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Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation
Yang, Wensi1,4; Yao QF(么庆丰)2,4; Ye KJ(叶可江)1; Xu CZ(须成忠)3
Department数字工厂研究室
Source PublicationINTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING
ISSN0885-7458
2020
Volume48Issue:1Pages:61-79
Indexed BySCI ; EI
EI Accession number20194707718699
WOS IDWOS:000496196700001
Contribution Rank2
Funding OrganizationNational Key R&D Program of China [2018YFB1004804] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [61702492] ; Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence ; Shenzhen Basic Research Program [JCYJ20170818153016513]
KeywordConvolutional neural networks Empirical mode decomposition Remaining useful life Reliability
Abstract

Remaining useful life (RUL) prediction plays an important role in guaranteeing safe operation and reducing maintenance cost in modern industry. In this paper, we present a novel deep learning method for RUL estimation based on time empirical mode decomposition (EMD) and temporal convolutional networks (TCN). The proposed framework can effectively reveal the non-stationary characteristics of bearing degradation signals and acquire time-series degradation signals which namely intrinsic mode functions through empirical mode decomposition. Furthermore, the feature information is used as the input to convolution layer and trained by TCN to predict remaining useful life. The proposed EMD-TCN model structure maintains a superior result compared to several state-of-the-art convolutional algorithms on public data sets. Experimental results show that the average score of EMD-TCN model is improved by 10-20% than traditional convolutional algorithms.

Language英语
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25890
Collection数字工厂研究室
Corresponding AuthorYe KJ(叶可江)
Affiliation1.Shengzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shengzhen, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
3.Department of Computer and Information Science, University of Macau, Macau, China
4.University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Yang, Wensi,Yao QF,Ye KJ,et al. Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation[J]. INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING,2020,48(1):61-79.
APA Yang, Wensi,Yao QF,Ye KJ,&Xu CZ.(2020).Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation.INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING,48(1),61-79.
MLA Yang, Wensi,et al."Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation".INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 48.1(2020):61-79.
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