Remaining Useful Life Prediction Method for Bearings Based on Pruned Exact Linear Time State Segmentation and Time–Frequency Diagram

Abstract

To improve the accuracy and robustness of bearing remaining useful life (RUL) prediction, this paper proposes a bearing RUL prediction method based on PELT state segmentation and time–frequency analysis, incorporating the Informer model for time-series modeling. First, the PELT (Pruned Exact Linear Time) algorithm is used to segment the vibration signals over the full life cycle of the bearing, accurately identifying critical degradation states and optimizing the stage division of the degradation process. Next, wavelet transform is applied to perform time–frequency analysis on the vibration signals, generating time–frequency spectrograms to comprehensively extract features in both the time and frequency domains. Finally, the extracted time–frequency features are used as input to predict the bearing RUL using the Informer model. As an efficient time-series prediction model, the Informer excels at handling long time series by leveraging a sparse self-attention mechanism to effectively capture the long-term dependencies in the signals. Experiments conducted on a publicly available dataset and comparisons with traditional methods demonstrate that the proposed method offers significant advantages in terms of prediction accuracy, computational efficiency, and robustness, making it more suitable for bearing health assessment and RUL prediction under complex working conditions.