Bearing Remaining Useful Life Prediction Based on AE-BiLSTM

The remaining useful life (RUL) prediction of rolling bearings can avoid unreasonable maintenance and major safety accidents. Considering the non-stationary characteristics, it is difficult to utilize the deep learning-based method to directly extract degradation features from the bearing vibration...

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Bibliographic Details
Main Authors: Liu, Jie, Yang, Zian, Wang, Ruijie, Liu, Shanhui
Format: Conference Proceeding
Language:English
Subjects:
AutoEncoder
Bi-LSTM
Degradation
Frequency-domain analysis
Noise reduction
Predictive models
remaining useful life
rolling bearing
Rolling bearings
Vibration measurement
Vibrations
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Summary:The remaining useful life (RUL) prediction of rolling bearings can avoid unreasonable maintenance and major safety accidents. Considering the non-stationary characteristics, it is difficult to utilize the deep learning-based method to directly extract degradation features from the bearing vibration signal. Therefore, in this paper, a fusion prediction model AE-BiLSTM is proposed. The AutoEncoder (AE) is used to extract degradation features from the frequency-domain signals, and BiLSTM network is used to predict the bearing RUL. The experimental verification is conducted on the FEMTO-ST bearing dataset. Experimental results illustrate that the proposed AE-BiLSTM network can accurately predict the RUL of roll bearings.
ISSN:2693-3128
DOI:10.1109/ITNEC56291.2023.10082350