Remaining Useful Life Prognosis Method of Rolling Bearings Considering Degradation Distribution Shift
Bo Tang, Dechen Yao, Jianwei Yang, F.W. Zhang
Abstract
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.