rolling bearing;
residual life prediction;
multi-scale feature extraction;
attention mechanism;
CONVOLUTIONAL NEURAL-NETWORK;
RECOGNITION;
D O I:
10.3390/electronics11213616
中图分类号:
TP [自动化技术、计算机技术];
学科分类号:
0812 ;
摘要:
In response to the problems of difficult identification of degradation stage start points and inadequate extraction of degradation features in the current rolling bearing remaining life prediction method, a rolling bearing remaining life prediction method based on multi-scale feature extraction and attention mechanism is proposed. Firstly, this paper takes the normalized bearing vibration signal as input and adopts a quadratic function as the RUL prediction label, avoiding identifying the degradation stage start point. Secondly, the spatial and temporal features of the bearing vibration signal are extracted using the dilated convolutional neural network and LSTM network, respectively, and the channel attention mechanism is used to assign weights to each degradation feature to effectively use multi-scale information. Finally, the mapping of bearing degradation features to remaining life labels is achieved through a fully connected layer for the RUL prediction of bearings. The proposed method is validated using the PHM 2012 Challenge bearing dataset, and the experimental results show that the predictive performance of the proposed method is superior to that of other RUL prediction methods.
机构:
Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou, Peoples R ChinaZhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou, Peoples R China
Wang, Yan
Liang, Jie
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机构:
Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou, Peoples R ChinaZhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou, Peoples R China
Liang, Jie
Gu, Xiaoguang
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机构:
Henan Big Data Ctr, Dept Appl Res, 39 Jinshui East Rd, Zhengzhou 45003, Peoples R ChinaZhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou, Peoples R China
Gu, Xiaoguang
Ling, Dan
论文数: 0引用数: 0
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机构:
Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou, Peoples R ChinaZhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou, Peoples R China
Ling, Dan
Yu, Haowen
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机构:
Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou, Peoples R ChinaZhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou, Peoples R China
机构:
Univ Shanghai Sci & Technol, Sch Energy & Power Engn, Shanghai 200093, Peoples R China
Liverpool John Moores Univ, Dept Maritime & Mech Engn, Byrom St, Liverpool L3 3AF, Merseyside, EnglandUniv Shanghai Sci & Technol, Sch Energy & Power Engn, Shanghai 200093, Peoples R China
Xu, Zifei
Li, Chun
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机构:
Univ Shanghai Sci & Technol, Sch Energy & Power Engn, Shanghai 200093, Peoples R ChinaUniv Shanghai Sci & Technol, Sch Energy & Power Engn, Shanghai 200093, Peoples R China
Li, Chun
Yang, Yang
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机构:
Univ Shanghai Sci & Technol, Sch Energy & Power Engn, Shanghai 200093, Peoples R China
Liverpool John Moores Univ, Dept Maritime & Mech Engn, Byrom St, Liverpool L3 3AF, Merseyside, EnglandUniv Shanghai Sci & Technol, Sch Energy & Power Engn, Shanghai 200093, Peoples R China