A New Method of Bearing Remaining Useful Life Based on Life Evolution and SE-ConvLSTM Neural Network

The degradation process of bearing performance in the whole life cycle is a complex and nonlinear process. However, the traditional neural network-based approaches usually consider the degradation process of bearing performance as linear, which does not accord with the actual situation of bearing degradation. To overcome this shortcoming, a rolling bearing's remaining useful life prediction method based on a Squeeze-and-Excitation-Convolutional long short-term memory (SE-ConvLSTM) neural network was proposed based on the construction of a new health index in the process of bearing life evolution. The proposed method considered the change rule of the health indicator during the whole life cycle evolution of bearings, then constructed the health indicator by using the SE-ConvLSTM neural network, effectively improving the model prediction accuracy and training efficiency. Firstly, the original data are filtered and denoised by Ensemble Empirical Mode Decomposition. Combined with Principal Component Analysis (PCA) dimensionality reduction and the Local Outlier Factor (LOF) algorithm, the bearing's life evolution interval is divided. Then, the health indicator is constructed based on the proposed SE-ConvLSTM model, and the remaining useful life of rolling bearings is predicted by a particle filter and double exponential model. The proposed method is compared with other related methods with the PHM2012 dataset, and the results show that the proposed method has higher accuracy in remaining useful life predictions. Compared with the traditional method, the health index construction based on the division of the lifespan evolution interval has higher practical significance.