A Method for Predicting the Remaining Life of Rolling Bearings Based on Multi-Scale Feature Extraction and Attention Mechanism

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...

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Bibliographic Details
Published in:Electronics (Basel) 2022-11, Vol.11 (21), p.3616
Main Authors: Jiang, Changhong, Liu, Xinyu, Liu, Yizheng, Xie, Mujun, Liang, Chao, Wang, Qiming
Format: Article
Language:English
Subjects:
Artificial neural networks
Bearings
Deep learning
Degradation
Fault diagnosis
Fault location (Engineering)
Feature extraction
Life prediction
Methods
Neural networks
Performance prediction
Quadratic equations
Reliability (Engineering)
Roller bearings
Time series
Vibration
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Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11213616