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Residual life prediction for complex systems with multi-phase degradation by ARMA-filtered hidden Markov model
Sheng, Zhidong1; Hu, Qingpei2; Liu, Jian3; Yu, Dan2,4
2019-01-02
Source PublicationQUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT
ISSN1684-3703
Volume16Issue:1Pages:19-35
AbstractThe performance of certain critical complex systems, such as the power output of ground photovoltaic (PV) modules or spacecraft solar arrays, exhibits a multi-phase degradation pattern due to the redundant structure. This pattern shows a degradation trend with multiple jump points, which are mixed effects of two failure modes: a soft mode of continuous smooth degradation and a hard mode of abrupt failure. Both modes need to be modeled jointly to predict the system residual life. In this paper, an autoregressive moving average model-filtered hidden Markov model is proposed to fit the multi-phase degradation data with unknown number of jump points, together with an iterative algorithm for parameter estimation. The comprehensive algorithm is composed of non-linear least-square method, recursive extended least-square method, and expectation-maximization algorithm to handle different parts of the model. The proposed methodology is applied to a specific PV module system with simulated performance measurements for its reliability evaluation and residual life prediction. Comprehensive studies have been conducted, and analysis results show better performance over competing models and more importantly all the jump points in the simulated data have been identified. Also, this algorithm converges fast with satisfactory parameter estimates accuracy, regardless of the jump point number.
KeywordSystem reliability residual life prediction multi-phase degradation hidden Markov model
DOI10.1080/16843703.2017.1335496
Language英语
Funding ProjectNational Center for Mathematics and Interdisciplinary Sciences (CAS) ; Key Laboratory of Systems and Control (CAS)
WOS Research AreaEngineering ; Operations Research & Management Science ; Mathematics
WOS SubjectEngineering, Industrial ; Operations Research & Management Science ; Statistics & Probability
WOS IDWOS:000456947900002
PublisherNCTU-NATIONAL CHIAO TUNG UNIV PRESS
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/32308
Collection系统科学研究所
Affiliation1.Univ Sci & Technol China, Sch Management, Hefei, Anhui, Peoples R China
2.Chinese Acad Sci, Qual & Data Sci Ctr, Acad Math & Syst Sci, Beijing, Peoples R China
3.Univ Arizona, Dept Syst & Ind Engn, Tucson, AZ 85721 USA
4.Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Sheng, Zhidong,Hu, Qingpei,Liu, Jian,et al. Residual life prediction for complex systems with multi-phase degradation by ARMA-filtered hidden Markov model[J]. QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT,2019,16(1):19-35.
APA Sheng, Zhidong,Hu, Qingpei,Liu, Jian,&Yu, Dan.(2019).Residual life prediction for complex systems with multi-phase degradation by ARMA-filtered hidden Markov model.QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT,16(1),19-35.
MLA Sheng, Zhidong,et al."Residual life prediction for complex systems with multi-phase degradation by ARMA-filtered hidden Markov model".QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT 16.1(2019):19-35.
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