RUL prediction integrating feature distribution change point identification and martingale

Abstract The two-stage Wiener process (WP) model has become a common method to describe the phased deterioration of bearings over time. However, this model ignores the correlation of feature data distribution structure and change points (CPs) between the two stages, as well as the limitations of maximum-likelihood estimation methods for WP model parameter estimation. Therefore, this paper proposes a remaining useful life prediction approach that integrates feature distribution CP identification and a martingale process. First, a two-step feature screening method adopting trend consistency and composite score is proposed to construct a health indicator, which accounts for the trend consistency of the same feature on different bearings and can avoid redundancy while containing sufficient degradation information. Next, a t -neighborhood granular mean-shift clustering method is proposed, which makes the divisibility of the feature distribution more obvious and can identify CPs sensitively, flexibly and stably. Finally, a martingale method is introduced so that the parameter estimation of the two-stage WP model depends on the entire degradation path, which overcomes the limitations of WP model parameter estimation and enables the model to better characterize the bearing degradation process.