An Online Adaptive Multidegradation Model for Accurate Remaining Useful Life Prediction

The majority of existing online remaining useful life (RUL) prediction models for rolling bearings adopt a single degradation model, and lack the capacity for real-time assessment of model matching. Furthermore, these models determine the first prediction time (FPT) using subjective thresholds, which often results in inaccuracies and considerable deviations in subsequent online RUL predictions. To overcome these limitations, this article proposes an online adaptive matching multidegradation model for RUL prediction. First, a curvature analysis incorporating a dynamic sliding window strategy is proposed. This strategy determines the first prediction time in real time by analyzing the change in curvature of the root-mean-square values within a dynamic sliding window. Second, an online parallel prediction algorithm with multiple degradation models is developed. This algorithm selects the most suitable prediction model by dynamically evaluating the degree of matching between different degradation models and the actual data, thereby ensuring the timeliness of the prediction model. Finally, the validation of the proposed approach is conducted by accelerating the degradation rolling bearing test and the IMS rolling bearing dataset. The results demonstrate that the proposed method outperforms existing approaches in accurately identifying FPT and predicting online RUL.