Pub Date : 2013-10-08DOI: 10.1109/ICPHM.2013.6621452
H. Ferdowsi, S. Jagannathan
In this paper, a novel decentralized detection and accommodation (FDA) methodology is proposed for interconnected nonlinear continuous-time systems by using local subsystem states alone in contrast with traditional distributed FDA schemes where the entire measured or the estimated state vector is needed. First, the detection scheme is revisited where a network of local fault detectors (LFD) is proposed. A fault is detected by generating a residual from the measured and estimated state vectors locally and the fault dynamics are estimated by using an online approximator upon detection. Subsequently, a fault accommodation scheme is initiated in the subsystem by using a second online approximator to augment the control input of each subsystem in order to minimize the effects of the faults on the overall system. Decentralization avoids the transmission of the entire system state vector to each subsystem. Finally the proposed methods are verified in the simulation environment.
{"title":"A decentralized fault accommodation scheme for nonlinear interconnected systems","authors":"H. Ferdowsi, S. Jagannathan","doi":"10.1109/ICPHM.2013.6621452","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621452","url":null,"abstract":"In this paper, a novel decentralized detection and accommodation (FDA) methodology is proposed for interconnected nonlinear continuous-time systems by using local subsystem states alone in contrast with traditional distributed FDA schemes where the entire measured or the estimated state vector is needed. First, the detection scheme is revisited where a network of local fault detectors (LFD) is proposed. A fault is detected by generating a residual from the measured and estimated state vectors locally and the fault dynamics are estimated by using an online approximator upon detection. Subsequently, a fault accommodation scheme is initiated in the subsystem by using a second online approximator to augment the control input of each subsystem in order to minimize the effects of the faults on the overall system. Decentralization avoids the transmission of the entire system state vector to each subsystem. Finally the proposed methods are verified in the simulation environment.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115563978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-24DOI: 10.1109/ICPHM.2013.6621453
H. Ferdowsi, S. Jagannathan, M. Zawodniok
Identifying and removing the outliers is important in order to make the data more trustworthy and improve the reliability of fault detection, since outliers in the measured data can cause false alarms. An online outlier identification and removal (OIR) scheme, suitable for nonlinear dynamic systems, is proposed in this paper. A neural network (NN) is utilized to estimate the actual outlier-free system states using only the measured system states which involve outliers. Outlier identification is performed online by finding the difference between measured and estimated states and comparing it with its median and standard deviation over a dynamic time window. Furthermore, the neural network weight update law is designed such that the detected outliers will not affect the state estimation. The proposed OIR scheme is then combined with fault diagnosis scheme as a preprocessing unit, in order to improve fault detection performance. A separate model-based fault detection observer is designed which uses the estimated outlier-free states to perform fault diagnosis. Finally a simple linear system is used to verify the scheme in simulations followed by a piston pump test bed study.
{"title":"A neural network based outlier identification and removal scheme","authors":"H. Ferdowsi, S. Jagannathan, M. Zawodniok","doi":"10.1109/ICPHM.2013.6621453","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621453","url":null,"abstract":"Identifying and removing the outliers is important in order to make the data more trustworthy and improve the reliability of fault detection, since outliers in the measured data can cause false alarms. An online outlier identification and removal (OIR) scheme, suitable for nonlinear dynamic systems, is proposed in this paper. A neural network (NN) is utilized to estimate the actual outlier-free system states using only the measured system states which involve outliers. Outlier identification is performed online by finding the difference between measured and estimated states and comparing it with its median and standard deviation over a dynamic time window. Furthermore, the neural network weight update law is designed such that the detected outliers will not affect the state estimation. The proposed OIR scheme is then combined with fault diagnosis scheme as a preprocessing unit, in order to improve fault detection performance. A separate model-based fault detection observer is designed which uses the estimated outlier-free states to perform fault diagnosis. Finally a simple linear system is used to verify the scheme in simulations followed by a piston pump test bed study.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127373854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-24DOI: 10.1109/ICPHM.2013.6621426
V. Balanica, Linxia Liao, Heiko Claussen, J. Rosca
Continuous vibration monitoring of mechanical roller bearing parts potentially reduces machine downtime through timely prediction and diagnosis of abnormal events. Despite the progress made in the literature, challenges remain in how to assess performance related information for maintenance decision-making from large data streams. Furthermore, since roller bearings operate under various regimes (e.g., speed and load), it is not trivial to consider the effect of regime changes in the modeling in order to reduce false alarms. The paper describes a multi-model approach to monitor the condition of roller bearings under different operating regimes. Two modeling approaches for anomaly and degradation monitoring are proposed to automatically retrieve information from the data. A self-organizing map (SOM) and a support vector machines (SVM) are used comparatively for the evaluation of a bearing degradation in time (i.e., a dynamic health indicator) and for the determination of changes in the tracked features. The proposed method is validated using data from multiple bearings of the same type.
{"title":"A multi-model approach for anomaly detection and diagnosis using vibration signals","authors":"V. Balanica, Linxia Liao, Heiko Claussen, J. Rosca","doi":"10.1109/ICPHM.2013.6621426","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621426","url":null,"abstract":"Continuous vibration monitoring of mechanical roller bearing parts potentially reduces machine downtime through timely prediction and diagnosis of abnormal events. Despite the progress made in the literature, challenges remain in how to assess performance related information for maintenance decision-making from large data streams. Furthermore, since roller bearings operate under various regimes (e.g., speed and load), it is not trivial to consider the effect of regime changes in the modeling in order to reduce false alarms. The paper describes a multi-model approach to monitor the condition of roller bearings under different operating regimes. Two modeling approaches for anomaly and degradation monitoring are proposed to automatically retrieve information from the data. A self-organizing map (SOM) and a support vector machines (SVM) are used comparatively for the evaluation of a bearing degradation in time (i.e., a dynamic health indicator) and for the determination of changes in the tracked features. The proposed method is validated using data from multiple bearings of the same type.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115583433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-24DOI: 10.1109/ICPHM.2013.6621432
Arvind Sai Sarathi Vasan, Chao-Shiou Chen, M. Pecht
Electronic system failures during field operation in mission, safety and infrastructure critical applications can have severe implications. In these applications, incorporating prognostics and health management (PHM) techniques provide systems with capabilities to self assess performance, determine the advent of failure and mitigate system risks. However, the prognostics problem for electronic systems is still approached from a component-centric-view. Extending a component-centric approach to an electronic system becomes complex and is often not worth the cost of pursuit due to the imbalance between scalability and efficiency of the prognostics approach. In order to address this problem, we propose a circuit-centric approach as an alternative method for realizing prognostics at an electronic system-level. The proposed approach is developed from the idea of decomposing a system into multiple critical circuits, and exploiting the parameters specific to the system's circuitries for predicting failure. Furthermore, a method is developed for detecting the gradual degradation of an electronic system by defining a health indicator to represent the system's health state at any given time. In this paper, we provide a formulation of the electronic system-level prognostics problem and demonstrate the approach on an electronic system for filtering analog signals.
{"title":"A circuit-centric approach to electronic system-level diagnostics and prognostics","authors":"Arvind Sai Sarathi Vasan, Chao-Shiou Chen, M. Pecht","doi":"10.1109/ICPHM.2013.6621432","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621432","url":null,"abstract":"Electronic system failures during field operation in mission, safety and infrastructure critical applications can have severe implications. In these applications, incorporating prognostics and health management (PHM) techniques provide systems with capabilities to self assess performance, determine the advent of failure and mitigate system risks. However, the prognostics problem for electronic systems is still approached from a component-centric-view. Extending a component-centric approach to an electronic system becomes complex and is often not worth the cost of pursuit due to the imbalance between scalability and efficiency of the prognostics approach. In order to address this problem, we propose a circuit-centric approach as an alternative method for realizing prognostics at an electronic system-level. The proposed approach is developed from the idea of decomposing a system into multiple critical circuits, and exploiting the parameters specific to the system's circuitries for predicting failure. Furthermore, a method is developed for detecting the gradual degradation of an electronic system by defining a health indicator to represent the system's health state at any given time. In this paper, we provide a formulation of the electronic system-level prognostics problem and demonstrate the approach on an electronic system for filtering analog signals.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114115556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-24DOI: 10.1109/ICPHM.2013.6621411
M. El-Koujok, M. Benammar, N. Meskin, M. Al-Naemi, R. Langari
Industrial processes rely heavily on information provided by sensors. Reliability of sensor data is vital to assure an acceptable performance of these complex and nonlinear processes. In this paper, the analytical redundancy approach has been adopted to detect and isolate sensor faults in which the model of a given nonlinear dynamical system is identified based on the available input/output time profile. Towards this goal, an evolving Takagi-Sugeno approach as a universal approximator is used to represent a nonlinear mapping between the past values of input/output data and the current value of the output data. However, the main challenge is the selection of the appropriate set of past values that can lead to the best estimate of the output. In this paper, a genetic algorithm is utilized as a powerful data-driven tool for finding the best set of input-output past values. The proposed approach is applied to the problem of sensor fault detection and isolation in a Continuous-Flow Stirred-Tank Reactor. Simulation results demonstrate and validate the performance capabilities of the proposed approach.
{"title":"Application of genetic algorithm in selection of dominant input variables in sensor fault diagnosis of nonlinear systems","authors":"M. El-Koujok, M. Benammar, N. Meskin, M. Al-Naemi, R. Langari","doi":"10.1109/ICPHM.2013.6621411","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621411","url":null,"abstract":"Industrial processes rely heavily on information provided by sensors. Reliability of sensor data is vital to assure an acceptable performance of these complex and nonlinear processes. In this paper, the analytical redundancy approach has been adopted to detect and isolate sensor faults in which the model of a given nonlinear dynamical system is identified based on the available input/output time profile. Towards this goal, an evolving Takagi-Sugeno approach as a universal approximator is used to represent a nonlinear mapping between the past values of input/output data and the current value of the output data. However, the main challenge is the selection of the appropriate set of past values that can lead to the best estimate of the output. In this paper, a genetic algorithm is utilized as a powerful data-driven tool for finding the best set of input-output past values. The proposed approach is applied to the problem of sensor fault detection and isolation in a Continuous-Flow Stirred-Tank Reactor. Simulation results demonstrate and validate the performance capabilities of the proposed approach.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131826282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-24DOI: 10.1109/ICPHM.2013.6621457
P. Lall, Junchao Wei, Lynn Davis
Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. Life prediction of L70 life for the LEDs used in SSL luminaires from KF and EKF based models have been compared with the TM-21 model predictions and experimental data.
{"title":"Prediction of lumen output and chromaticity shift in LEDs using Kalman Filter and Extended Kalman Filter based models","authors":"P. Lall, Junchao Wei, Lynn Davis","doi":"10.1109/ICPHM.2013.6621457","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621457","url":null,"abstract":"Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. Life prediction of L70 life for the LEDs used in SSL luminaires from KF and EKF based models have been compared with the TM-21 model predictions and experimental data.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124117185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-24DOI: 10.1109/ICPHM.2013.6621447
N. Verma, V. Gupta, Mayank Sharma, R. K. Sevakula
Support Vector Machine (SVM) has been very popular for use in machine fault diagnosis as classifier. In most of the complex machine learning problems, the main challenge lies in finding good features. Sparse autoencoders have the ability to learn good features from the input data in an unsuperivised fashion. Sparse auto-encoders and other deep architectures are already showing very good results in text classification, speaker and speech recognition and face recognition as well. In this paper, we compare the performance of sparse autoencoders with soft max regression, fast classifier based on Mahalanobis distance and SVM in fault diagnosis of air compressors.
{"title":"Intelligent condition based monitoring of rotating machines using sparse auto-encoders","authors":"N. Verma, V. Gupta, Mayank Sharma, R. K. Sevakula","doi":"10.1109/ICPHM.2013.6621447","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621447","url":null,"abstract":"Support Vector Machine (SVM) has been very popular for use in machine fault diagnosis as classifier. In most of the complex machine learning problems, the main challenge lies in finding good features. Sparse autoencoders have the ability to learn good features from the input data in an unsuperivised fashion. Sparse auto-encoders and other deep architectures are already showing very good results in text classification, speaker and speech recognition and face recognition as well. In this paper, we compare the performance of sparse autoencoders with soft max regression, fast classifier based on Mahalanobis distance and SVM in fault diagnosis of air compressors.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123584699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-24DOI: 10.1109/ICPHM.2013.6621417
Kun Zhang, Jin-yong Yao, T. Jiang, Xizhong Yin, Xiaobo Yu
This paper presents a simulation analysis method of degradation behavior for electro-hydraulic servo valve (EHSV). Unlike traditional statistical methods, our work is motivated by the failure mechanism of erosion wear. We assume that degradation trend of flow characteristic is related to structure wear in the valve components. Hence, in this paper, twin flapper-nozzle servo valve is considered as an example to analyze the degradation behavior in a simulation way. First, erosion wear rates at the precise structure are obtained in hydraulic oil of contaminant class 12 by the Computational Fluid Dynamics (CFD) models. Then, degradation trends of null leakage are simulated under different erosive wear conditions. Finally, the relationship between wear in the valve structure and degradation in null leakage is obtained by the testing data. The simulation results show that erosion wear happens at three sites i.e. the flapper surface, the nozzle outlet and sharp edges of the spool. Moreover, erosion wear of sharp edges greatly influences the flow rate of null leakage. The feasibility of our approach in analyzing degradation trend of hydraulic components is validated by the simulation experiments.
{"title":"Degradation behavior analysis of electro-hydraulic servo valve under erosion wear","authors":"Kun Zhang, Jin-yong Yao, T. Jiang, Xizhong Yin, Xiaobo Yu","doi":"10.1109/ICPHM.2013.6621417","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621417","url":null,"abstract":"This paper presents a simulation analysis method of degradation behavior for electro-hydraulic servo valve (EHSV). Unlike traditional statistical methods, our work is motivated by the failure mechanism of erosion wear. We assume that degradation trend of flow characteristic is related to structure wear in the valve components. Hence, in this paper, twin flapper-nozzle servo valve is considered as an example to analyze the degradation behavior in a simulation way. First, erosion wear rates at the precise structure are obtained in hydraulic oil of contaminant class 12 by the Computational Fluid Dynamics (CFD) models. Then, degradation trends of null leakage are simulated under different erosive wear conditions. Finally, the relationship between wear in the valve structure and degradation in null leakage is obtained by the testing data. The simulation results show that erosion wear happens at three sites i.e. the flapper surface, the nozzle outlet and sharp edges of the spool. Moreover, erosion wear of sharp edges greatly influences the flow rate of null leakage. The feasibility of our approach in analyzing degradation trend of hydraulic components is validated by the simulation experiments.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129489692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-24DOI: 10.1109/ICPHM.2013.6621434
Moon-Hwan Chang, M. Pecht, W. K. Yung
Light emitting diode (LED) lighting systems have been implemented in a wide range of applications because they save money and are better for the environment than traditional lighting systems. However, the lack of information regarding LED reliability is a barrier to the further expansion of LED use. Prognostics and health management (PHM) techniques can be utilized to provide LED reliability information to remove this barrier. PHM can provide early warning of failures, reduce unscheduled maintenance events, extend the time interval of the maintenance cycle, and reduce the life cycle cost of LED lighting systems. This paper presents an evaluation of the return on investment from implementing PHM in LED lighting systems with different failure distributions. It also presents the results of a study comparing the life cycle cost of an LED lighting system maintained by unscheduled maintenance with the life cycle cost of the same system maintained using a PHM approach.
{"title":"Return on investment associated with PHM applied to an LED lighting system","authors":"Moon-Hwan Chang, M. Pecht, W. K. Yung","doi":"10.1109/ICPHM.2013.6621434","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621434","url":null,"abstract":"Light emitting diode (LED) lighting systems have been implemented in a wide range of applications because they save money and are better for the environment than traditional lighting systems. However, the lack of information regarding LED reliability is a barrier to the further expansion of LED use. Prognostics and health management (PHM) techniques can be utilized to provide LED reliability information to remove this barrier. PHM can provide early warning of failures, reduce unscheduled maintenance events, extend the time interval of the maintenance cycle, and reduce the life cycle cost of LED lighting systems. This paper presents an evaluation of the return on investment from implementing PHM in LED lighting systems with different failure distributions. It also presents the results of a study comparing the life cycle cost of an LED lighting system maintained by unscheduled maintenance with the life cycle cost of the same system maintained using a PHM approach.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123180747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-24DOI: 10.1109/ICPHM.2013.6621412
S. Hareland, L. Radtke
Many types of implantable medical devices provide life-sustaining therapy for patients and are, therefore, designed for high levels of reliability. New concepts and approaches in prognostics and health management of systems may enable improved opportunities for ensuring continuity of life-sustaining therapies by providing adequate response time to handle emerging issues prior to adverse clinical impacts. Due to numerous competing constraints in implantable medical devices, a range of considerations can be taken into account when it comes to prognostic sensors, logic, and responses as part of a risk management approach.
{"title":"Prognostic opportunities and limitations in implantable medical devices","authors":"S. Hareland, L. Radtke","doi":"10.1109/ICPHM.2013.6621412","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621412","url":null,"abstract":"Many types of implantable medical devices provide life-sustaining therapy for patients and are, therefore, designed for high levels of reliability. New concepts and approaches in prognostics and health management of systems may enable improved opportunities for ensuring continuity of life-sustaining therapies by providing adequate response time to handle emerging issues prior to adverse clinical impacts. Due to numerous competing constraints in implantable medical devices, a range of considerations can be taken into account when it comes to prognostic sensors, logic, and responses as part of a risk management approach.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127803415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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