A Novel Bearing Remaining Useful Life Prediction Methodology With Slope-Based Change Point Detection and WOA-Attention-BiLSTM Model

Appropriate health indicator (HI) and efficient prediction models are critical factors for accurate remaining useful life (RUL) prediction, particularly when dealing with fluctuations and redundant information in the HI curve. To address these challenges, this study proposed an HI construction method for better characterization of the degradation behavior based on the entropy weight method and kernel entropy component analysis (EWM-KECA). The HI construction method can eliminate the fluctuations in HI and identify the fault change point position in HI. For RUL estimation, a bearing RUL prediction method was developed by integrating slope-based change point detection with a whale optimization algorithm (WOA)-Attention-bidirectional long short-term memory (BiLSTM) model. By eliminating more than 85% of duplicate data that are not useful for RUL prediction, this approach achieves more accurate RUL predictions while reducing computational resource requirements. The reliability and effectiveness of the proposed method are validated using the bearing degradation dataset. The results from comparative analysis and ablation experiments demonstrate that the proposed method consistently achieves superior performance. Compared with models such as CNN-Attention-BiGRU, WOA-CNN-BiGRU, and WOA-Attention-CNN, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root-mean-squared error (RMSE) values have been reduced by more than 50%, indicating that the proposed RUL prediction methodology represents an advanced and effective approach.