Pub Date : 2013-06-24DOI: 10.1109/ICPHM.2013.6621427
Jae Yoon, D. He, Bin Qiu
In this paper, an integrated full ceramic bearing fault diagnostic system developed with acoustic emission (AE) sensors and a large memory storage and retrieval (LAMSTAR) artificial neural network (ANN) is presented. LAMSTAR is a newly developed and US patented neural network algorithm. The performance of the diagnostic system is compared with those implemented with other types of fault classification algorithms using laboratory seeded fault test data. The presented diagnostic system with LAMSTAR network achieved over 93% individual fault detection accuracies along with over 96% overall accuracy.
{"title":"Full ceramic bearing fault diagnosis using LAMSTAR neural network","authors":"Jae Yoon, D. He, Bin Qiu","doi":"10.1109/ICPHM.2013.6621427","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621427","url":null,"abstract":"In this paper, an integrated full ceramic bearing fault diagnostic system developed with acoustic emission (AE) sensors and a large memory storage and retrieval (LAMSTAR) artificial neural network (ANN) is presented. LAMSTAR is a newly developed and US patented neural network algorithm. The performance of the diagnostic system is compared with those implemented with other types of fault classification algorithms using laboratory seeded fault test data. The presented diagnostic system with LAMSTAR network achieved over 93% individual fault detection accuracies along with over 96% overall accuracy.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"94 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":"126235261","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.2013.6621455
Zili Wang, Zhipeng Wang
Hydraulic pump is the critical part of a hydraulic system. The diagnosis of hydraulic pump is very crucial for reliability. This paper studies on a Chaotic Parallel Support Vector Machine (CPSVM) and employs it for fault diagnosis of hydraulic pump. The CPSVM combines the chaos theory and a number of SVMs connected in parallel. Phase-space reconstruction of chaos theory is utilized to determine the dimension of input vectors for each SVM. Each SVM has an output. A weighted sum of each output is considered as the output of the CPSVM. To diagnose faults of hydraulic pump, a residual error generator is designed based on the CPSVM. This residual error generator is firstly trained using data from normal state. Then, it can be used for fault clustering by analysis of the residual error. Its performance and effectiveness has also been validated via a plunger pump test-bed.
{"title":"Chaotic Parallel Support Vector Machine and its application for fault diagnosis of hydraulic pump","authors":"Zili Wang, Zhipeng Wang","doi":"10.1109/ICPHM.2013.6621455","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621455","url":null,"abstract":"Hydraulic pump is the critical part of a hydraulic system. The diagnosis of hydraulic pump is very crucial for reliability. This paper studies on a Chaotic Parallel Support Vector Machine (CPSVM) and employs it for fault diagnosis of hydraulic pump. The CPSVM combines the chaos theory and a number of SVMs connected in parallel. Phase-space reconstruction of chaos theory is utilized to determine the dimension of input vectors for each SVM. Each SVM has an output. A weighted sum of each output is considered as the output of the CPSVM. To diagnose faults of hydraulic pump, a residual error generator is designed based on the CPSVM. This residual error generator is firstly trained using data from normal state. Then, it can be used for fault clustering by analysis of the residual error. Its performance and effectiveness has also been validated via a plunger pump test-bed.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"36 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":"117318569","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.2013.6621438
Xiaowei Xu, Shi-dong Fan, Haofei Huang, Hanhua Zhu, Quan Wen
In order to change the current disadvantages of traditional ship maintenance concept, this article plans to apply the technical method of prognostics and health management (PHM) to improve equipment maintenance program and repair schedule. In the meantime, this thesis takes account into the example of the dredger power system of the Waterway Bureau to establish the PHM optimized system of life cycle ship maintenance program, which include the system architecture, work platforms and optimize processes.
{"title":"Research on PHM technology application of ship maintenance program optimization","authors":"Xiaowei Xu, Shi-dong Fan, Haofei Huang, Hanhua Zhu, Quan Wen","doi":"10.1109/ICPHM.2013.6621438","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621438","url":null,"abstract":"In order to change the current disadvantages of traditional ship maintenance concept, this article plans to apply the technical method of prognostics and health management (PHM) to improve equipment maintenance program and repair schedule. In the meantime, this thesis takes account into the example of the dredger power system of the Waterway Bureau to establish the PHM optimized system of life cycle ship maintenance program, which include the system architecture, work platforms and optimize processes.","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":"130673709","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.2013.6621424
A. Naganathan, M. Er, Xiang Li, H. Chan, Honglei Li, Jiaming Li, G. Vachtsevanos
The time for the occurrence of failure in a machine has been predicted using a Weibull model. The model uses the information of past failures and fits it into a probability distribution that yields a prediction of future failures. The operational data used for analysis is a series of failure times procured from an industrial machine used in a manufacturing system. This paper discusses three methods of parametric estimation of the Weibull distribution, namely the maximum likelihood estimation, the method of moments, and the least squares method, and compares their errors in estimation. In addition, for the maximum likelihood estimation method, we identify the parametric estimation error for various observation lengths to show the tradeoff between inspection load and error, and a time-to-failure prediction based on the parameters estimated.
{"title":"Complete parametric estimation of the Weibull model with an optimized inspection interval","authors":"A. Naganathan, M. Er, Xiang Li, H. Chan, Honglei Li, Jiaming Li, G. Vachtsevanos","doi":"10.1109/ICPHM.2013.6621424","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621424","url":null,"abstract":"The time for the occurrence of failure in a machine has been predicted using a Weibull model. The model uses the information of past failures and fits it into a probability distribution that yields a prediction of future failures. The operational data used for analysis is a series of failure times procured from an industrial machine used in a manufacturing system. This paper discusses three methods of parametric estimation of the Weibull distribution, namely the maximum likelihood estimation, the method of moments, and the least squares method, and compares their errors in estimation. In addition, for the maximum likelihood estimation method, we identify the parametric estimation error for various observation lengths to show the tradeoff between inspection load and error, and a time-to-failure prediction based on the parameters estimated.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"35 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":"115491995","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.2013.6621448
S. Evans, P. Mishra, Weizhong Yan, Bouchra Bouqata
In this paper we cast a vision for Security Prognostics (SP) for critical systems, promoting the view that security related protections would be well served to integrate fully with Monitoring and Diagnostics (M&D) systems that assess the health of complex assets and systems. To detect complex Cyber threats we propose combining system parameters already in use by M&D systems for Prognostics and Health Monitoring (PHM) with security parameters. Combining system parameters used by M&D to detect non-malicious faults with the system parameters used by security schemes to detect complex Cyber threats will improve: (a) accuracy of PHM (b) security of M&D, and (c) availability and safety of critical systems. We also introduce the notion of Remaining Secure Life (RSL), assessed based on the propagation of “security damage,” to create the prospect for Security Prognostics. RSL will assist in the selection of appropriate response(s), based on breach or compromise to security component's and potential impact on system operation. An example of M&D data is provided which is normally associated with non-malicious faults providing input to detect Malware execution through time series monitoring.
{"title":"Security Prognostics: Cyber meets PHM","authors":"S. Evans, P. Mishra, Weizhong Yan, Bouchra Bouqata","doi":"10.1109/ICPHM.2013.6621448","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621448","url":null,"abstract":"In this paper we cast a vision for Security Prognostics (SP) for critical systems, promoting the view that security related protections would be well served to integrate fully with Monitoring and Diagnostics (M&D) systems that assess the health of complex assets and systems. To detect complex Cyber threats we propose combining system parameters already in use by M&D systems for Prognostics and Health Monitoring (PHM) with security parameters. Combining system parameters used by M&D to detect non-malicious faults with the system parameters used by security schemes to detect complex Cyber threats will improve: (a) accuracy of PHM (b) security of M&D, and (c) availability and safety of critical systems. We also introduce the notion of Remaining Secure Life (RSL), assessed based on the propagation of “security damage,” to create the prospect for Security Prognostics. RSL will assist in the selection of appropriate response(s), based on breach or compromise to security component's and potential impact on system operation. An example of M&D data is provided which is normally associated with non-malicious faults providing input to detect Malware execution through time series monitoring.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"28 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":"132205414","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.2013.6621430
B. Lamoureux, J. Masse, N. Mechbal
This document introduces a hybrid approach for fault detection and identification of an aircraft engine pumping unit. It is based on the complementarity between a model-based approach accounting for uncertainties aimed at quantifying the degradation modes signatures and a data-driven approach aimed at recalibrating the healthy syndrome from measures. Because of the computational time costs of uncertainties propagation into the physics based model, a surrogate modeling technic called Kriging associated to Latin hypercube sampling is utilized. The hybrid approach is tested on a pumping unit of an aircraft engine and shows good results for computing the degradation modes signatures and performing their detection and identification.
{"title":"Diagnostics of an aircraft engine pumping unit using a hybrid approach based-on surrogate modeling","authors":"B. Lamoureux, J. Masse, N. Mechbal","doi":"10.1109/ICPHM.2013.6621430","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621430","url":null,"abstract":"This document introduces a hybrid approach for fault detection and identification of an aircraft engine pumping unit. It is based on the complementarity between a model-based approach accounting for uncertainties aimed at quantifying the degradation modes signatures and a data-driven approach aimed at recalibrating the healthy syndrome from measures. Because of the computational time costs of uncertainties propagation into the physics based model, a surrogate modeling technic called Kriging associated to Latin hypercube sampling is utilized. The hybrid approach is tested on a pumping unit of an aircraft engine and shows good results for computing the degradation modes signatures and performing their detection and identification.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"13 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":"129962077","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.2013.6621429
D. Lin, F. Labeau, Guixia Kang
Increasing more patients (especially the elderly) suffering from comorbid diseases motivates us to develop a decision support system which can combine multiple concurrently applied disease-specific clinical practice guidelines. Combining multiple guidelines is not naively running each guideline one by one. Instead, the occurance of potential conflicts between guidelines must be detected and mitigated by a decision support system. In this paper, we present the design of a telecommunication and computer technology based system for monitoring hypertensive patients in the presence of a comorbid condition. The core of this system is a decision support unit, which performs combining of guidelines for different diseases in view of the potential conflict occurring. The architecture of our system, including this decision support unit, is detailed in this paper. To show the reliability of our system, we analyze the ability of a decision support system to correctly make decisions as well as to detect the conflict between guidelines.
{"title":"Decision support system for monitoring hypertensive patients in a comorbid condition","authors":"D. Lin, F. Labeau, Guixia Kang","doi":"10.1109/ICPHM.2013.6621429","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621429","url":null,"abstract":"Increasing more patients (especially the elderly) suffering from comorbid diseases motivates us to develop a decision support system which can combine multiple concurrently applied disease-specific clinical practice guidelines. Combining multiple guidelines is not naively running each guideline one by one. Instead, the occurance of potential conflicts between guidelines must be detected and mitigated by a decision support system. In this paper, we present the design of a telecommunication and computer technology based system for monitoring hypertensive patients in the presence of a comorbid condition. The core of this system is a decision support unit, which performs combining of guidelines for different diseases in view of the potential conflict occurring. The architecture of our system, including this decision support unit, is detailed in this paper. To show the reliability of our system, we analyze the ability of a decision support system to correctly make decisions as well as to detect the conflict between guidelines.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"19 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":"126729564","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 : 2012-05-23DOI: 10.1109/ICPHM.2013.6621445
Mohamed-Hedi Karray, B. Chebel-Morello, N. Zerhouni
Prognostics and Health Management platforms are founded on Condition Based Maintenance process. Major works on this topic are focused on the diagnostic and prognostic modules and neglect the Decision Support Module which must be investigated to give more efficiency to PHM platforms. To improve this module with intelligence and rapidity we propose in this work to integrate a Trace Based System (TBS) into the decision support module. This TBS provides three main services (Traceability, self-learning and self-management) which are developed in this work.
{"title":"A trace based system for decision activities in CBM process","authors":"Mohamed-Hedi Karray, B. Chebel-Morello, N. Zerhouni","doi":"10.1109/ICPHM.2013.6621445","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621445","url":null,"abstract":"Prognostics and Health Management platforms are founded on Condition Based Maintenance process. Major works on this topic are focused on the diagnostic and prognostic modules and neglect the Decision Support Module which must be investigated to give more efficiency to PHM platforms. To improve this module with intelligence and rapidity we propose in this work to integrate a Trace Based System (TBS) into the decision support module. This TBS provides three main services (Traceability, self-learning and self-management) which are developed in this work.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126501721","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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