimental applications. Initially, the reliability of the EMI method when carried out with the proposed wireless sensor system is evaluated by comparison with the wireless counterpart. Afterwards, the performance of the system is evaluated in terms of software stability and reliability of functioning.

  5. Improved Stochastic Subspace System Identification for Structural Health Monitoring

    Science.gov (United States)

    Chang, Chia-Ming; Loh, Chin-Hsiung

    2015-07-01

    Structural health monitoring acquires structural information through numerous sensor measurements. Vibrational measurement data render the dynamic characteristics of structures to be extracted, in particular of the modal properties such as natural frequencies, damping, and mode shapes. The stochastic subspace system identification has been recognized as a power tool which can present a structure in the modal coordinates. To obtain qualitative identified data, this tool needs to spend computational expense on a large set of measurements. In study, a stochastic system identification framework is proposed to improve the efficiency and quality of the conventional stochastic subspace system identification. This framework includes 1) measured signal processing, 2) efficient space projection, 3) system order selection, and 4) modal property derivation. The measured signal processing employs the singular spectrum analysis algorithm to lower the noise components as well as to present a data set in a reduced dimension. The subspace is subsequently derived from the data set presented in a delayed coordinate. With the proposed order selection criteria, the number of structural modes is determined, resulting in the modal properties. This system identification framework is applied to a real-world bridge for exploring the feasibility in real-time applications. The results show that this improved system identification method significantly decreases computational time, while qualitative modal parameters are still attained.

  6. Active Wireless System for Structural Health Monitoring Applications

    Directory of Open Access Journals (Sweden)

    Ricardo Perera

    2017-12-01

    Full Text Available The use of wireless sensors in Structural Health Monitoring (SHM has increased significantly in the last years. Piezoelectric-based lead zirconium titanate (PZT sensors have been on the rise in SHM due to their superior sensing abilities. They are applicable in different technologies such as electromechanical impedance (EMI-based SHM. This work develops a flexible wireless smart sensor (WSS framework based on the EMI method using active sensors for full-scale and autonomous SHM. In contrast to passive sensors, the self-sensing properties of the PZTs allow interrogating with or exciting a structure when desired. The system integrates the necessary software and hardware within a service-oriented architecture approach able to provide in a modular way the services suitable to satisfy the key requirements of a WSS. The framework developed in this work has been validated on different experimental applications. Initially, the reliability of the EMI method when carried out with the proposed wireless sensor system is evaluated by comparison with the wireless counterpart. Afterwards, the performance of the system is evaluated in terms of software stability and reliability of functioning.

  7. Structural health monitoring and probability of detection estimation

    Science.gov (United States)

    Forsyth, David S.

    2016-02-01

    Structural health monitoring (SHM) methods are often based on nondestructive testing (NDT) sensors and are often proposed as replacements for NDT to lower cost and/or improve reliability. In order to take advantage of SHM for life cycle management, it is necessary to determine the Probability of Detection (POD) of the SHM system just as for traditional NDT to ensure that the required level of safety is maintained. Many different possibilities exist for SHM systems, but one of the attractive features of SHM versus NDT is the ability to take measurements very simply after the SHM system is installed. Using a simple statistical model of POD, some authors have proposed that very high rates of SHM system data sampling can result in high effective POD even in situations where an individual test has low POD. In this paper, we discuss the theoretical basis for determining the effect of repeated inspections, and examine data from SHM experiments against this framework to show how the effective POD from multiple tests can be estimated.

  8. On-line Bayesian model updating for structural health monitoring

    Science.gov (United States)

    Rocchetta, Roberto; Broggi, Matteo; Huchet, Quentin; Patelli, Edoardo

    2018-03-01

    Fatigue induced cracks is a dangerous failure mechanism which affects mechanical components subject to alternating load cycles. System health monitoring should be adopted to identify cracks which can jeopardise the structure. Real-time damage detection may fail in the identification of the cracks due to different sources of uncertainty which have been poorly assessed or even fully neglected. In this paper, a novel efficient and robust procedure is used for the detection of cracks locations and lengths in mechanical components. A Bayesian model updating framework is employed, which allows accounting for relevant sources of uncertainty. The idea underpinning the approach is to identify the most probable crack consistent with the experimental measurements. To tackle the computational cost of the Bayesian approach an emulator is adopted for replacing the computationally costly Finite Element model. To improve the overall robustness of the procedure, different numerical likelihoods, measurement noises and imprecision in the value of model parameters are analysed and their effects quantified. The accuracy of the stochastic updating and the efficiency of the numerical procedure are discussed. An experimental aluminium frame and on a numerical model of a typical car suspension arm are used to demonstrate the applicability of the approach.

  9. Development of sensing techniques for weaponry health monitoring

    Science.gov (United States)

    Edwards, Eugene; Ruffin, Paul B.; Walker, Ebonee A.; Brantley, Christina L.

    2013-04-01

    Due to the costliness of destructive evaluation methods for assessing the aging and shelf-life of missile and rocket components, the identification of nondestructive evaluation methods has become increasingly important to the Army. Verifying that there is a sufficient concentration of stabilizer is a dependable indicator that the missile's double-based solid propellant is viable. The research outlined in this paper summarizes the Army Aviation and Missile Research, Development, and Engineering Center's (AMRDEC's) comparative use of nanoporous membranes, carbon nanotubes, and optical spectroscopic configured sensing techniques for detecting degradation in rocket motor propellant. The first sensing technique utilizes a gas collecting chamber consisting of nanoporous structures that trap the smaller solid propellant particles for measurement by a gas analysis device. In collaboration with NASA-Ames, sensing methods are developed that utilize functionalized single-walled carbon nanotubes as the key sensing element. The optical spectroscopic sensing method is based on a unique light collecting optical fiber system designed to detect the concentration of the propellant stabilizer. Experimental setups, laboratory results, and overall effectiveness of each technique are presented in this paper. Expectations are for the three sensing mechanisms to provide nondestructive evaluation methods that will offer cost-savings and improved weaponry health monitoring.

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

    Science.gov (United States)

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

    2001-06-01

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

  11. A Wireless Laser Displacement Sensor Node for Structural Health Monitoring

    Directory of Open Access Journals (Sweden)

    Se Woon Choi

    2013-09-01

    Full Text Available This study describes a wireless laser displacement sensor node that measures displacement as a representative damage index for structural health monitoring (SHM. The proposed measurement system consists of a laser displacement sensor (LDS and a customized wireless sensor node. Wireless communication is enabled by a sensor node that consists of a sensor module, a code division multiple access (CDMA communication module, a processor, and a power module. An LDS with a long measurement distance is chosen to increase field applicability. For a wireless sensor node driven by a battery, we use a power control module with a low-power processor, which facilitates switching between the sleep and active modes, thus maximizing the power consumption efficiency during non-measurement and non-transfer periods. The CDMA mode is also used to overcome the limitation of communication distance, which is a challenge for wireless sensor networks and wireless communication. To evaluate the reliability and field applicability of the proposed wireless displacement measurement system, the system is tested onsite to obtain the required vertical displacement measurements during the construction of mega-trusses and an edge truss, which are the primary structural members in a large-scale irregular building currently under construction. The measurement values confirm the validity of the proposed wireless displacement measurement system and its potential for use in safety evaluations of structural elements.

  12. Acceptance by laypersons and medical professionals of the personalized eHealth platform, eHealthMonitor.

    Science.gov (United States)

    Griebel, Lena; Kolominsky-Rabas, Peter; Schaller, Sandra; Siudyka, Jakub; Sierpinski, Radoslaw; Papapavlou, Dimitrios; Simeonidou, Aliki; Prokosch, Hans-Ulrich; Sedlmayr, Martin

    2017-09-01

    Often, eHealth services are not accepted because of factors such as eHealth literacy or trust. Within this study, eHealthMonitor was evaluated in three European countries (Germany, Greece, and Poland) by medical professionals and laypersons with respect to numerous acceptance factors. Questionnaires were created on the basis of factors from literature and with the help of scales which have already been validated. A qualitative survey was conducted in Germany, Poland, and Greece. The eHealth literacy of all participants was medium/high. Laypersons mostly agreed that they could easily become skillful with eHealthMonitor and that other people thought that they should use eHealthMonitor. Amongst medical professionals, a large number were afraid that eHealthMonitor could violate their privacy or the privacy of their patients. Overall, the participants thought that eHealthMonitor was a good concept and that they would use it. The main hindrances to the use of eHealthMonitor were found in trust issues including data privacy. In the future, more research on the linkage of all measured factors is needed, for example, to address the question of whether highly educated people tend to mistrust eHealth information more than people with lower levels of education.

  13. Automated Long-Term Monitoring of Parallel Microfluidic Operations Applying a Machine Vision-Assisted Positioning Method

    Science.gov (United States)

    Yip, Hon Ming; Li, John C. S.; Cui, Xin; Gao, Qiannan; Leung, Chi Chiu

    2014-01-01

    As microfluidics has been applied extensively in many cell and biochemical applications, monitoring the related processes is an important requirement. In this work, we design and fabricate a high-throughput microfluidic device which contains 32 microchambers to perform automated parallel microfluidic operations and monitoring on an automated stage of a microscope. Images are captured at multiple spots on the device during the operations for monitoring samples in microchambers in parallel; yet the device positions may vary at different time points throughout operations as the device moves back and forth on a motorized microscopic stage. Here, we report an image-based positioning strategy to realign the chamber position before every recording of microscopic image. We fabricate alignment marks at defined locations next to the chambers in the microfluidic device as reference positions. We also develop image processing algorithms to recognize the chamber positions in real-time, followed by realigning the chambers to their preset positions in the captured images. We perform experiments to validate and characterize the device functionality and the automated realignment operation. Together, this microfluidic realignment strategy can be a platform technology to achieve precise positioning of multiple chambers for general microfluidic applications requiring long-term parallel monitoring of cell and biochemical activities. PMID:25133248

  14. Automated long-term monitoring of parallel microfluidic operations applying a machine vision-assisted positioning method.

    Science.gov (United States)

    Yip, Hon Ming; Li, John C S; Xie, Kai; Cui, Xin; Prasad, Agrim; Gao, Qiannan; Leung, Chi Chiu; Lam, Raymond H W

    2014-01-01

    As microfluidics has been applied extensively in many cell and biochemical applications, monitoring the related processes is an important requirement. In this work, we design and fabricate a high-throughput microfluidic device which contains 32 microchambers to perform automated parallel microfluidic operations and monitoring on an automated stage of a microscope. Images are captured at multiple spots on the device during the operations for monitoring samples in microchambers in parallel; yet the device positions may vary at different time points throughout operations as the device moves back and forth on a motorized microscopic stage. Here, we report an image-based positioning strategy to realign the chamber position before every recording of microscopic image. We fabricate alignment marks at defined locations next to the chambers in the microfluidic device as reference positions. We also develop image processing algorithms to recognize the chamber positions in real-time, followed by realigning the chambers to their preset positions in the captured images. We perform experiments to validate and characterize the device functionality and the automated realignment operation. Together, this microfluidic realignment strategy can be a platform technology to achieve precise positioning of multiple chambers for general microfluidic applications requiring long-term parallel monitoring of cell and biochemical activities.

  15. Automated Long-Term Monitoring of Parallel Microfluidic Operations Applying a Machine Vision-Assisted Positioning Method

    Directory of Open Access Journals (Sweden)

    Hon Ming Yip

    2014-01-01

    Full Text Available As microfluidics has been applied extensively in many cell and biochemical applications, monitoring the related processes is an important requirement. In this work, we design and fabricate a high-throughput microfluidic device which contains 32 microchambers to perform automated parallel microfluidic operations and monitoring on an automated stage of a microscope. Images are captured at multiple spots on the device during the operations for monitoring samples in microchambers in parallel; yet the device positions may vary at different time points throughout operations as the device moves back and forth on a motorized microscopic stage. Here, we report an image-based positioning strategy to realign the chamber position before every recording of microscopic image. We fabricate alignment marks at defined locations next to the chambers in the microfluidic device as reference positions. We also develop image processing algorithms to recognize the chamber positions in real-time, followed by realigning the chambers to their preset positions in the captured images. We perform experiments to validate and characterize the device functionality and the automated realignment operation. Together, this microfluidic realignment strategy can be a platform technology to achieve precise positioning of multiple chambers for general microfluidic applications requiring long-term parallel monitoring of cell and biochemical activities.

  16. Machine Protection

    CERN Document Server

    Schmidt, R

    2014-01-01

    The protection of accelerator equipment is as old as accelerator technology and was for many years related to high-power equipment. Examples are the protection of powering equipment from overheating (magnets, power converters, high-current cables), of superconducting magnets from damage after a quench and of klystrons. The protection of equipment from beam accidents is more recent. It is related to the increasing beam power of high-power proton accelerators such as ISIS, SNS, ESS and the PSI cyclotron, to the emission of synchrotron light by electron–positron accelerators and FELs, and to the increase of energy stored in the beam (in particular for hadron colliders such as LHC). Designing a machine protection system requires an excellent understanding of accelerator physics and operation to anticipate possible failures that could lead to damage. Machine protection includes beam and equipment monitoring, a system to safely stop beam operation (e.g. dumping the beam or stopping the beam at low energy) and an ...

  17. Contributions of national and global health estimates to monitoring health-related sustainable development goals.

    Science.gov (United States)

    Bundhamcharoen, Kanitta; Limwattananon, Supon; Kusreesakul, Khanitta; Tangcharoensathien, Viroj

    2016-01-01

    The millennium development goals triggered an increased demand for data on child and maternal mortalities for monitoring progress. With the advent of the sustainable development goals and growing evidence of an epidemiological transition toward non-communicable diseases, policymakers need data on mortality and disease trends and distribution to inform effective policies and support monitoring progress. Where there are limited capacities to produce national health estimates (NHEs), global health estimates (GHEs) can fill gaps for global monitoring and comparisons. This paper discusses lessons learned from Thailand's burden of disease (BOD) study on capacity development on NHEs and discusses the contributions and limitations of GHEs in informing policies at the country level. Through training and technical support by external partners, capacities are gradually strengthened and institutionalized to enable regular updates of BOD at national and subnational levels. Initially, the quality of cause-of-death reporting in death certificates was inadequate, especially for deaths occurring in the community. Verbal autopsies were conducted, using domestic resources, to determine probable causes of deaths occurring in the community. This method helped to improve the estimation of years of life lost. Since the achievement of universal health coverage in 2002, the quality of clinical data on morbidities has also considerably improved. There are significant discrepancies between the Global Burden of Disease 2010 study estimates for Thailand and the 1999 nationally generated BOD, especially for years of life lost due to HIV/AIDS, and the ranking of priority diseases. National ownership of NHEs and an effective interface between researchers and decision-makers contribute to enhanced country policy responses, whereas subnational data are intended to be used by various subnational partners. Although GHEs contribute to benchmarking country achievement compared with global health

  18. [What potential do geographic information systems have for population-wide health monitoring in Germany? : Perspectives and challenges for the health monitoring of the Robert Koch Institute].

    Science.gov (United States)

    Thißen, Martin; Niemann, Hildegard; Varnaccia, Gianni; Rommel, Alexander; Teti, Andrea; Butschalowsky, Hans; Manz, Kristin; Finger, Jonas David; Kroll, Lars Eric; Ziese, Thomas

    2017-12-01

    Geographic information systems (GISs) are computer-based systems with which geographical data can be recorded, stored, managed, analyzed, visualized and provided. In recent years, they have become an integral part of public health research. They offer a broad range of analysis tools, which enable innovative solutions for health-related research questions. An analysis of nationwide studies that applied geographic information systems underlines the potential this instrument bears for health monitoring in Germany. Geographic information systems provide up-to-date mapping and visualization options to be used for national health monitoring at the Robert Koch Institute (RKI). Furthermore, objective information on the residential environment as an influencing factor on population health and on health behavior can be gathered and linked to RKI survey data at different geographic scales. Besides using physical information, such as climate, vegetation or land use, as well as information on the built environment, the instrument can link socioeconomic and sociodemographic data as well as information on health care and environmental stress to the survey data and integrate them into concepts for analyses. Therefore, geographic information systems expand the potential of the RKI to present nationwide, representative and meaningful health-monitoring results. In doing so, data protection regulations must always be followed. To conclude, the development of a national spatial data infrastructure and the identification of important data sources can prospectively improve access to high quality data sets that are relevant for the health monitoring.

  19. To track or not to track: user reactions to concepts in longitudinal health monitoring.

    Science.gov (United States)

    Beaudin, Jennifer S; Intille, Stephen S; Morris, Margaret E

    2006-01-01

    Advances in ubiquitous computing, smart homes, and sensor technologies enable novel, longitudinal health monitoring applications in the home. Many home monitoring technologies have been proposed to detect health crises, support aging-in-place, and improve medical care. Health professionals and potential end users in the lay public, however, sometimes question whether home health monitoring is justified given the cost and potential invasion of privacy. The aim of the study was to elicit specific feedback from health professionals and laypeople about how they might use longitudinal health monitoring data for proactive health and well-being. Interviews were conducted with 8 health professionals and 26 laypeople. Participants were asked to evaluate mock data visualization displays that could be generated by novel home monitoring systems. The mock displays were used to elicit reactions to longitudinal monitoring in the home setting as well as what behaviors, events, and physiological indicators people were interested in tracking. Based on the qualitative data provided by the interviews, lists of benefits of and concerns about health tracking from the perspectives of the practitioners and laypeople were compiled. Variables of particular interest to the interviewees, as well as their specific ideas for applications of collected data, were documented. Based upon these interviews, we recommend that ubiquitous "monitoring" systems may be more readily adopted if they are developed as tools for personalized, longitudinal self-investigation that help end users learn about the conditions and variables that impact their social, cognitive, and physical health.

  20. Effects of payment method on work control, work risk and work-related musculoskeletal health among sewing machine operators

    Directory of Open Access Journals (Sweden)

    R. Nawawi

    2015-12-01

    Full Text Available Effects of payment method on work control, work risk and work-related musculoskeletal health among sewing machine operators R. Nawawi1, B.M. Deros1*, D.D.I. Daruis2, A. Ramli3, R.M. Zein4 and L.H. Joseph3 1Dept. of Mechanical and Materials Engineering Faculty of Engineering & Built Environment Universiti Kebangsaan Malaysia, Malaysia *Email: hjbaba@ukm.edu.my 2Faculty of Engineering, Universiti Pertahanan Nasional Malaysia, Malaysia 3Department of Physiotherapy Faculty of Science, Lincoln University College, Malaysia 4Department of Consultation, Research & Development, National Institute of Occupational Safety and Health (NIOSH, Malaysia ABSTRACT This study aimed to identify payment method and its effects on work control, work risk and work-related musculoskeletal health among Malaysian sewing machine operators. The study sample comprised 337 sewing machine operators (male, n=122, female, n=215; aged between 18-54 years old; mean 30.74±8.44 from four different garment-making companies in Malaysia. They were being paid via time rate wages (n=246 and piece rate wages (n=91. Data was collected through Nordic Musculoskeletal Questionnaire and pen-and-paper assessment via Rapid Upper Limb Assessment (RULA. From the study, the piece rate wage group was found to take fewer breaks, had high work production demands, worked at a faster pace and experienced more exhaustion and pressure due to increasing work demands as compared to the time rate group. They were also observed working with higher physical exposure such as repetitive tasks, awkward static postures, awkward grips and hand movements, pulling, lifting and pushing as compared to those in the time rate wage group. The final RULA scores was also higher from the piece rate wage group (72.53% RULA score 7 which indicated higher work risks among them. The study found that the type of wage payment was significantly associated with work risks (p=0.036, df=1 and WRMSD at the shoulder, lower back

  1. Health Monitor for Multitasking, Safety-Critical, Real-Time Software

    Science.gov (United States)

    Zoerner, Roger

    2011-01-01

    Health Manager can detect Bad Health prior to a failure occurring by periodically monitoring the application software by looking for code corruption errors, and sanity-checking each critical data value prior to use. A processor s memory can fail and corrupt the software, or the software can accidentally write to the wrong address and overwrite the executing software. This innovation will continuously calculate a checksum of the software load to detect corrupted code. This will allow a system to detect a failure before it happens. This innovation monitors each software task (thread) so that if any task reports "bad health," or does not report to the Health Manager, the system is declared bad. The Health Manager reports overall system health to the outside world by outputting a square wave signal. If the square wave stops, this indicates that system health is bad or hung and cannot report. Either way, "bad health" can be detected, whether caused by an error, corrupted data, or a hung processor. A separate Health Monitor Task is started and run periodically in a loop that starts and stops pending on a semaphore. Each monitored task registers with the Health Manager, which maintains a count for the task. The registering task must indicate if it will run more or less often than the Health Manager. If the task runs more often than the Health Manager, the monitored task calls a health function that increments the count and verifies it did not go over max-count. When the periodic Health Manager runs, it verifies that the count did not go over the max-count and zeroes it. If the task runs less often than the Health Manager, the periodic Health Manager will increment the count. The monitored task zeroes the count, and both the Health Manager and monitored task verify that the count did not go over the max-count.

  2. A power supply design of body sensor networks for health monitoring of neonates

    NARCIS (Netherlands)

    Chen, W.; Sonntag, C.L.W.; Boesten, F.; Bambang Oetomo, S.; Feijs, L.M.G.

    2008-01-01

    Critically ill new born babies are extremely tiny and vulnerable to external disturbance. Non-invasive health monitoring with body sensor networks is crucial for the survival of these neonates and the quality of their life later on. A key question for health monitoring with body sensor networks is

  3. Tunable Laser Development for In-flight Fiber Optic Based Structural Health Monitoring Systems

    Science.gov (United States)

    Richards, Lance; Parker, Allen; Chan, Patrick

    2014-01-01

    The objective of this task is to investigate, develop, and demonstrate a low-cost swept lasing light source for NASA DFRC's fiber optics sensing system (FOSS) to perform structural health monitoring on current and future aerospace vehicles. This is the regular update of the Tunable Laser Development for In-flight Fiber Optic Based Structural Health Monitoring Systems website.

  4. Localizing the HL7 Personal Health Monitoring Record for Danish Telemedicine

    DEFF Research Database (Denmark)

    Christensen, Henrik Bærbak

    2014-01-01

    Telemedicine holds a promise of lowering cost in health care and improving the life quality of chronic ill patients by allowing monitoring in the home. The Personal Health Monitoring Record (PHMR) is an international HL7 standard data format for encoding measurements made by devices in the home...

  5. Monitoring and Analysis of Ground Settlement Induced by Tunnelling with Slurry Pressure-Balanced Tunnel Boring Machine

    Directory of Open Access Journals (Sweden)

    Hyunku Park

    2018-01-01

    Full Text Available A case study of monitoring and analysis of ground settlement caused by tunnelling of stacked twin tunnels for underground metro line construction through the densely populated area using the slurry pressure-balanced TBM is presented. Detailed ground settlement monitoring was carried out for the initial stage of down-track tunnelling in order to estimate trough width factor and volume losses including face, shield, and tail losses. In addition, using the gap model, prediction of volume loss and ground settlement was carried out with consideration of the ground condition, TBM configurations, and actual operation data. The predictions of the gap model were compared with the observed results, and adjustment factors were determined for volume loss estimation. The adjusted factors were applied to predict ground settlement of the up-track tunnel, and its results were compared with the field measurements.

  6. Automated Impedance Tomography for Monitoring Permeable Reactive Barrier Health

    Energy Technology Data Exchange (ETDEWEB)

    LaBrecque, D J; Adkins, P L

    2009-07-02

    The objective of this research was the development of an autonomous, automated electrical geophysical monitoring system which allows for near real-time assessment of Permeable Reactive Barrier (PRB) health and aging and which provides this assessment through a web-based interface to site operators, owners and regulatory agencies. Field studies were performed at four existing PRB sites; (1) a uranium tailing site near Monticello, Utah, (2) the DOE complex at Kansas City, Missouri, (3) the Denver Federal Center in Denver, Colorado and (4) the Asarco Smelter site in East Helena, Montana. Preliminary surface data over the PRB sites were collected (in December, 2005). After the initial round of data collection, the plan was modified to include studies inside the barriers in order to better understand barrier aging processes. In September 2006 an autonomous data collection system was designed and installed at the EPA PRB and the electrode setups in the barrier were revised and three new vertical electrode arrays were placed in dedicated boreholes which were in direct contact with the PRB material. Final data were collected at the Kansas City, Denver and Monticello, Utah PRB sites in the fall of 2007. At the Asarco Smelter site in East Helena, Montana, nearly continuous data was collected by the autonomous monitoring system from June 2006 to November 2007. This data provided us with a picture of the evolution of the barrier, enabling us to examine barrier changes more precisely and determine whether these changes are due to installation issues or are normal barrier aging. Two rounds of laboratory experiments were carried out during the project. We conducted column experiments to investigate the effect of mineralogy on the electrical signatures resulting from iron corrosion and mineral precipitation in zero valent iron (ZVI) columns. In the second round of laboratory experiments we observed the electrical response from simulation of actual field PRBs at two sites: the

  7. In-situ monitoring of blood glucose level for dialysis machine by AAA-battery-size ATR Fourier spectroscopy

    Science.gov (United States)

    Hosono, Satsuki; Sato, Shun; Ishida, Akane; Suzuki, Yo; Inohara, Daichi; Nogo, Kosuke; Abeygunawardhana, Pradeep K.; Suzuki, Satoru; Nishiyama, Akira; Wada, Kenji; Ishimaru, Ichiro

    2015-07-01

    For blood glucose level measurement of dialysis machines, we proposed AAA-battery-size ATR (Attenuated total reflection) Fourier spectroscopy in middle infrared light region. The proposed one-shot Fourier spectroscopic imaging is a near-common path and spatial phase-shift interferometer with high time resolution. Because numerous number of spectral data that is 60 (= camera frame rare e.g. 60[Hz]) multiplied by pixel number could be obtained in 1[sec.], statistical-averaging improvement realize high-accurate spectral measurement. We evaluated the quantitative accuracy of our proposed method for measuring glucose concentration in near-infrared light region with liquid cells. We confirmed that absorbance at 1600[nm] had high correlations with glucose concentrations (correlation coefficient: 0.92). But to measure whole-blood, complex light phenomenon caused from red blood cells, that is scattering and multiple reflection or so, deteriorate spectral data. Thus, we also proposed the ultrasound-assisted spectroscopic imaging that traps particles at standing-wave node. Thus, if ATR prism is oscillated mechanically, anti-node area is generated around evanescent light field on prism surface. By elimination complex light phenomenon of red blood cells, glucose concentration in whole-blood will be quantify with high accuracy. In this report, we successfully trapped red blood cells in normal saline solution with ultrasonic standing wave (frequency: 2[MHz]).

  8. National Surveys of Population Health: Big Data Analytics for Mobile Health Monitors.

    Science.gov (United States)

    Schatz, Bruce R

    2015-12-01

    At the core of the healthcare crisis is fundamental lack of actionable data. Such data could stratify individuals within populations to predict which persons have which outcomes. If baselines existed for all variations of all conditions, then managing health could be improved by matching the measuring of individuals to their cohort in the population. The scale required for complete baselines involves effective National Surveys of Population Health (NSPH). Traditionally, these have been focused upon acute medicine, measuring people to contain the spread of epidemics. In recent decades, the focus has moved to chronic conditions as well, which require smaller measures over longer times. NSPH have long utilized quality of life questionnaires. Mobile Health Monitors, where computing technologies eliminate manual administration, provide richer data sets for health measurement. Older technologies of telephone interviews will be replaced by newer technologies of smartphone sensors to provide deeper individual measures at more frequent timings across larger-sized populations. Such continuous data can provide personal health records, supporting treatment guidelines specialized for population cohorts. Evidence-based medicine will become feasible by leveraging hundreds of millions of persons carrying mobile devices interacting with Internet-scale services for Big Data Analytics.

  9. Low-Cost, Distributed Environmental Monitors for Factory Worker Health

    Directory of Open Access Journals (Sweden)

    Geb W. Thomas

    2018-05-01

    Full Text Available An integrated network of environmental monitors was developed to continuously measure several airborne hazards in a manufacturing facility. The monitors integrated low-cost sensors to measure particulate matter, carbon monoxide, ozone and nitrogen dioxide, noise, temperature and humidity. The monitors were developed and tested in situ for three months in several overlapping deployments, before a full cohort of 40 was deployed in a heavy vehicle manufacturing facility for a year of data collection. The monitors collect data from each sensor and report them to a central database every 5 min. The work includes an experimental validation of the particle, gas and noise monitors. The R2 for the particle sensor ranges between 0.98 and 0.99 for particle mass densities up to 300 μg/m3. The R2 for the carbon monoxide sensor is 0.99 for concentrations up to 15 ppm. The R2 for the oxidizing gas sensor is 0.98 over the sensitive range from 20 to 180 ppb. The noise monitor is precise within 1% between 65 and 95 dBA. This work demonstrates the capability of distributed monitoring as a means to examine exposure variability in both space and time, building an important preliminary step towards a new approach for workplace hazard monitoring.

  10. Assessment of Machine Learning Algorithms for Automatic Benthic Cover Monitoring and Mapping Using Towed Underwater Video Camera and High-Resolution Satellite Images

    Directory of Open Access Journals (Sweden)

    Hassan Mohamed

    2018-05-01

    Full Text Available Benthic habitat monitoring is essential for many applications involving biodiversity, marine resource management, and the estimation of variations over temporal and spatial scales. Nevertheless, both automatic and semi-automatic analytical methods for deriving ecologically significant information from towed camera images are still limited. This study proposes a methodology that enables a high-resolution towed camera with a Global Navigation Satellite System (GNSS to adaptively monitor and map benthic habitats. First, the towed camera finishes a pre-programmed initial survey to collect benthic habitat videos, which can then be converted to geo-located benthic habitat images. Second, an expert labels a number of benthic habitat images to class habitats manually. Third, attributes for categorizing these images are extracted automatically using the Bag of Features (BOF algorithm. Fourth, benthic cover categories are detected automatically using Weighted Majority Voting (WMV ensembles for Support Vector Machines (SVM, K-Nearest Neighbor (K-NN, and Bagging (BAG classifiers. Fifth, WMV-trained ensembles can be used for categorizing more benthic cover images automatically. Finally, correctly categorized geo-located images can provide ground truth samples for benthic cover mapping using high-resolution satellite imagery. The proposed methodology was tested over Shiraho, Ishigaki Island, Japan, a heterogeneous coastal area. The WMV ensemble exhibited 89% overall accuracy for categorizing corals, sediments, seagrass, and algae species. Furthermore, the same WMV ensemble produced a benthic cover map using a Quickbird satellite image with 92.7% overall accuracy.

  11. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

    Directory of Open Access Journals (Sweden)

    Wei Luo

    Full Text Available For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD outcomes (four NCDs and two major clinical risk factors, based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88 and those excluded from the development for use as a completely separated validation sample (median correlation 0.85, demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

  12. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

    Science.gov (United States)

    Luo, Wei; Nguyen, Thin; Nichols, Melanie; Tran, Truyen; Rana, Santu; Gupta, Sunil; Phung, Dinh; Venkatesh, Svetha; Allender, Steve

    2015-01-01

    For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

  13. RISMA: A Rule-based Interval State Machine Algorithm for Alerts Generation, Performance Analysis and Monitoring Real-Time Data Processing

    Science.gov (United States)

    Laban, Shaban; El-Desouky, Aly

    2013-04-01

    The monitoring of real-time systems is a challenging and complicated process. So, there is a continuous need to improve the monitoring process through the use of new intelligent techniques and algorithms for detecting exceptions, anomalous behaviours and generating the necessary alerts during the workflow monitoring of such systems. The interval-based or period-based theorems have been discussed, analysed, and used by many researches in Artificial Intelligence (AI), philosophy, and linguistics. As explained by Allen, there are 13 relations between any two intervals. Also, there have also been many studies of interval-based temporal reasoning and logics over the past decades. Interval-based theorems can be used for monitoring real-time interval-based data processing. However, increasing the number of processed intervals makes the implementation of such theorems a complex and time consuming process as the relationships between such intervals are increasing exponentially. To overcome the previous problem, this paper presents a Rule-based Interval State Machine Algorithm (RISMA) for processing, monitoring, and analysing the behaviour of interval-based data, received from real-time sensors. The proposed intelligent algorithm uses the Interval State Machine (ISM) approach to model any number of interval-based data into well-defined states as well as inferring them. An interval-based state transition model and methodology are presented to identify the relationships between the different states of the proposed algorithm. By using such model, the unlimited number of relationships between similar large numbers of intervals can be reduced to only 18 direct relationships using the proposed well-defined states. For testing the proposed algorithm, necessary inference rules and code have been designed and applied to the continuous data received in near real-time from the stations of International Monitoring System (IMS) by the International Data Centre (IDC) of the Preparatory

  14. Efficient color correction method for smartphone camera-based health monitoring application.

    Science.gov (United States)

    Duc Dang; Chae Ho Cho; Daeik Kim; Oh Seok Kwon; Jo Woon Chong

    2017-07-01

    Smartphone health monitoring applications are recently highlighted due to the rapid development of hardware and software performance of smartphones. However, color characteristics of images captured by different smartphone models are dissimilar each other and this difference may give non-identical health monitoring results when the smartphone health monitoring applications monitor physiological information using their embedded smartphone cameras. In this paper, we investigate the differences in color properties of the captured images from different smartphone models and apply a color correction method to adjust dissimilar color values obtained from different smartphone cameras. Experimental results show that the color corrected images using the correction method provide much smaller color intensity errors compared to the images without correction. These results can be applied to enhance the consistency of smartphone camera-based health monitoring applications by reducing color intensity errors among the images obtained from different smartphones.

  15. Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks.

    Science.gov (United States)

    Dawadi, Prafulla N; Cook, Diane J; Schmitter-Edgecombe, Maureen

    2013-11-01

    One of the many services that intelligent systems can provide is the automated assessment of resident well-being. We hypothesize that the functional health of individuals, or ability of individuals to perform activities independently without assistance, can be estimated by tracking their activities using smart home technologies. In this paper, we introduce a machine learning-based method for assessing activity quality in smart homes. To validate our approach we quantify activity quality for 179 volunteer participants who performed a complex, interweaved set of activities in our smart home apartment. We observed a statistically significant correlation (r=0.79) between automated assessment of task quality and direct observation scores. Using machine learning techniques to predict the cognitive health of the participants based on task quality is accomplished with an AUC value of 0.64. We believe that this capability is an important step in understanding everyday functional health of individuals in their home environments.

  16. FLEXURAL TESTING MACHINE AS AN OFF-LINE CONTROL SYSTEM FOR QUALITY MONITORING IN THE PRODUCTION OF BENDED CERAMIC TILES

    Directory of Open Access Journals (Sweden)

    Cristiano Fragassa

    2016-06-01

    Full Text Available The capability to bend in a controlled manner Gres Porcelain stoneware tiles passing by a very exclusive process of pyroplastic deformation opens up entirely new opportunities in utilisation of this important family of ceramics. A bended tile can be exploited in innovative applications, such as stairs, shelves, benches and even radiators, turning this element from a simple piece of furnishing in a modern functional component. But this change in functionality also requires a different approach in the quality control, both at the product and process levels, that can no longer be limited to the use of tests specified in the regulations for traditional ceramics (e.g. colour, porosity, hygroscopic .... This article describes the first device so far devised for the verification of resistance to bending of curved tiles, discussing the correct way of use. The adoption of this particular equipment as an off-line control device can represent a valid strategy for monitoring the product and process quality.

  17. Applications in bridge structure health monitoring using distributed fiber sensing

    Science.gov (United States)

    Feng, Yafei; Zheng, Huan; Ge, Huiliang

    2017-10-01

    In this paper, Brillouin Optical Time Domain Analysis (BOTDA) is proposed to solve the problem that the traditional point sensor is difficult to realize the comprehensive safety monitoring of bridges and so on. This technology not only breaks through the bottleneck of traditional monitoring point sensor, realize the distributed measurement of temperature and strain on a transmission path; can also be used for bridge and other structures of the damage identification, fracture positioning, settlement monitoring. The effectiveness and frontier of the technology are proved by comparing the test of the indoor model beam and the external field bridge, and the significance of the distributed optical fiber sensing technology to the monitoring of the important structure of the bridge is fully explained.

  18. An online substructure identification method for local structural health monitoring

    International Nuclear Information System (INIS)

    Hou, Jilin; Ou, Jinping; Jankowski, Łukasz

    2013-01-01

    This paper proposes a substructure isolation method, which uses time series of measured local response for online monitoring of substructures. The proposed monitoring process consists of two key steps: construction of the isolated substructure, and its identification. The isolated substructure is an independent virtual structure, which is numerically isolated from the global structure by placing virtual supports on the interface. First, the isolated substructure is constructed by a specific linear combination of time series of its measured local responses. Then, the isolated substructure is identified using its local natural frequencies extracted from the combined responses. The substructure is assumed to be linear; the outside part of the global structure can have any characteristics. The method has no requirements on the initial state of the structure, and so the process can be carried out repetitively for online monitoring. Online isolation and monitoring is illustrated in a numerical example with a frame model, and then verified in a cantilever beam experiment. (paper)

  19. System health monitoring using multiple-model adaptive estimation techniques

    Science.gov (United States)

    Sifford, Stanley Ryan

    Monitoring system health for fault detection and diagnosis by tracking system parameters concurrently with state estimates is approached using a new multiple-model adaptive estimation (MMAE) method. This novel method is called GRid-based Adaptive Parameter Estimation (GRAPE). GRAPE expands existing MMAE methods by using new techniques to sample the parameter space. GRAPE expands on MMAE with the hypothesis that sample models can be applied and resampled without relying on a predefined set of models. GRAPE is initially implemented in a linear framework using Kalman filter models. A more generalized GRAPE formulation is presented using extended Kalman filter (EKF) models to represent nonlinear systems. GRAPE can handle both time invariant and time varying systems as it is designed to track parameter changes. Two techniques are presented to generate parameter samples for the parallel filter models. The first approach is called selected grid-based stratification (SGBS). SGBS divides the parameter space into equally spaced strata. The second approach uses Latin Hypercube Sampling (LHS) to determine the parameter locations and minimize the total number of required models. LHS is particularly useful when the parameter dimensions grow. Adding more parameters does not require the model count to increase for LHS. Each resample is independent of the prior sample set other than the location of the parameter estimate. SGBS and LHS can be used for both the initial sample and subsequent resamples. Furthermore, resamples are not required to use the same technique. Both techniques are demonstrated for both linear and nonlinear frameworks. The GRAPE framework further formalizes the parameter tracking process through a general approach for nonlinear systems. These additional methods allow GRAPE to either narrow the focus to converged values within a parameter range or expand the range in the appropriate direction to track the parameters outside the current parameter range boundary

  20. Smart homes and home health monitoring technologies for older adults: A systematic review.

    Science.gov (United States)

    Liu, Lili; Stroulia, Eleni; Nikolaidis, Ioanis; Miguel-Cruz, Antonio; Rios Rincon, Adriana

    2016-07-01

    Around the world, populations are aging and there is a growing concern about ways that older adults can maintain their health and well-being while living in their homes. The aim of this paper was to conduct a systematic literature review to determine: (1) the levels of technology readiness among older adults and, (2) evidence for smart homes and home-based health-monitoring technologies that support aging in place for older adults who have complex needs. We identified and analyzed 48 of 1863 relevant papers. Our analyses found that: (1) technology-readiness level for smart homes and home health monitoring technologies is low; (2) the highest level of evidence is 1b (i.e., one randomized controlled trial with a PEDro score ≥6); smart homes and home health monitoring technologies are used to monitor activities of daily living, cognitive decline and mental health, and heart conditions in older adults with complex needs; (3) there is no evidence that smart homes and home health monitoring technologies help address disability prediction and health-related quality of life, or fall prevention; and (4) there is conflicting evidence that smart homes and home health monitoring technologies help address chronic obstructive pulmonary disease. The level of technology readiness for smart homes and home health monitoring technologies is still low. The highest level of evidence found was in a study that supported home health technologies for use in monitoring activities of daily living, cognitive decline, mental health, and heart conditions in older adults with complex needs. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  1. Capacity building for health inequality monitoring in Indonesia: enhancing the equity orientation of country health information system.

    Science.gov (United States)

    Hosseinpoor, Ahmad Reza; Nambiar, Devaki; Tawilah, Jihane; Schlotheuber, Anne; Briot, Benedicte; Bateman, Massee; Davey, Tamzyn; Kusumawardani, Nunik; Myint, Theingi; Nuryetty, Mariet Tetty; Prasetyo, Sabarinah; Suparmi; Floranita, Rustini

    Inequalities in health represent a major problem in many countries, including Indonesia. Addressing health inequality is a central component of the Sustainable Development Goals and a priority of the World Health Organization (WHO). WHO provides technical support for health inequality monitoring among its member states. Following a capacity-building workshop in the WHO South-East Asia Region in 2014, Indonesia expressed interest in incorporating health-inequality monitoring into its national health information system. This article details the capacity-building process for national health inequality monitoring in Indonesia, discusses successes and challenges, and how this process may be adapted and implemented in other countries/settings. We outline key capacity-building activities undertaken between April 2016 and December 2017 in Indonesia and present the four key outcomes of this process. The capacity-building process entailed a series of workshops, meetings, activities, and processes undertaken between April 2016 and December 2017. At each stage, a range of stakeholders with access to the relevant data and capacity for data analysis, interpretation and reporting was engaged with, under the stewardship of state agencies. Key steps to strengthening health inequality monitoring included capacity building in (1) identification of the health topics/areas of interest, (2) mapping data sources and identifying gaps, (3) conducting equity analyses using raw datasets, and (4) interpreting and reporting inequality results. As a result, Indonesia developed its first national report on the state of health inequality. A number of peer-reviewed manuscripts on various aspects of health inequality in Indonesia have also been developed. The capacity-building process undertaken in Indonesia is designed to be adaptable to other contexts. Capacity building for health inequality monitoring among countries is a critical step for strengthening equity-oriented national health

  2. Personalized Health Monitoring System for Managing Well-Being in Rural Areas.

    Science.gov (United States)

    Nedungadi, Prema; Jayakumar, Akshay; Raman, Raghu

    2017-12-14

    Rural India lacks easy access to health practitioners and medical centers, depending instead on community health workers. In these areas, common ailments that are easy to manage with medicines, often lead to medical escalations and even fatalities due to lack of awareness and delayed diagnosis. The introduction of wearable health devices has made it easier to monitor health conditions and to connect doctors and patients in urban areas. However, existing initiatives have not succeeded in providing adequate health monitoring to rural and low-literate patients, as current methods are expensive, require consistent connectivity and expect literate users. Our design considerations address these concerns by providing low-cost medical devices connected to a low-cost health platform, along with personalized guidance based on patient physiological parameters in local languages, and alerts to medical practitioners in case of emergencies. This patient-centric integrated healthcare system is designed to manage the overall health of villagers with real-time health monitoring of patients, to offer guidance on preventive care, and to increase health awareness and self-monitoring at an affordable price. This personalized health monitoring system addresses the health-related needs in remote and rural areas by (1) empowering health workers in monitoring of basic health conditions for rural patients in order to prevent escalations, (2) personalized feedback regarding nutrition, exercise, diet, preventive Ayurveda care and yoga postures based on vital parameters and (3) reporting of patient data to the patient's health center with emergency alerts to doctor and patient. The system supports community health workers in the diagnostic procedure, management, and reporting of rural patients, and functions well even with only intermittent access to Internet.

  3. Vehicle Remote Health Monitoring and Prognostic Maintenance System

    Directory of Open Access Journals (Sweden)

    Uferah Shafi

    2018-01-01

    Full Text Available In many industries inclusive of automotive vehicle industry, predictive maintenance has become more important. It is hard to diagnose failure in advance in the vehicle industry because of the limited availability of sensors and some of the designing exertions. However with the great development in automotive industry, it looks feasible today to analyze sensor’s data along with machine learning techniques for failure prediction. In this article, an approach is presented for fault prediction of four main subsystems of vehicle, fuel system, ignition system, exhaust system, and cooling system. Sensor is collected when vehicle is on the move, both in faulty condition (when any failure in specific system has occurred and in normal condition. The data is transmitted to the server which analyzes the data. Interesting patterns are learned using four classifiers, Decision Tree, Support Vector Machine, K Nearest Neighbor, and Random Forest. These patterns are later used to detect future failures in other vehicles which show the similar behavior. The approach is produced with the end goal of expanding vehicle up-time and was demonstrated on 70 vehicles of Toyota Corolla type. Accuracy comparison of all classifiers is performed on the basis of Receiver Operating Characteristics (ROC curves.

  4. Machine Protection

    International Nuclear Information System (INIS)

    Schmidt, R

    2014-01-01

    The protection of accelerator equipment is as old as accelerator technology and was for many years related to high-power equipment. Examples are the protection of powering equipment from overheating (magnets, power converters, high-current cables), of superconducting magnets from damage after a quench and of klystrons. The protection of equipment from beam accidents is more recent. It is related to the increasing beam power of high-power proton accelerators such as ISIS, SNS, ESS and the PSI cyclotron, to the emission of synchrotron light by electron–positron accelerators and FELs, and to the increase of energy stored in the beam (in particular for hadron colliders such as LHC). Designing a machine protection system requires an excellent understanding of accelerator physics and operation to anticipate possible failures that could lead to damage. Machine protection includes beam and equipment monitoring, a system to safely stop beam operation (e.g. dumping the beam or stopping the beam at low energy) and an interlock system providing the glue between these systems. The most recent accelerator, the LHC, will operate with about 3 × 10 14 protons per beam, corresponding to an energy stored in each beam of 360 MJ. This energy can cause massive damage to accelerator equipment in case of uncontrolled beam loss, and a single accident damaging vital parts of the accelerator could interrupt operation for years. This article provides an overview of the requirements for protection of accelerator equipment and introduces the various protection systems. Examples are mainly from LHC, SNS and ESS

  5. Improving health in the community: a role for performance monitoring

    National Research Council Canada - National Science Library

    Durch, Jane; Bailey, Linda A; Stoto, Michael A

    How do communities protect and improve the health of their populations? Health care is part of the answer but so are environmental protections, social and educational services, adequate nutrition, and a host of other activities...

  6. Sustainable machining

    CERN Document Server

    2017-01-01

    This book provides an overview on current sustainable machining. Its chapters cover the concept in economic, social and environmental dimensions. It provides the reader with proper ways to handle several pollutants produced during the machining process. The book is useful on both undergraduate and postgraduate levels and it is of interest to all those working with manufacturing and machining technology.

  7. Electrical machines diagnosis

    CERN Document Server

    Trigeassou, Jean-Claude

    2013-01-01

    Monitoring and diagnosis of electrical machine faults is a scientific and economic issue which is motivated by objectives for reliability and serviceability in electrical drives.This book provides a survey of the techniques used to detect the faults occurring in electrical drives: electrical, thermal and mechanical faults of the electrical machine, faults of the static converter and faults of the energy storage unit.Diagnosis of faults occurring in electrical drives is an essential part of a global monitoring system used to improve reliability and serviceability. This diagnosis is perf

  8. Predicting Health Care Utilization After the First Behavioral Health Visit Using Natural Language Processing and Machine Learning

    OpenAIRE

    Roysden, Nathaniel

    2016-01-01

    Mental health problems are an independent predictor of increased healthcare utilization. We created random forest classifiers for predicting two outcomes following a patient’s first behavioral health encounter: decreased utilization by any amount (AUROC 0.74) and ultra-high absolute utilization (AUROC 0.88). These models may be used for clinical decision support by referring providers, to automatically detect patients who may benefit from referral, for cost management, or for risk/protection ...

  9. A simple and reliable health monitoring system for shoulder health: proposal.

    Science.gov (United States)

    Liu, Shuo-Fang; Lee, Yann-Long

    2014-02-26

    The current health care system is complex and inefficient. A simple and reliable health monitoring system that can help patients perform medical self-diagnosis is seldom readily available. Because the medical system is vast and complex, it has hampered or delayed patients in seeking medical advice or treatment in a timely manner, which may potentially affect the patient's chances of recovery, especially those with severe sicknesses such as cancer, and heart disease. The purpose of this paper is to propose a methodology in designing a simple, low cost, Internet-based health-screening platform. This health-screening platform will enable patients to perform medical self-diagnosis over the Internet. Historical data has shown the importance of early detection to ensure patients receive proper treatment and speedy recovery. The platform is designed with special emphasis on the user interface. Standard Web-based user-interface design is adopted so the user feels ease to operate in a familiar Web environment. In addition, graphics such as charts and graphs are used generously to help users visualize and understand the result of the diagnostic. The system is developed using hypertext preprocessor (PHP) programming language. One important feature of this system platform is that it is built to be a stand-alone platform, which tends to have better user privacy security. The prototype system platform was developed by the National Cheng Kung University Ergonomic and Design Laboratory. The completed prototype of this system platform was submitted to the Taiwan Medical Institute for evaluation. The evaluation of 120 participants showed that this platform system is a highly effective tool in health-screening applications, and has great potential for improving the medical care quality for the general public.

  10. A Simple and Reliable Health Monitoring System For Shoulder Health: Proposal

    Science.gov (United States)

    Lee, Yann-Long

    2014-01-01

    Background The current health care system is complex and inefficient. A simple and reliable health monitoring system that can help patients perform medical self-diagnosis is seldom readily available. Because the medical system is vast and complex, it has hampered or delayed patients in seeking medical advice or treatment in a timely manner, which may potentially affect the patient’s chances of recovery, especially those with severe sicknesses such as cancer, and heart disease. Objective The purpose of this paper is to propose a methodology in designing a simple, low cost, Internet-based health-screening platform. Methods This health-screening platform will enable patients to perform medical self-diagnosis over the Internet. Historical data has shown the importance of early detection to ensure patients receive proper treatment and speedy recovery. Results The platform is designed with special emphasis on the user interface. Standard Web-based user-interface design is adopted so the user feels ease to operate in a familiar Web environment. In addition, graphics such as charts and graphs are used generously to help users visualize and understand the result of the diagnostic. The system is developed using hypertext preprocessor (PHP) programming language. One important feature of this system platform is that it is built to be a stand-alone platform, which tends to have better user privacy security. The prototype system platform was developed by the National Cheng Kung University Ergonomic and Design Laboratory. Conclusions The completed prototype of this system platform was submitted to the Taiwan Medical Institute for evaluation. The evaluation of 120 participants showed that this platform system is a highly effective tool in health-screening applications, and has great potential for improving the medical care quality for the general public. PMID:24571980

  11. Using Social Robots in Health Settings: Implications of Personalization on Human-Machine Communication

    Directory of Open Access Journals (Sweden)

    Lisa Tam and Rajiv Khosla

    2016-09-01

    Full Text Available In view of the shortage of healthcare workers and a growing aging population, it is worthwhile to explore the applicability of new technologies in improving the quality of healthcare and reducing its cost. However, it remains a challenge to deploy such technologies in environments where individuals have limited knowledge about how to use them. Thus, this paper explores how the social robots designed for use in health settings in Australia have sought to overcome some of the limitations through personalization. Deployed in aged care and home-based care facilities, the social robots are person-centered, emphasizing the personalization of care with human-like attributes (e.g., human appearances to engage in reciprocal communication with users. While there have been debates over the advantages and disadvantages of personalization, this paper discusses the implications of personalization on the design of the robots for enhancing engagement, empowerment and enablement in health settings.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-03-15

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

  13. Machine learning approach for automatic quality criteria detection of health web pages.

    Science.gov (United States)

    Gaudinat, Arnaud; Grabar, Natalia; Boyer, Célia

    2007-01-01

    The number of medical websites is constantly growing [1]. Owing to the open nature of the Web, the reliability of information available on the Web is uneven. Internet users are overwhelmed by the quantity of information available on the Web. The situation is even more critical in the medical area, as the content proposed by health websites can have a direct impact on the users' well being. One way to control the reliability of health websites is to assess their quality and to make this assessment available to users. The HON Foundation has defined a set of eight ethical principles. HON's experts are working in order to manually define whether a given website complies with s the required principles. As the number of medical websites is constantly growing, manual expertise becomes insufficient and automatic systems should be used in order to help medical experts. In this paper we present the design and the evaluation of an automatic system conceived for the categorisation of medical and health documents according to he HONcode ethical principles. A first evaluation shows promising results. Currently the system shows 0.78 micro precision and 0.73 F-measure, with 0.06 errors.

  14. The design of an m-Health monitoring system based on a cloud computing platform

    Science.gov (United States)

    Xu, Boyi; Xu, Lida; Cai, Hongming; Jiang, Lihong; Luo, Yang; Gu, Yizhi

    2017-01-01

    Compared to traditional medical services provided within hospitals, m-Health monitoring systems (MHMSs) face more challenges in personalised health data processing. To achieve personalised and high-quality health monitoring by means of new technologies, such as mobile network and cloud computing, in this paper, a framework of an m-Health monitoring system based on a cloud computing platform (Cloud-MHMS) is designed to implement pervasive health monitoring. Furthermore, the modules of the framework, which are Cloud Storage and Multiple Tenants Access Control Layer, Healthcare Data Annotation Layer, and Healthcare Data Analysis Layer, are discussed. In the data storage layer, a multiple tenant access method is designed to protect patient privacy. In the data annotation layer, linked open data are adopted to augment health data interoperability semantically. In the data analysis layer, the process mining algorithm and similarity calculating method are implemented to support personalised treatment plan selection. These three modules cooperate to implement the core functions in the process of health monitoring, which are data storage, data processing, and data analysis. Finally, we study the application of our architecture in the monitoring of antimicrobial drug usage to demonstrate the usability of our method in personal healthcare analysis.

  15. Flexible, Stretchable Sensors for Wearable Health Monitoring: Sensing Mechanisms, Materials, Fabrication Strategies and Features

    Science.gov (United States)

    Liu, Yan; Wang, Hai; Zhao, Wei; Qin, Hongbo; Xie, Yongqiang

    2018-01-01

    Wearable health monitoring systems have gained considerable interest in recent years owing to their tremendous promise for personal portable health watching and remote medical practices. The sensors with excellent flexibility and stretchability are crucial components that can provide health monitoring systems with the capability of continuously tracking physiological signals of human body without conspicuous uncomfortableness and invasiveness. The signals acquired by these sensors, such as body motion, heart rate, breath, skin temperature and metabolism parameter, are closely associated with personal health conditions. This review attempts to summarize the recent progress in flexible and stretchable sensors, concerning the detected health indicators, sensing mechanisms, functional materials, fabrication strategies, basic and desired features. The potential challenges and future perspectives of wearable health monitoring system are also briefly discussed. PMID:29470408

  16. Bayesian updating and decision making using correlated structural health monitoring observations

    DEFF Research Database (Denmark)

    Nielsen, Jannie Sønderkær

    2018-01-01

    A Bayesian approach is often applied when updating a deterioration model using observations from expected structural health monitoring or condition monitoring. Usually, observations are assumed to be independent conditioned on the damage size, but this assumption does not always hold, especially ...... is properly modeled. In case of correlated observations, an advanced decision model using all past observations for decision making is needed to make monitoring feasible compared to only using inspections....

  17. Sustainable Development Goals for Monitoring Action to Improve Global Health.

    Science.gov (United States)

    Cesario, Sandra K

    2016-01-01

    Women and children compose the largest segment of the more than 1 billion people worldwide who are unable to access needed health care services. To address this and other global health issues, the United Nations brought together world leaders to address growing health inequities, first by establishing the Millennium Development Goals in 2000 and more recently establishing Sustainable Development Goals, which are an intergovernmental set of 17 goals consisting of 169 targets with 304 indicators to measure compliance; they were designed to be applicable to all countries. Goal number 3, "Good Health and Well-Being: Ensure Heathy Lives and Promote Well-Being for All at All Ages," includes targets to improve the health of women and newborns. © 2016 AWHONN, the Association of Women’s Health, Obstetric and Neonatal Nurses.

  18. Data-intensive structural health monitoring in the infrawatch project

    NARCIS (Netherlands)

    Veerman, R.P.; Miao, S.; Koenders, E.A.B.; Knobbe, A.

    2013-01-01

    The InfraWatch project is a Dutch research project, aimed at developing novel techniques for large-scale monitoring of concrete infra-structures. The project involves a large bridge, fitted with multiple types of sensors that capture the high-resolution dynamic behavior of the bridge. With 145

  19. A Dual-Core System Solution for Wearable Health Monitors

    NARCIS (Netherlands)

    Santana Arnaiz, O.A.; Bouwens, F.; Huisken, J.A.; De Groot, H.; Bennebroek, M.T.; Van Meerbergen, J.L.; Abbo, A.A.; Fraboulet, A.

    2011-01-01

    This paper presents a system design study for wearable sensor devices intended for healthcare and lifestyle applications based on ECG,EEG and activity monitoring. In order to meet the low-power requirement of these applications, a dual-core signal processing system is proposed which combines an

  20. On-Orbit Health Monitoring and Repair Assessment of Thermal Protection Systems, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — This SBIR project delivers On-orbit health MoNItoring and repair assessment of THERMal protection systems (OMNI_THERM). OMNI_THERM features impedance-based...