Adaptive staged RUL prediction of rolling bearing

Remaining useful life (RUL) prediction is an important prognostic task in mechanical maintenance. However, a single health indicator cannot provide complete degradation information and clearly divide the degradation process, which reduces the accuracy of predicting the RUL of key components. Therefore, an adaptive staged RUL prediction method based on feature fusion is proposed. Firstly, a proportion-weighted method is proposed, which integrates various degradation information of rolling bearings to construct an evaluative health indicator (EHI). Secondly, aiming at the problem of clustering boundary ambiguity in different degradation stages, a two-step outlier detection strategy is proposed to solve the boundary point state interweaving problem between stages. Then, a staged RUL prediction model is proposed to match the model of the corresponding stage adaptively and dynamically to accurately predict RUL. Finally, two different sets of experiments are carried out to verify the accuracy and superiority of the proposed method in the RUL prediction.