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A method for degradation prediction based on Hidden Semi-Markov models with mixture of Kernels
Yang TJ(杨天吉)1,2; Zheng ZY(郑泽宇)1,2,3; Qi L(亓亮)4
Department数字工厂研究室
Source PublicationComputers in Industry
ISSN0166-3615
2020
Volume122Pages:1-13
Indexed BySCI ; EI
EI Accession number20203309060405
WOS IDWOS:000571219600002
Contribution Rank1
Funding OrganizationNational Key Research and Devel-opment Program of China under Grant 2018YFF0214704
KeywordPrognostics and health management Remaining useful lifetime Hidden semi-Markov models Kernel method approximationa
Abstract

The degradation prediction of equipment is a crucial task in Prognostics and Health Management. This paper proposes an integrated method for data-driven prognosis based on Hidden Semi-Markov Models (HSMM) with kernel methods. However, unlike the assumption of a mixture of Gaussian distribution of emitting probability, we approximate the probabilities of multidimensional condition monitoring data as a linear combination of kernel functions. This method can achieve high-dimensional function fitting with limited parameters. Then, the procedures of parameter re-estimation and kernel center selection are developed. The reliability of equipment is estimated by the posterior probabilities. Finally, we give an integrated framework including offline training and online prediction processes. Some experiments are conducted on an open dataset of aircraft engines. Compared with other HSMM-based methods, it shows that the proposed method is more accurate and credible in RUL prediction. The shape of a mixture of kernels approximation is different from the Gaussian-type of distribution, which impacts the parameters of the degradation model. Therefore, the proposed method can identify a short-term warning state.

Language英语
WOS SubjectComputer Science, Interdisciplinary Applications
WOS KeywordREMAINING USEFUL LIFE ; EQUIPMENT HEALTH DIAGNOSIS ; FAULT-DETECTION ; PROGNOSTICS ; SYSTEMS ; ALGORITHM ; FRAMEWORK ; LSTM
WOS Research AreaComputer Science
Funding ProjectNational Key Research and Development Program of China[2018YFF0214704]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/27540
Collection数字工厂研究室
Corresponding AuthorYang TJ(杨天吉)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Recommended Citation
GB/T 7714
Yang TJ,Zheng ZY,Qi L. A method for degradation prediction based on Hidden Semi-Markov models with mixture of Kernels[J]. Computers in Industry,2020,122:1-13.
APA Yang TJ,Zheng ZY,&Qi L.(2020).A method for degradation prediction based on Hidden Semi-Markov models with mixture of Kernels.Computers in Industry,122,1-13.
MLA Yang TJ,et al."A method for degradation prediction based on Hidden Semi-Markov models with mixture of Kernels".Computers in Industry 122(2020):1-13.
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