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International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
Fax:
86-571-87239571
Add:
No.9 Gaoguannong,Daxue Road,Hangzhou,China
P.C:
310009
E-mail:
meem_contribute@163.com
Abstract: To solve the problems of the difficulty in determining the remaining useful life (RUL) of bearings and the low accuracy of a single life prediction model to predict degradation start time (DST), a RUL prediction method based on state division and ensemble learning models were proposed. First, the DST was determined adaptively by extracting the bearing vibration signal characteristics, constantly updating the 3σ criterion warning range using a sliding window and combining a continuous triggering mechanism. Then, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to adaptively decompose the signal sequence in the degradation phase. Finally, an ensemble learning model was built to perform multi-stage rolling prediction considering different component characteristics, the prediction results were merged to obtain the bearing RUL, and the public bearing dataset XJTU-SY was used for experimental verification. The research results show that the mean absolute error of the prediction results of the proposed method is respectively reduced by 11.7% and 5.6%, and the relative mean square error is respectively decreased by 12.2% and 10.7%, comparing with the prediction methods based on long short-term memory neural network (LSTM) and back-propagation neural network (BPNN). The validity and superiority of the proposed method in the application of bearing RUL prediction is verified.
Key words: rolling bearing remaining useful life (RUL); degradation start time (DST); adaptive DST state division; ensemble learning model; degenerate feature extraction; complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); long short-term memory neural network (LSTM)
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- Chinese Core Periodicals
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- Chinese Sci-tech Core Periodicals
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- SA, INSPEC Indexed
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- CSA: T Indexed
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- UPD:Indexed
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2010 Zhejiang Information Institute of Mechinery Industry
Technical Support：Hangzhou Bory science and technology
