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Remaining useful life estimation by empirical mode decomposition and ensemble deep convolution neural networks
Yao QF(么庆丰)1,2,3,4; Yang TJ(杨天吉)1,2,3,4; Liu Z(刘智)1,2,3,4; Zheng ZY(郑泽宇)1,2,3,4
Department数字工厂研究室
Conference Name2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019
Conference DateJune 17-20, 2019
Conference PlaceSan Francisco, CA, United states
Source Publication2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019
PublisherIEEE
Publication PlaceNew York
2019
Pages1-6
Indexed ByEI
EI Accession number20194007491368
Contribution Rank1
ISBN978-1-5386-8357-6
KeywordNeural Networks Ensemble Learning Empirical Mode Decomposition Remaining Useful Life
AbstractBearing remaining useful life (RUL) prediction plays a key role in guaranteeing safe operation and reducing maintenance costs. In this paper, we present a novel deep learning method for RUL estimation approach through time Empirical Mode Decomposition (EMD) and Convolutional Neural Network (CNN). EMD can reveal the nonstationary property of bearing degradation signals effectively. After acquiring time-series degradation signals, namely Intrinsic Mode Functions (IMF), we can utilize the featured information as the input of Convolution layer of models. Here, we introduce an EMD-CNN model structure, which keeps the global and local information synchronously compared to a traditional CNN. In order to get a more accurate prediction, an ensemble model with several weighting methods are proposed, where the experiment indicates an improvement of performance.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/25663
Collection数字工厂研究室
Corresponding AuthorYao QF(么庆丰)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, Liaoning, China
3.University of Chinese Academy of Sciences, Beijing, 100049, China
4.Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, 110016, Liaoning, China
Recommended Citation
GB/T 7714
Yao QF,Yang TJ,Liu Z,et al. Remaining useful life estimation by empirical mode decomposition and ensemble deep convolution neural networks[C]. New York:IEEE,2019:1-6.
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