Deep transfer learning based on Bi-LSTM and attention for remaining useful life prediction of rolling bearing

Abstract

Many transfer learning methods focus on training models between domains with large differences. However, the data feature distribution varies greatly in different bearing degradation processes, which affects the prediction accuracy of Remaining useful life (RUL). To solve this problem, a novel method for RUL prediction with more refined transfer is proposed, which added failure behavior judgment. Firstly, a failure behavior judgment method is proposed by using the convolutional autoencoder (CAE) and Pearson correlation coefficient to determine whether the bearing fails gradually or suddenly. Then, a multi-channel transfer network is proposed for extracting multi-scale features of bearing degradation. Each channel uses convolutional network and bidirectional long short-term memory (Bi-LSTM) to extract global and temporal information. To reduce the difference in feature distribution between the training and test bearing data, a domain adaptive structure is added after feature fusion in each channel to enable the model to learn domain invariant features. By applying this method to experimental data and comparing it with other methods, the superiority and effectiveness of the proposed method are verified.