A Feature Fusion-Based Method for Remaining Useful Life Prediction of Rolling Bearings.

Predicting the remaining useful life (RUL) of rolling bearings is a critical technology for ensuring safe operation and reducing the maintenance costs of rotating mechanical equipment. The accuracy of RUL prediction is highly dependent on the quality of degradation features screened from the original statistical features. However, there is redundancy between degradation features after screening, and the screening rule is highly subjective. In addition, some useful information of the unselected features is lost. To solve the above problems, this article proposes a feature fusion-based method for bearing RUL prediction. First, the self-organizing map (SOM) method is used to cluster the statistical features, which are then subjected to dimensionality reduction by the principal component analysis (PCA) method to obtain a fusion degradation feature set. Second, a bidirectional long short-term memory (BiLSTM) network combined with the self-attention (SA) mechanism (BiLSTM-SA model) is established to predict bearing RUL with the fusion feature set. The SA mechanism is introduced to assign different weights to different fusion features to adaptively obtain the optimal feature combination. Finally, the effectiveness of the proposed feature fusion-based prediction method is verified on the Franche-Comte Electronics Mechanics Thermal Science and Optics (FEMTO) bearing dataset and the Intelligent Maintenance Systems (IMS) bearing dataset. The experimental results show that the proposed method can accurately predict bearing RUL, and its performance is superior to that of some state-of-the-art methods.