A dual-model adaptive Kalman filtering for remaining useful life prediction method based on feature fusion and online TSP recognition

Abstract

Remaining Useful Life (RUL) prediction is of significant importance for the safe operation of machines. However, the Health Indicators (HIs) constructed by a single feature of machines cannot precisely describe the non-stationary and accelerating degradation operation. Moreover, poor identification of Time to Start Prediction (TSP), and the limited adaptability of models restrict the accuracy of RUL prediction. A Dual-Model Adaptive Kalman Filter (DMAKF) RUL prediction method based on feature fusion and online TSP recognition is proposed. Firstly, a feature-fusion strategy is proposed, integrating various information to construct distinguishing HIs. Secondly, a statistic-based change-point detection approach is investigated to online recognize TSP, balancing the sensitivity and interference immunity. Finally, the DMAKF is conducted for adaptive adjustment of RUL prediction in real-time. The proposed method is validated on two sets of bearing datasets from different sources. Comparison with four other methods demonstrates the effectiveness of the proposed method.