Pub Date : 2013-06-24DOI: 10.1109/ICPHM.2013.6621410
S. Leem, Jooho Choi
A model-based method is proposed to diagnose the gear fault in the gearbox under variable loading condition with the objective to apply it to the wind turbine CMS. A simple test bed is installed to illustrate the approach, which consists of motors and a pair of spur gears. A crack is imbedded at the tooth root of a gear. Tachometer-based order analysis, being independent on the shaft speed, is employed as a signal processing technique to identify the fault, which includes detecting the location and extracting the adequate feature. Lumped parameter dynamic model is used to simulate the operation of the test bed. In the model, the parameter related with the fault is inversely estimated by minimizing the difference between the simulated and measured features. Finally the parameter is used to estimate the crack size based on the regression model made by finite element stiffness analyses of a set of gears with imbedded crack. The results of estimated crack are validated by comparing with the actual ones of seeded crack.
{"title":"Model-based diagnosis of gear fault under variable loading condition","authors":"S. Leem, Jooho Choi","doi":"10.1109/ICPHM.2013.6621410","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621410","url":null,"abstract":"A model-based method is proposed to diagnose the gear fault in the gearbox under variable loading condition with the objective to apply it to the wind turbine CMS. A simple test bed is installed to illustrate the approach, which consists of motors and a pair of spur gears. A crack is imbedded at the tooth root of a gear. Tachometer-based order analysis, being independent on the shaft speed, is employed as a signal processing technique to identify the fault, which includes detecting the location and extracting the adequate feature. Lumped parameter dynamic model is used to simulate the operation of the test bed. In the model, the parameter related with the fault is inversely estimated by minimizing the difference between the simulated and measured features. Finally the parameter is used to estimate the crack size based on the regression model made by finite element stiffness analyses of a set of gears with imbedded crack. The results of estimated crack are validated by comparing with the actual ones of seeded crack.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"260 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":"116113683","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.2013.6621415
Junda Zhu, Jae Yoon, D. He, Bin Qiu, Eric Bechhoefer
To increase wind energy production rate, there is a pressing need to improve the wind turbine availability and reduce the operational and maintenance costs. The safety and reliability of a functioning wind turbine depend largely on the protective properties of the lubrication oil for its drive train subassemblies such as gearbox and means for lubrication oil condition monitoring and degradation detection. The purpose of lubrication oil condition monitoring and degradation detection is to determine whether the oil has deteriorated to such a degree that it no longer fulfills its function. In this paper, particle contamination of lubrication oil and the remaining useful life (RUL) of the particle contaminated lubrication oil are investigated. Physical models are developed to quantify the relationship between particle contamination level and the outputs of commercially available online oil dielectric and viscosity sensors. The effectiveness of the developed models is then validated using laboratory experiments. In particular, the remaining useful life prediction of degraded lubrication oil with viscosity and dielectric constant data using particle filtering is presented. A simulation case study is provided to demonstrate the effectiveness of the developed technique.
{"title":"Online condition monitoring and remaining useful life prediction of particle contaminated lubrication oil","authors":"Junda Zhu, Jae Yoon, D. He, Bin Qiu, Eric Bechhoefer","doi":"10.1109/ICPHM.2013.6621415","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621415","url":null,"abstract":"To increase wind energy production rate, there is a pressing need to improve the wind turbine availability and reduce the operational and maintenance costs. The safety and reliability of a functioning wind turbine depend largely on the protective properties of the lubrication oil for its drive train subassemblies such as gearbox and means for lubrication oil condition monitoring and degradation detection. The purpose of lubrication oil condition monitoring and degradation detection is to determine whether the oil has deteriorated to such a degree that it no longer fulfills its function. In this paper, particle contamination of lubrication oil and the remaining useful life (RUL) of the particle contaminated lubrication oil are investigated. Physical models are developed to quantify the relationship between particle contamination level and the outputs of commercially available online oil dielectric and viscosity sensors. The effectiveness of the developed models is then validated using laboratory experiments. In particular, the remaining useful life prediction of degraded lubrication oil with viscosity and dielectric constant data using particle filtering is presented. A simulation case study is provided to demonstrate the effectiveness of the developed technique.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"822 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":"122399595","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.2013.6621451
N. Verma, Sreevidya Khatravath, A. Salour
Investment in maintenance of machines is significant for industries. In this paper, we suggest a Cost Benefit Analysis model and review most commonly used maintenance strategies like corrective maintenance and preventive maintenance with the model. Here, we presented a method to calculate net revenue generated by machine for optimal and cost effective alternative for machine maintenance. Method used for calculating net revenue is improved using factors such as labor cost, failure rate, utilization factor etc. Formulated net revenue provides satisfactory results when compared with the industrial information on machine maintenance strategies. Realistic data are used to analyze revenue with different maintenance strategies. From the results it is concluded that though condition based maintenance as a maintenance strategy has high initial investment cost with time it has high revenue generation compared to other maintenance polices.
{"title":"Cost Benefit Analysis for condition based maintenance","authors":"N. Verma, Sreevidya Khatravath, A. Salour","doi":"10.1109/ICPHM.2013.6621451","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621451","url":null,"abstract":"Investment in maintenance of machines is significant for industries. In this paper, we suggest a Cost Benefit Analysis model and review most commonly used maintenance strategies like corrective maintenance and preventive maintenance with the model. Here, we presented a method to calculate net revenue generated by machine for optimal and cost effective alternative for machine maintenance. Method used for calculating net revenue is improved using factors such as labor cost, failure rate, utilization factor etc. Formulated net revenue provides satisfactory results when compared with the industrial information on machine maintenance strategies. Realistic data are used to analyze revenue with different maintenance strategies. From the results it is concluded that though condition based maintenance as a maintenance strategy has high initial investment cost with time it has high revenue generation compared to other maintenance polices.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"4 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":"126668117","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.2013.6621449
Budhaditya Deb, Mohak Shah, S. Evans, Manoj Mehta, Anthony Gargulak, Tom Lasky
Many application systems are transforming from device centric architectures to cloud based systems that leverage shared compute resources to reduce cost and maximize reach. These systems require new paradigms to assure availability and quality of service. In this paper, we discuss the challenges in assuring Availability and Quality of Service in a Cloud Based Application System. We propose machine learning techniques for monitoring systems logs to assess the health of the system. A web services data set is employed to show that variety of services can be clustered to different service classes using a k-means clustering scheme. Reliability, Availability, and Serviceability (RAS) logs and Job logs dataset from high performance computing system is employed to show that impending fatal errors in the system can be predicted from the logs using an SVM classifier. These approaches illustrate the feasibility of methods to monitor the systems health and performance of compute resources and hence can be used to manage these systems for high availability and quality of service for critical tasks such as health care monitoring in the cloud.
{"title":"Towards systems level prognostics in the Cloud","authors":"Budhaditya Deb, Mohak Shah, S. Evans, Manoj Mehta, Anthony Gargulak, Tom Lasky","doi":"10.1109/ICPHM.2013.6621449","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621449","url":null,"abstract":"Many application systems are transforming from device centric architectures to cloud based systems that leverage shared compute resources to reduce cost and maximize reach. These systems require new paradigms to assure availability and quality of service. In this paper, we discuss the challenges in assuring Availability and Quality of Service in a Cloud Based Application System. We propose machine learning techniques for monitoring systems logs to assess the health of the system. A web services data set is employed to show that variety of services can be clustered to different service classes using a k-means clustering scheme. Reliability, Availability, and Serviceability (RAS) logs and Job logs dataset from high performance computing system is employed to show that impending fatal errors in the system can be predicted from the logs using an SVM classifier. These approaches illustrate the feasibility of methods to monitor the systems health and performance of compute resources and hence can be used to manage these systems for high availability and quality of service for critical tasks such as health care monitoring in the cloud.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"98 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":"127949187","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.2013.6621441
Datong Liu, Yue Luo, Limeng Guo, Yu Peng
The uncertainty of prognostics and remaining useful life (RUL) estimation for the lithium-ion battery is emphasized in the battery management system (BMS). Many machine learning algorithms and statistical methods can not only realize the RUL prediction but also provide the probability density function (PDF) as the prognostic uncertainty representation, involving particle filter (PF), Relevance Vector Machine (RVM), etc. This paper presents a fusion RUL prediction approach with PF algorithm and data-driven autoregression (AR) algorithm for lithium-ion battery. Moreover, a framework to quantitatively analyze and evaluate the PDF distribution of the lithium-ion battery RUL prediction is presented. The probability confidence interval estimation, PDF histogram and distribution hypothesis test are included in quantifying the uncertainty. These quantitative analysis results can be meaningful for lithium-ion battery health management and maintenance. The experimental results with the battery data of NASA Ames Prognostics Data Repository show that the proposed framework can achieve the quantification of PDF to introduce the reference for the corresponding maintenance and management. The proposed work also shows potential prospective for industrial application.
{"title":"Uncertainty quantification of fusion prognostics for lithium-ion battery remaining useful life estimation","authors":"Datong Liu, Yue Luo, Limeng Guo, Yu Peng","doi":"10.1109/ICPHM.2013.6621441","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621441","url":null,"abstract":"The uncertainty of prognostics and remaining useful life (RUL) estimation for the lithium-ion battery is emphasized in the battery management system (BMS). Many machine learning algorithms and statistical methods can not only realize the RUL prediction but also provide the probability density function (PDF) as the prognostic uncertainty representation, involving particle filter (PF), Relevance Vector Machine (RVM), etc. This paper presents a fusion RUL prediction approach with PF algorithm and data-driven autoregression (AR) algorithm for lithium-ion battery. Moreover, a framework to quantitatively analyze and evaluate the PDF distribution of the lithium-ion battery RUL prediction is presented. The probability confidence interval estimation, PDF histogram and distribution hypothesis test are included in quantifying the uncertainty. These quantitative analysis results can be meaningful for lithium-ion battery health management and maintenance. The experimental results with the battery data of NASA Ames Prognostics Data Repository show that the proposed framework can achieve the quantification of PDF to introduce the reference for the corresponding maintenance and management. The proposed work also shows potential prospective for industrial application.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"24 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":"131838448","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.2013.6621456
Sheng Hong, Zheng Zhou, Chuan Lv, Hongyi Guo
The failure issues of power insulated gate bipolar transistor (IGBT) modules are mainly related to thermal and thermo-mechanical aging mechanism. This aging causes degradation of the device performance and faults which can lead to the failure bringing huge loss and catastrophic influence. To avoid those failures, the monitoring of the device operation and the detection of an aging state remain as a priority. This paper, at first, describes the failure mechanism of power cycling by analyzing of the structure of lead-based solder and joint failure due to solder fatigue. Secondly, Gaussian Process Regression (GPR) model supporting uncertainty representation is used to realize the prognosis for the junction temperature of the IGBT. Furthermore, the comparison of GPR prediction with the Neural Network algorithm has been achieved, and the dynamic model is introduced to improve the prediction accuracy for the IGBT health assessment. Meantime, GPR owns more simple computational complexity and less time consuming.
{"title":"Prognosis for insulated gate bipolar transistor based on Gaussian Process Regression","authors":"Sheng Hong, Zheng Zhou, Chuan Lv, Hongyi Guo","doi":"10.1109/ICPHM.2013.6621456","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621456","url":null,"abstract":"The failure issues of power insulated gate bipolar transistor (IGBT) modules are mainly related to thermal and thermo-mechanical aging mechanism. This aging causes degradation of the device performance and faults which can lead to the failure bringing huge loss and catastrophic influence. To avoid those failures, the monitoring of the device operation and the detection of an aging state remain as a priority. This paper, at first, describes the failure mechanism of power cycling by analyzing of the structure of lead-based solder and joint failure due to solder fatigue. Secondly, Gaussian Process Regression (GPR) model supporting uncertainty representation is used to realize the prognosis for the junction temperature of the IGBT. Furthermore, the comparison of GPR prediction with the Neural Network algorithm has been achieved, and the dynamic model is introduced to improve the prediction accuracy for the IGBT health assessment. Meantime, GPR owns more simple computational complexity and less time consuming.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"426 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":"132064312","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.2013.6621443
Yaqiu Li, Guangyan Zhao, Yufeng Sun, Weiwei Hu
Aimed at the long-term store and one-time use characteristics of missiles, activities of failures, maintenance and support in storage process were simulated to evaluate the missile availability. Missile availability simulation model considering storage process was developed based on the idea of dynamic simulation model for discrete events. Relevant simulation logics were designed, and the task process and fault sampling simulation logics were described in detail. Finally, the comparison was made between two simulation model cases: one considered the storage process and the other did not. The result shows that the simulation module and algorithm are correct. The simulation method of considering the effects of storage on the missile availability are proposed, which can provide technical support for demonstration and evaluation of the missile's indexes of reliability, maintainability and supportability.
{"title":"Simulation on missile availability with consideration to storage process","authors":"Yaqiu Li, Guangyan Zhao, Yufeng Sun, Weiwei Hu","doi":"10.1109/ICPHM.2013.6621443","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621443","url":null,"abstract":"Aimed at the long-term store and one-time use characteristics of missiles, activities of failures, maintenance and support in storage process were simulated to evaluate the missile availability. Missile availability simulation model considering storage process was developed based on the idea of dynamic simulation model for discrete events. Relevant simulation logics were designed, and the task process and fault sampling simulation logics were described in detail. Finally, the comparison was made between two simulation model cases: one considered the storage process and the other did not. The result shows that the simulation module and algorithm are correct. The simulation method of considering the effects of storage on the missile availability are proposed, which can provide technical support for demonstration and evaluation of the missile's indexes of reliability, maintainability and supportability.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"50 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":"125420950","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.2013.6621435
Seunghyuk Choi, B. Youn, M. Pecht
Portable electronic makers have introduced a liquid damage indicator (LDI) tape in their products to detect warranty abuse caused by water damage. They refused to provide warranty service for a device that has triggered the LDI tape. However, a LDI tape exhibits inconsistencies in characteristics and that can lead to improper warranty denials. In this study, we conducted life tests to evaluate the performance of LDI tapes and developed an empirical model for liquid contact that describes the performance of the indicator for the benefit of the public. Then, we validated the model by a nonparametric survival analysis and temperature cycling tests with two smartphone sets.
{"title":"A liquid contact indicator model for warranty abuse detection in portable electronics","authors":"Seunghyuk Choi, B. Youn, M. Pecht","doi":"10.1109/ICPHM.2013.6621435","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621435","url":null,"abstract":"Portable electronic makers have introduced a liquid damage indicator (LDI) tape in their products to detect warranty abuse caused by water damage. They refused to provide warranty service for a device that has triggered the LDI tape. However, a LDI tape exhibits inconsistencies in characteristics and that can lead to improper warranty denials. In this study, we conducted life tests to evaluate the performance of LDI tapes and developed an empirical model for liquid contact that describes the performance of the indicator for the benefit of the public. Then, we validated the model by a nonparametric survival analysis and temperature cycling tests with two smartphone sets.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"53 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":"125112749","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.2013.6621442
P. D. Van, A. Voisin, E. Levrat, B. Iung
This paper deals with a proactive condition-based maintenance (CBM) considering both perfect and imperfect maintenance actions for deteriorating systems. Perfect maintenance actions restore completely the system to the `as good as new' state. Their related cost are however often high. Imperfect preventive maintenance restores partially the system with reduced maintenance cost. Nevertheless, the restoration quality may depend not only on the allocated maintenance cost, the system age but also on the total number of imperfect maintenance actions executed since the last replacement time of the system. The first objective of the paper is to model imperfect maintenance actions. The second objective is to propose an adaptive maintenance policy which can help to select optimally maintenance actions (perfect or imperfect actions), if needed, at each inspection time using the remaining useful life (RUL) prognosis. To illustrate the use and the advantages of the proposed maintenance policy, different sensitivity studies are analyzed and discussed through a numerical example.
{"title":"Remaining useful life based maintenance decision making for deteriorating systems with both perfect and imperfect maintenance actions","authors":"P. D. Van, A. Voisin, E. Levrat, B. Iung","doi":"10.1109/ICPHM.2013.6621442","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621442","url":null,"abstract":"This paper deals with a proactive condition-based maintenance (CBM) considering both perfect and imperfect maintenance actions for deteriorating systems. Perfect maintenance actions restore completely the system to the `as good as new' state. Their related cost are however often high. Imperfect preventive maintenance restores partially the system with reduced maintenance cost. Nevertheless, the restoration quality may depend not only on the allocated maintenance cost, the system age but also on the total number of imperfect maintenance actions executed since the last replacement time of the system. The first objective of the paper is to model imperfect maintenance actions. The second objective is to propose an adaptive maintenance policy which can help to select optimally maintenance actions (perfect or imperfect actions), if needed, at each inspection time using the remaining useful life (RUL) prognosis. To illustrate the use and the advantages of the proposed maintenance policy, different sensitivity studies are analyzed and discussed through a numerical example.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"300 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":"123397538","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.2013.6621425
R. Meyer, J. Coble, P. Ramuhalli
Advanced small modular reactors (aSMRs), which are based on modularization of advanced reactor concepts, may provide a longer-term alternative to traditional light-water reactors and near term small modular reactors (SMRs), which are based on integral pressurized water reactor (iPWR) concepts. aSMRs are conceived for applications in remote locations and for diverse missions that include providing process or district heating, water desalination, and hydrogen production. Several challenges exist with respect to cost-effective operations and maintenance (O&M) of aSMRs, including the impacts of aggressive operating environments and modularity, and limiting these costs and staffing needs will be essential to ensuring the economic feasibility of aSMR deployment. In this regard, prognostic health management (PHM) systems have the potential to play a vital role in supporting the deployment of aSMR systems. This paper identifies requirements and technical gaps associated with implementation of PHM systems for passive aSMR components.
{"title":"Requirements for prognostic health management of passive components in advanced small modular reactors","authors":"R. Meyer, J. Coble, P. Ramuhalli","doi":"10.1109/ICPHM.2013.6621425","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621425","url":null,"abstract":"Advanced small modular reactors (aSMRs), which are based on modularization of advanced reactor concepts, may provide a longer-term alternative to traditional light-water reactors and near term small modular reactors (SMRs), which are based on integral pressurized water reactor (iPWR) concepts. aSMRs are conceived for applications in remote locations and for diverse missions that include providing process or district heating, water desalination, and hydrogen production. Several challenges exist with respect to cost-effective operations and maintenance (O&M) of aSMRs, including the impacts of aggressive operating environments and modularity, and limiting these costs and staffing needs will be essential to ensuring the economic feasibility of aSMR deployment. In this regard, prognostic health management (PHM) systems have the potential to play a vital role in supporting the deployment of aSMR systems. This paper identifies requirements and technical gaps associated with implementation of PHM systems for passive aSMR components.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"41 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":"129865993","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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