Pub Date : 2014-06-22DOI: 10.1109/ICPHM.2014.7036366
V. Agarwal, N. Lybeck, R. Bickford, Richard Rusaw
Proactive online monitoring in the nuclear industry is being explored using the Electric Power Research Institute's Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software. The FW-PHM Suite is a set of web-based diagnostic and prognostic tools and databases that serves as an integrated health monitoring architecture. The FW-PHM Suite has four main modules: (1) Diagnostic Advisor, (2) Asset Fault Signature Database, (3) Remaining Useful Life Advisor, and (4) Remaining Useful Life Database. This paper focuses on development of asset fault signatures to assess the health status of generator step-up generators and emergency diesel generators in nuclear power plants. Asset fault signatures describe distinctive features based on technical examinations that can be used to detect a specific fault type. At the most basic level, fault signatures are comprised of an asset type, a fault type, and a set of one or more fault features (symptoms) that are indicative of the specified fault. The Asset Fault Signature Database is populated with asset fault signatures via a content development exercise that is based on the results of intensive technical research and on the knowledge and experience of technical experts. The developed fault signatures capture this knowledge and implement it in a standardized approach, thereby streamlining the diagnostic and prognostic process. This will support the automation of proactive online monitoring techniques in nuclear power plants to diagnose incipient faults, perform proactive maintenance, and estimate the remaining useful life of assets.
{"title":"Development of asset fault signatures for Prognostic and Health Management in the nuclear industry","authors":"V. Agarwal, N. Lybeck, R. Bickford, Richard Rusaw","doi":"10.1109/ICPHM.2014.7036366","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036366","url":null,"abstract":"Proactive online monitoring in the nuclear industry is being explored using the Electric Power Research Institute's Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software. The FW-PHM Suite is a set of web-based diagnostic and prognostic tools and databases that serves as an integrated health monitoring architecture. The FW-PHM Suite has four main modules: (1) Diagnostic Advisor, (2) Asset Fault Signature Database, (3) Remaining Useful Life Advisor, and (4) Remaining Useful Life Database. This paper focuses on development of asset fault signatures to assess the health status of generator step-up generators and emergency diesel generators in nuclear power plants. Asset fault signatures describe distinctive features based on technical examinations that can be used to detect a specific fault type. At the most basic level, fault signatures are comprised of an asset type, a fault type, and a set of one or more fault features (symptoms) that are indicative of the specified fault. The Asset Fault Signature Database is populated with asset fault signatures via a content development exercise that is based on the results of intensive technical research and on the knowledge and experience of technical experts. The developed fault signatures capture this knowledge and implement it in a standardized approach, thereby streamlining the diagnostic and prognostic process. This will support the automation of proactive online monitoring techniques in nuclear power plants to diagnose incipient faults, perform proactive maintenance, and estimate the remaining useful life of assets.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"4 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":"114625733","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.2014.7036389
Guangxing Bai, Pingfeng Wang
Li-ion battery failures are becoming the major concern in the battery application area. A failure such as sudden capacity loss could cause catastrophic damage and loss. Li-plating, a reason causing short circuit and capacity loss, is arousing researchers' and manufacturers' interest today. Based on mechanisms of Li-plating, this paper proposed a new approach to detect the occurrence of Li-plating. With the simulation of Li-plating using COMSOL, the proposed Li-plating occurrence model is implemented under different conditions. The experimental results indicate the local effects and the onset timing of Li-plating.
{"title":"Multiphysics based failure identification of lithium battery failure for prognostics","authors":"Guangxing Bai, Pingfeng Wang","doi":"10.1109/ICPHM.2014.7036389","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036389","url":null,"abstract":"Li-ion battery failures are becoming the major concern in the battery application area. A failure such as sudden capacity loss could cause catastrophic damage and loss. Li-plating, a reason causing short circuit and capacity loss, is arousing researchers' and manufacturers' interest today. Based on mechanisms of Li-plating, this paper proposed a new approach to detect the occurrence of Li-plating. With the simulation of Li-plating using COMSOL, the proposed Li-plating occurrence model is implemented under different conditions. The experimental results indicate the local effects and the onset timing of Li-plating.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"13 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":"121304144","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.2014.7036380
Yunnhon Lo, S. B. Johnson, Jonanthan T. Breckenridge
This paper describes the quantitative application of the theory of System Health Management and its operational subset, Fault Management, to the selection of Abort Triggers for a human-rated launch vehicle, the United States' National Aeronautics and Space Administration's (NASA) Space Launch System (SLS). The results demonstrate the efficacy of the theory to assess the effectiveness of candidate failure detection and response mechanisms to protect humans from time-critical and severe hazards. The quantitative method was successfully used on the SLS to aid selection of its suite of Abort Triggers.
{"title":"Application of Fault Management theory to the quantitative selection of a launch vehicle Abort Trigger suite","authors":"Yunnhon Lo, S. B. Johnson, Jonanthan T. Breckenridge","doi":"10.1109/ICPHM.2014.7036380","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036380","url":null,"abstract":"This paper describes the quantitative application of the theory of System Health Management and its operational subset, Fault Management, to the selection of Abort Triggers for a human-rated launch vehicle, the United States' National Aeronautics and Space Administration's (NASA) Space Launch System (SLS). The results demonstrate the efficacy of the theory to assess the effectiveness of candidate failure detection and response mechanisms to protect humans from time-critical and severe hazards. The quantitative method was successfully used on the SLS to aid selection of its suite of Abort Triggers.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"124 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":"128146508","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.2014.7036394
Jingyue Pang, Datong Liu, H. Liao, Yu Peng, Xiyuan Peng
Condition monitoring has gradually become the necessary part of the diagnostics and prognostics for the complex systems. Especially, with the rapid development of data acquisition and communication technology, the appearing of large scale data set and data stream brings great challenges to model and process the condition monitoring data As a result, anomaly detection of the streaming monitoring data attracts more attention in the fields of prognostics and health management (PHM). Hence, in this study, Gaussian process regression (GPR) is applied for the abnormal detection in data stream; and on this basis a real-time abnormal detection method is proposed based on the improved anomaly detection and mitigation (IADAM) strategy and GPR which realizes incremental detecting for future data samples and requires no pre-classification labels of anomalies. Anomaly detection tested on an artificial data set and actual mobile traffic data set indicates the effectiveness and reasonability of IADAM-GPR model compared with naïve and Multilayer Perceptron (MLP) models.
{"title":"Anomaly detection based on data stream monitoring and prediction with improved Gaussian process regression algorithm","authors":"Jingyue Pang, Datong Liu, H. Liao, Yu Peng, Xiyuan Peng","doi":"10.1109/ICPHM.2014.7036394","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036394","url":null,"abstract":"Condition monitoring has gradually become the necessary part of the diagnostics and prognostics for the complex systems. Especially, with the rapid development of data acquisition and communication technology, the appearing of large scale data set and data stream brings great challenges to model and process the condition monitoring data As a result, anomaly detection of the streaming monitoring data attracts more attention in the fields of prognostics and health management (PHM). Hence, in this study, Gaussian process regression (GPR) is applied for the abnormal detection in data stream; and on this basis a real-time abnormal detection method is proposed based on the improved anomaly detection and mitigation (IADAM) strategy and GPR which realizes incremental detecting for future data samples and requires no pre-classification labels of anomalies. Anomaly detection tested on an artificial data set and actual mobile traffic data set indicates the effectiveness and reasonability of IADAM-GPR model compared with naïve and Multilayer Perceptron (MLP) models.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"19 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":"125407681","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.2014.7036374
Xihui Liang, M. Zuo, Mohammad R. Hoseini
This paper investigates the vibration properties of a planetary gear set. A two-dimensional lumped mass model is developed to simulate the vibration signals of a planetary gear set in the perfect and crack situations. Through dynamic simulation, the vibration signals of each individual component can be simulated, including the vibration signals of the sun gear, each planet gear, and the ring gear. By incorporating the effect of transmission path, resultant vibration signals of the gearbox at the transducer location are obtained. Results show obvious fault symptoms in the signals of an individual component, such as the sun gear. After going through the transmission path, amplitude modulation is shown in the resultant vibration signals. When there is a crack on a sun gear tooth, a large amount of sidebands appears in the vibration spectrum. The locations of these sidebands are investigated and identified, which are helpful for fault detection.
{"title":"Understanding vibration properties of a planetary gear set for fault detection","authors":"Xihui Liang, M. Zuo, Mohammad R. Hoseini","doi":"10.1109/ICPHM.2014.7036374","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036374","url":null,"abstract":"This paper investigates the vibration properties of a planetary gear set. A two-dimensional lumped mass model is developed to simulate the vibration signals of a planetary gear set in the perfect and crack situations. Through dynamic simulation, the vibration signals of each individual component can be simulated, including the vibration signals of the sun gear, each planet gear, and the ring gear. By incorporating the effect of transmission path, resultant vibration signals of the gearbox at the transducer location are obtained. Results show obvious fault symptoms in the signals of an individual component, such as the sun gear. After going through the transmission path, amplitude modulation is shown in the resultant vibration signals. When there is a crack on a sun gear tooth, a large amount of sidebands appears in the vibration spectrum. The locations of these sidebands are investigated and identified, which are helpful for fault detection.","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":"126315207","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.2014.7036396
Qi Li, Zhanbao Gao, L. Shao
This paper presents a prognostics approach based on operating condition for estimating the Remaining Useful Life (RUL). Operating condition is used to describe the state or environment of a system. This approach is suit for the dataset that contains sensor measurements and operational settings. Predicting RUL contains two stages: modeling stage using the training dataset and predicting stage using the result of modeling and testing dataset. This approach can increase available information in modeling stage and simulate the actual work situation of the test unit in the predicting stage. The performance of this approach was tested by the dataset from 2008 PHM Data Challenge Competition where sensor measurements and operational settings were provided. The task of the competition was to estimate the RUL of an unspecified system. The results showed that this prognostic method could get accurate predictions in most situations and had a good rank in all competition results.
{"title":"An operating condition classified prognostics approach for Remaining Useful Life estimation","authors":"Qi Li, Zhanbao Gao, L. Shao","doi":"10.1109/ICPHM.2014.7036396","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036396","url":null,"abstract":"This paper presents a prognostics approach based on operating condition for estimating the Remaining Useful Life (RUL). Operating condition is used to describe the state or environment of a system. This approach is suit for the dataset that contains sensor measurements and operational settings. Predicting RUL contains two stages: modeling stage using the training dataset and predicting stage using the result of modeling and testing dataset. This approach can increase available information in modeling stage and simulate the actual work situation of the test unit in the predicting stage. The performance of this approach was tested by the dataset from 2008 PHM Data Challenge Competition where sensor measurements and operational settings were provided. The task of the competition was to estimate the RUL of an unspecified system. The results showed that this prognostic method could get accurate predictions in most situations and had a good rank in all competition results.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"59 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":"121518010","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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