The Research of SAE and BiLSTM-SA for Bearing Residual Life Prediction

In the prediction of the remaining life of bearings, the traditional method has the problem of information redundancy in the multi-domain features of bearing vibration signals and dimensionality reduction of high-dimensional features. To address this issue, this paper proposes a prediction method based on feature fusion using a Sparse Autoencoder (SAE), combined with a Bidirectional Long Short-Term Memory network (BiLSTM) and a Self-Attention mechanism (SA). First, multi-domain feature extraction is performed on the original signal to construct an initial feature set containing time, frequency and time-frequency domain metrics; then, a comprehensive evaluation system is established based on the three dimensions of feature relevance, robustness and monotonicity, and sensitive degraded features with scores higher than the preset thresholds are screened as the input to the model, and the features extracted from time, frequency and time-frequency domains are fused and downgraded by SAE to obtain the low-dimensional fusion features. Finally, the fused features are input into the BiLSTM-SA module to further extract the timing information and assign feature weights, and validated on the PHM2012 dataset. The results show that the proposed method exhibits better performance in several evaluation indexes compared with the three models with BiLSTM-SA, SAE-BiLSTM and SAE-BiGRU, proving its effectiveness in predicting the remaining life of bearings.