Pub Date : 2014-06-22DOI: 10.1109/ICPHM.2014.7036363
Marine Jouin, R. Gouriveau, D. Hissel, M. Péra, N. Zerhouni
In the perspective of decreasing polluting emissions and developing alternative energies, fuel cells, and more precisely Proton Exchange Membrane Fuel Cells (PEMFC), represent a promising solution. Even if this technology is close to being competitive, it still suffers from too short life duration. As a consequence, prognostic seems to be a great solution to anticipate PEMFC stacks degradation. However, a PEMFC implies multiphysics and multiscale phenomena making the construction of an aging model only based on physics very complex. One solution consists in using a hybrid approach for prognostics combining the use of models and available data. Among these hybrid approaches, particle filtering methods seem to be really appropriate as they offer the possibility to compute models with time varying parameters and to update them all along the prognostics process. But to be efficient, not only should the prognostics system take into account the aging of the stack but also external events influencing this aging. Indeed, some acquisition techniques introduce disturbances in the fuel cell behavior and a voltage recovery can be observed at the end of the characterization process. This paper proposes to tackle this problem. First, PEMFC fuel cells and their complexities are introduced. Then, the impact of characterization of the fuel cell behavior is described. Empirical models are built and introduced in both learning and prediction phases of the prognostics model by combining three particle filters. The new prognostic framework is used to perform remaining useful life estimates and the whole proposition is illustrated with a long term experiment data set of a PEMFC in constant load solicitation and stable operating conditions. Estimates can be given with an error less than 5% for life durations of more than 1000 hours. Finally, the results are compared to a previous work to show that introducing a disturbance modeling can dramatically reduce the uncertainty coming with the predictions.
{"title":"Prognostics of Proton Exchange Membrane Fuel Cell stack in a particle filtering framework including characterization disturbances and voltage recovery","authors":"Marine Jouin, R. Gouriveau, D. Hissel, M. Péra, N. Zerhouni","doi":"10.1109/ICPHM.2014.7036363","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036363","url":null,"abstract":"In the perspective of decreasing polluting emissions and developing alternative energies, fuel cells, and more precisely Proton Exchange Membrane Fuel Cells (PEMFC), represent a promising solution. Even if this technology is close to being competitive, it still suffers from too short life duration. As a consequence, prognostic seems to be a great solution to anticipate PEMFC stacks degradation. However, a PEMFC implies multiphysics and multiscale phenomena making the construction of an aging model only based on physics very complex. One solution consists in using a hybrid approach for prognostics combining the use of models and available data. Among these hybrid approaches, particle filtering methods seem to be really appropriate as they offer the possibility to compute models with time varying parameters and to update them all along the prognostics process. But to be efficient, not only should the prognostics system take into account the aging of the stack but also external events influencing this aging. Indeed, some acquisition techniques introduce disturbances in the fuel cell behavior and a voltage recovery can be observed at the end of the characterization process. This paper proposes to tackle this problem. First, PEMFC fuel cells and their complexities are introduced. Then, the impact of characterization of the fuel cell behavior is described. Empirical models are built and introduced in both learning and prediction phases of the prognostics model by combining three particle filters. The new prognostic framework is used to perform remaining useful life estimates and the whole proposition is illustrated with a long term experiment data set of a PEMFC in constant load solicitation and stable operating conditions. Estimates can be given with an error less than 5% for life durations of more than 1000 hours. Finally, the results are compared to a previous work to show that introducing a disturbance modeling can dramatically reduce the uncertainty coming with the predictions.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122124086","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 : 2014-06-22DOI: 10.1109/ICPHM.2014.7036401
P. Lall, Junchao Wei, P. Sakalaukus
A new method has been developed for assessment of the onset of degradation in solid state luminaires to classify failure mechanisms by using metrics beyond lumen degradation that are currently used for identification of failure. Luminous Flux output, Correlated Color Temperature Data on Philips LED Lamps has been gathered under 85°C/85%RH till lamp failure. Failure modes of the test population of the lamps have been studied to understand the failure mechanisms in 85°C/85%RH accelerated test. Results indicate that the dominant failure mechanism is the discoloration of the LED encapsulant inside the lamps which is the likely cause for the luminous flux degradation and the color shift. The acquired data has been used in conjunction with Bayesian Probabilistic Models to identify luminaires with onset of degradation much prior to failure through identification of decision boundaries between lamps with accrued damage and lamps beyond the failure threshold in the feature space. In addition luminaires with different failure modes have been classified separately from healthy pristine luminaires. The α-λ plots have been used to evaluate the robustness of the proposed methodology. Results show that the predicted degradation for the lamps tracks the true degradation observed during 85°C/85%RH during accelerated life test fairly closely within the ±20% confidence bounds. Correlation of model prediction with experimental results indicates that the presented methodology allows the early identification of the onset of failure much prior to development of complete failure distributions and can be used for assessing the damage state of SSLs in fairly large deployments. It is expected that, the new prediction technique will allow the development of failure distributions without testing till L70 life for the manifestation of failure.
{"title":"Bayesian probabilistic model for life prediction and fault mode classification of solid state luminaires","authors":"P. Lall, Junchao Wei, P. Sakalaukus","doi":"10.1109/ICPHM.2014.7036401","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036401","url":null,"abstract":"A new method has been developed for assessment of the onset of degradation in solid state luminaires to classify failure mechanisms by using metrics beyond lumen degradation that are currently used for identification of failure. Luminous Flux output, Correlated Color Temperature Data on Philips LED Lamps has been gathered under 85°C/85%RH till lamp failure. Failure modes of the test population of the lamps have been studied to understand the failure mechanisms in 85°C/85%RH accelerated test. Results indicate that the dominant failure mechanism is the discoloration of the LED encapsulant inside the lamps which is the likely cause for the luminous flux degradation and the color shift. The acquired data has been used in conjunction with Bayesian Probabilistic Models to identify luminaires with onset of degradation much prior to failure through identification of decision boundaries between lamps with accrued damage and lamps beyond the failure threshold in the feature space. In addition luminaires with different failure modes have been classified separately from healthy pristine luminaires. The α-λ plots have been used to evaluate the robustness of the proposed methodology. Results show that the predicted degradation for the lamps tracks the true degradation observed during 85°C/85%RH during accelerated life test fairly closely within the ±20% confidence bounds. Correlation of model prediction with experimental results indicates that the presented methodology allows the early identification of the onset of failure much prior to development of complete failure distributions and can be used for assessing the damage state of SSLs in fairly large deployments. It is expected that, the new prediction technique will allow the development of failure distributions without testing till L70 life for the manifestation of failure.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123470474","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 : 2014-06-22DOI: 10.1109/ICPHM.2014.7036391
Houman Hanachi, Jie Liu, A. Banerjee, Ying Chen
Monitoring the performance of gas turbine engines (GTEs) by sampling the operating parameters of the GTEs is the central part of the GTEs health management program. The rate of data sampling and the consequent analyses of the sampled data are restricted to the available resources. It especially appears as a principal constraint where the data is manually logged by the operators. In a recent research work, a physics-based approach and resulting performance indicators, i.e., “Heat Loss index” and “Power Deficit index” were introduced by the authors to monitor the health state of the gas turbines using only the readings from the GTE operating system. Statistical estimation approach was taken to establish prediction models for performance indicators. This study provides a quantitative analysis for the effect of sampling decimation on the accuracy of the developed predictor within a time window. Consequently, it provides an insight into the performance prediction uncertainty, in connection with the sampling frequency and the length of the observation window on which the model is established.
{"title":"Effects of sampling decimation on a gas turbine performance monitoring","authors":"Houman Hanachi, Jie Liu, A. Banerjee, Ying Chen","doi":"10.1109/ICPHM.2014.7036391","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036391","url":null,"abstract":"Monitoring the performance of gas turbine engines (GTEs) by sampling the operating parameters of the GTEs is the central part of the GTEs health management program. The rate of data sampling and the consequent analyses of the sampled data are restricted to the available resources. It especially appears as a principal constraint where the data is manually logged by the operators. In a recent research work, a physics-based approach and resulting performance indicators, i.e., “Heat Loss index” and “Power Deficit index” were introduced by the authors to monitor the health state of the gas turbines using only the readings from the GTE operating system. Statistical estimation approach was taken to establish prediction models for performance indicators. This study provides a quantitative analysis for the effect of sampling decimation on the accuracy of the developed predictor within a time window. Consequently, it provides an insight into the performance prediction uncertainty, in connection with the sampling frequency and the length of the observation window on which the model is established.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124197404","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 : 2014-06-22DOI: 10.1109/ICPHM.2014.7036397
Nathalie Herr, J. Nicod, C. Varnier
This paper adresses the problem of optimizing the useful life of a heterogeneous distributed platform which has to produce a given production service. The purpose is to provide a production scheduling that maximizes the production horizon. The use of Prognostics and Health Management (PHM) results in the form of Remaining Useful Life (RUL) allows to adapt the schedule to the wear and tear of equipment. This work comes within the scope of Prognostics Decision Making (DM). Each considered machine is supposed to be able to provide several throughputs corresponding to different operating conditions. The key point is to select the appropriate profile for each machine during the whole useful life of the platform. Many heuristics are proposed to cope with this decision problem and are compared through simulation results. Simulations assess the efficiency of these heuristics. Distance to the theoretical maximal value comes close to 10% for the most efficient ones. A repair module performing a revision of the schedules provided by the heuristics is moreover proposed to enhance the results. First results are promising.
{"title":"Prognostic Decision Making to extend a platform useful life under service constraint","authors":"Nathalie Herr, J. Nicod, C. Varnier","doi":"10.1109/ICPHM.2014.7036397","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036397","url":null,"abstract":"This paper adresses the problem of optimizing the useful life of a heterogeneous distributed platform which has to produce a given production service. The purpose is to provide a production scheduling that maximizes the production horizon. The use of Prognostics and Health Management (PHM) results in the form of Remaining Useful Life (RUL) allows to adapt the schedule to the wear and tear of equipment. This work comes within the scope of Prognostics Decision Making (DM). Each considered machine is supposed to be able to provide several throughputs corresponding to different operating conditions. The key point is to select the appropriate profile for each machine during the whole useful life of the platform. Many heuristics are proposed to cope with this decision problem and are compared through simulation results. Simulations assess the efficiency of these heuristics. Distance to the theoretical maximal value comes close to 10% for the most efficient ones. A repair module performing a revision of the schedules provided by the heuristics is moreover proposed to enhance the results. First results are promising.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121206750","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 : 2014-06-22DOI: 10.1109/ICPHM.2014.7036375
Yi Wu, You-ren Wang, Yuanyuan Jiang, Quan Sun
Prognosis of DC-DC power converters is necessary in embedded and safety critical applications to prevent further damages. However, most of the prognostic methods of power converters are focus on the critical components of the converters. Furthermore, the methods seldom consider the effect of changes in operating conditions (e.g. power supply and load). In order to address these problems, an innovative system-level fault characteristic parameter (FCP) represents the degradation status of the entire converter is extracted, and a prognostic method of DC-DC converters based on the degradation trend prediction of the FCP is proposed. Firstly, the effect of component-level degradation on the overall performance of the DC-DC converters is studied. Then, a performance parameter of DC-DC converters which is sensitive to the degradation of all critical components is chosen, and a least squares support vector machine (LSSVM) model is used to convert the performance parameter to the FCP under predetermined normal condition to eliminate the influence of changes in operating conditions. Finally, the trend prediction of the FCP is performed based on Gaussian process regression (GPR) to realize the prognosis of DC-DC converters. A Boost converter is taken as an illustrative example. Results show the feasibility and effectiveness of the proposed method.
{"title":"A Prognostic method for DC-DC converters under variable operating conditions","authors":"Yi Wu, You-ren Wang, Yuanyuan Jiang, Quan Sun","doi":"10.1109/ICPHM.2014.7036375","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036375","url":null,"abstract":"Prognosis of DC-DC power converters is necessary in embedded and safety critical applications to prevent further damages. However, most of the prognostic methods of power converters are focus on the critical components of the converters. Furthermore, the methods seldom consider the effect of changes in operating conditions (e.g. power supply and load). In order to address these problems, an innovative system-level fault characteristic parameter (FCP) represents the degradation status of the entire converter is extracted, and a prognostic method of DC-DC converters based on the degradation trend prediction of the FCP is proposed. Firstly, the effect of component-level degradation on the overall performance of the DC-DC converters is studied. Then, a performance parameter of DC-DC converters which is sensitive to the degradation of all critical components is chosen, and a least squares support vector machine (LSSVM) model is used to convert the performance parameter to the FCP under predetermined normal condition to eliminate the influence of changes in operating conditions. Finally, the trend prediction of the FCP is performed based on Gaussian process regression (GPR) to realize the prognosis of DC-DC converters. A Boost converter is taken as an illustrative example. Results show the feasibility and effectiveness of the proposed method.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128665322","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 : 2014-06-22DOI: 10.1109/ICPHM.2014.7036377
A. Rastegari, M. Bengtsson
This paper presents a guide for implementation of Condition Based Maintenance (CBM) in a manufacturing industry, considering the technical constituents and organizational aspects when implementing CBM. The empirical base for the study is a case study from a major manufacturing site in Sweden. The data was collected during a pilot project to implement CBM at the case company. The purpose of the pilot study at the company was to implement online condition monitoring on some of the critical components in the hardening process. Hereby, two of the main online condition monitoring techniques namely vibration analysis and Shock Pulse Method (SPM) have been implemented and tested on electric motors to monitor bearing conditions. The paper presents the process of implementation and the elements included in this process. Some of the main elements in the implementation process are selection of the components to be monitored, techniques and technologies as well as installation of the technologies and finally how to analyze the results from the condition monitoring. The data from online condition monitoring on the electric motors, driving the furnace fans, are recorded and presented in the paper including breakdown data on two objects. This information is leading to useful and reliable knowledge for maintenance work to be cost effective and be able to increase the overall equipment availability (OEA). In addition to this, the result indicates to what extent advanced CBM practices are applicable in the hardening environment in the manufacturing company and it provides guidance for further research and development in this area. The paper concludes with a discussion on possible future trends and research areas, needed to increase the effective and efficient use of CBM.
{"title":"Implementation of Condition Based Maintenance in manufacturing industry - A pilot case study","authors":"A. Rastegari, M. Bengtsson","doi":"10.1109/ICPHM.2014.7036377","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036377","url":null,"abstract":"This paper presents a guide for implementation of Condition Based Maintenance (CBM) in a manufacturing industry, considering the technical constituents and organizational aspects when implementing CBM. The empirical base for the study is a case study from a major manufacturing site in Sweden. The data was collected during a pilot project to implement CBM at the case company. The purpose of the pilot study at the company was to implement online condition monitoring on some of the critical components in the hardening process. Hereby, two of the main online condition monitoring techniques namely vibration analysis and Shock Pulse Method (SPM) have been implemented and tested on electric motors to monitor bearing conditions. The paper presents the process of implementation and the elements included in this process. Some of the main elements in the implementation process are selection of the components to be monitored, techniques and technologies as well as installation of the technologies and finally how to analyze the results from the condition monitoring. The data from online condition monitoring on the electric motors, driving the furnace fans, are recorded and presented in the paper including breakdown data on two objects. This information is leading to useful and reliable knowledge for maintenance work to be cost effective and be able to increase the overall equipment availability (OEA). In addition to this, the result indicates to what extent advanced CBM practices are applicable in the hardening environment in the manufacturing company and it provides guidance for further research and development in this area. The paper concludes with a discussion on possible future trends and research areas, needed to increase the effective and efficient use of CBM.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126055942","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 : 2014-06-22DOI: 10.1109/ICPHM.2014.7036367
Naipeng Li, Y. Lei, Zongyao Liu, Jing Lin
Rolling element bearings are one of the most widely used components in rotating machinery. However, they are also the components which frequently suffer from damage. Remaining useful life (RUL) prediction of rolling element bearings has received considerable attention, since it can avoid failure risks, and ensure availability, reliability and security. Model-based methods are commonly used in RUL prediction because of their high accuracy in long-time prediction. In model-based methods, a degradation indicator which describes the whole degradation process of bearings, however, is very critical but difficult to be extracted. A model function, used to predict the evolution trend and the RUL of bearings, is difficult to develop as well. In this paper, a particle filtering (PF)-based approach is developed to predict the RUL of rolling element bearings. In this approach, two modules are included, i.e. indicator calculation module and PF-based prediction module. In the first module, a new degradation indicator is calculated based on correlation matrix clustering and weight algorithm. This indicator fuses different characteristics of multiple features, includes more fault information and therefore has a better prediction tendency. In the second module, a PF-based approach is proposed to predict the RUL of bearings. Different from the traditional PF-based approach, a new algorithm of parameter initialization is introduced to calculate the initial parameters of the state space model. Experimental data of rolling element bearings are used to demonstrate the effectiveness of this approach. For comparison, another RUL prediction approach based on adaptive neuro-fuzzy inference system (ANFIS) is also utilized to process the experimental data. The result shows that the proposed approach can effectively calculate the appropriate degradation indicator, initialize the model parameters and perform better in RUL prediction than the ANFIS-based approach for rolling element bearings.
{"title":"A particle filtering-based approach for remaining useful life predication of rolling element bearings","authors":"Naipeng Li, Y. Lei, Zongyao Liu, Jing Lin","doi":"10.1109/ICPHM.2014.7036367","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036367","url":null,"abstract":"Rolling element bearings are one of the most widely used components in rotating machinery. However, they are also the components which frequently suffer from damage. Remaining useful life (RUL) prediction of rolling element bearings has received considerable attention, since it can avoid failure risks, and ensure availability, reliability and security. Model-based methods are commonly used in RUL prediction because of their high accuracy in long-time prediction. In model-based methods, a degradation indicator which describes the whole degradation process of bearings, however, is very critical but difficult to be extracted. A model function, used to predict the evolution trend and the RUL of bearings, is difficult to develop as well. In this paper, a particle filtering (PF)-based approach is developed to predict the RUL of rolling element bearings. In this approach, two modules are included, i.e. indicator calculation module and PF-based prediction module. In the first module, a new degradation indicator is calculated based on correlation matrix clustering and weight algorithm. This indicator fuses different characteristics of multiple features, includes more fault information and therefore has a better prediction tendency. In the second module, a PF-based approach is proposed to predict the RUL of bearings. Different from the traditional PF-based approach, a new algorithm of parameter initialization is introduced to calculate the initial parameters of the state space model. Experimental data of rolling element bearings are used to demonstrate the effectiveness of this approach. For comparison, another RUL prediction approach based on adaptive neuro-fuzzy inference system (ANFIS) is also utilized to process the experimental data. The result shows that the proposed approach can effectively calculate the appropriate degradation indicator, initialize the model parameters and perform better in RUL prediction than the ANFIS-based approach for rolling element bearings.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127081212","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 : 2014-06-22DOI: 10.1109/ICPHM.2014.7036399
R. Meyer, P. Ramuhalli, E. Hirt, A. Pardini, J. Suter, M. Prowant
Sustainable nuclear power to promote energy security and to reduce greenhouse gas emissions are two key national energy priorities. The development of deployable small modular reactors (SMRs) is expected to support these objectives by developing technologies that improve the reliability, sustain safety, and improve affordability of new reactors. Advanced SMRs (AdvSMRs) refer to a specific class of SMRs and are based on modularization of advanced reactor concepts. Prognostic health management (PHM) systems can benefit both the safety and economics of deploying AdvSMRs and can play an essential role in managing the inspection and maintenance of passive components in AdvSMR systems. This paper describes progress on development of an experimental setup for testing and validation of PHM systems for AdvSMR passive components. The experimental set-up for validation of prognostic algorithms is focused on thermal creep degradation as the prototypic degradation mechanism. The test bed enables accelerated thermal creep aging of materials relevant to AdvSMRs along with multiple nondestructive evaluation (NDE) measurements for assessment of thermal creep damage. NDE techniques include eddy current, magnetic Barkhausen noise (MBN), and linear and non-linear ultrasonic measurements. Details of the test-bed design as well as initial measurements results for specimens at different levels of thermal creep damage are presented.
{"title":"Progress towards prognostic health management of passive components in advanced small modular reactors","authors":"R. Meyer, P. Ramuhalli, E. Hirt, A. Pardini, J. Suter, M. Prowant","doi":"10.1109/ICPHM.2014.7036399","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036399","url":null,"abstract":"Sustainable nuclear power to promote energy security and to reduce greenhouse gas emissions are two key national energy priorities. The development of deployable small modular reactors (SMRs) is expected to support these objectives by developing technologies that improve the reliability, sustain safety, and improve affordability of new reactors. Advanced SMRs (AdvSMRs) refer to a specific class of SMRs and are based on modularization of advanced reactor concepts. Prognostic health management (PHM) systems can benefit both the safety and economics of deploying AdvSMRs and can play an essential role in managing the inspection and maintenance of passive components in AdvSMR systems. This paper describes progress on development of an experimental setup for testing and validation of PHM systems for AdvSMR passive components. The experimental set-up for validation of prognostic algorithms is focused on thermal creep degradation as the prototypic degradation mechanism. The test bed enables accelerated thermal creep aging of materials relevant to AdvSMRs along with multiple nondestructive evaluation (NDE) measurements for assessment of thermal creep damage. NDE techniques include eddy current, magnetic Barkhausen noise (MBN), and linear and non-linear ultrasonic measurements. Details of the test-bed design as well as initial measurements results for specimens at different levels of thermal creep damage are presented.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"101 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134426105","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 : 2014-06-22DOI: 10.1109/ICPHM.2014.7036393
Adarsh Kumar, J. Ramkumar, N. Verma, Sonal Dixit
In this era of flexible manufacturing systems, increase in demand of automatic and unattended machining process is very high. Thus arise the need for proper online tool condition monitoring methods, in order to minimize error and waste of work-material. In this study, Support Vector Machine (SVM), Artificial Neural Network (ANN) and Bayes classifier are used to develop such a system for automatic drilling operations with the help of vibration signals. The performances of models generated by these classifiers are compared with each other in order to establish the best method. As the vibration signals were acquired under different drilling parameters, this study also tries to understand the events in drilling process that help in ease of fault classification. Three different kinds of wears were studied and later compared to understand the degree or magnitude of effect of wears on the drilling process and signals.
{"title":"Detection and classification for faults in drilling process using vibration analysis","authors":"Adarsh Kumar, J. Ramkumar, N. Verma, Sonal Dixit","doi":"10.1109/ICPHM.2014.7036393","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036393","url":null,"abstract":"In this era of flexible manufacturing systems, increase in demand of automatic and unattended machining process is very high. Thus arise the need for proper online tool condition monitoring methods, in order to minimize error and waste of work-material. In this study, Support Vector Machine (SVM), Artificial Neural Network (ANN) and Bayes classifier are used to develop such a system for automatic drilling operations with the help of vibration signals. The performances of models generated by these classifiers are compared with each other in order to establish the best method. As the vibration signals were acquired under different drilling parameters, this study also tries to understand the events in drilling process that help in ease of fault classification. Three different kinds of wears were studied and later compared to understand the degree or magnitude of effect of wears on the drilling process and signals.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114293580","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 : 2014-06-22DOI: 10.1109/ICPHM.2014.7036390
Guangxing Bai, Pingfeng Wang
Accurate estimation of the state-of-charge (SoC) and state-of-health (SoH) for an operating battery, as a critical task for battery health management, greatly depends on the validity and generalizability of battery models. Due to the variability and uncertainties involved in battery design, manufacturing, and operation, developing a generally applicable battery physical model is a big challenge. To eliminate the dependency of SoC and SoH estimation on battery physical models, this paper presents a generic data-driven approach for lithium-ion battery health management that integrates an artificial neural network (ANN) with a dual extended Kalman filter (DEKF) algorithm. The ANN is trained offline to model the battery terminal voltages to be used by the DEKF. With the trained ANN, the DEKF algorithm is then employed online for SoC and SoH estimation, where voltage outputs from the trained ANN model are used in DEKF state-space equations to replace the battery physical model. Experimental results are used to demonstrate the effectiveness of the developed model-free approach for battery health management.
{"title":"A self-cognizant dynamic system approach for battery state of health estimation","authors":"Guangxing Bai, Pingfeng Wang","doi":"10.1109/ICPHM.2014.7036390","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036390","url":null,"abstract":"Accurate estimation of the state-of-charge (SoC) and state-of-health (SoH) for an operating battery, as a critical task for battery health management, greatly depends on the validity and generalizability of battery models. Due to the variability and uncertainties involved in battery design, manufacturing, and operation, developing a generally applicable battery physical model is a big challenge. To eliminate the dependency of SoC and SoH estimation on battery physical models, this paper presents a generic data-driven approach for lithium-ion battery health management that integrates an artificial neural network (ANN) with a dual extended Kalman filter (DEKF) algorithm. The ANN is trained offline to model the battery terminal voltages to be used by the DEKF. With the trained ANN, the DEKF algorithm is then employed online for SoC and SoH estimation, where voltage outputs from the trained ANN model are used in DEKF state-space equations to replace the battery physical model. Experimental results are used to demonstrate the effectiveness of the developed model-free approach for battery health management.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121186997","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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