Prediction model of bearing fault remaining useful life based on weighted variable loss degradation characteristics

Abstract

Abstract In the prediction of bearing fault remaining useful life (RUL), the identification and feature extraction of early bearing faults are very important. In order to improve the accuracy of early fault RUL prediction, a bearing fault RUL prediction model based on weighted variable loss degradation characteristics is proposed. The model is composed of a stack denoising autoencoder (SDAE) module guided by variable loss, a signal-to-noise feature adaptive weighting module and a long-short term memory (LSTM) degradation characteristics extraction and regression output module. Firstly, this model improves the ability of SDAE model to extract weak fault features by ascending dimension learning and variable loss function. Then, an adaptive weighting matrix is generated according to the test signal to modulate the weight vector of SDAE. Finally, the hidden layer features of SDAE were input into LSTM model to extract the bearing state degradation features and realize the RUL prediction of bearing faults. The experimental results show that the proposed model can accurately predict the RUL of the test data in the early fault stage and the fault development stage. The proposed model can give early fault warning to the bearing state.