Remaining Useful Life Prognosis Method of Rolling Bearings Considering Degradation Distribution Shift

The problem of distribution shift during the degradation of rolling bearings can lead to a decrease in the prediction accuracy of the remaining useful life (RUL). Moreover, the traditional sequential long short-term memory (LSTM) model has limitations in its ability to extract relevant information, which hinders its prediction performance. This article presents a novel strategy called channel dependent and channel independent (CD-CI) to alleviate distribution shift by improving robustness. In the CD strategy, a fusion of the residual LSTM and self-attention (RLSA) module is proposed. Subsequently, residuals connect multiple RLSAs to create the RRLSA stack, enhancing the learning of feature dependencies. Meanwhile, the CI strategy fuses DLinear and self-attention (DLSA) to intensify attention toward the degradation information of a single feature. Ultimately, this article introduces an adaptive channel-time attention shrinkage (CTAS) module to reduce internal noise within the model. The validation and analysis of the CD-CI strategy uses the IEEE PHM 2012 bearing dataset and XJTU-SY bearing dataset. The CD-CI strategy exhibits minimal (non-)robustness variation, indicating a robust performance. The root mean square error (RMSE), mean absolute error (MAE), and score predicted by RUL are improved by 9.9%, 16.4%, and 15.9%, respectively, compared with the advanced model. These experiments indicate that the CD-CI strategy can alleviate distribution shifts and improve prediction accuracy by enhancing robustness.