Remaining useful life prediction of rolling element bearing based on hybrid drive of data-driven and dynamic model

The dynamic model can provide high capabilities at accurate simulation for rolling element bearings (REBs), but to simulate the continuous dynamic response over the whole life time is difficult. This paper proposes a hybrid method based on a data-driven and dynamic model to obtain continuous parameters of dynamic model, which can be used to predict the remaining useful life (RUL) of REBs. First, four dynamic models of the REB with the variable parameters of roughness, crack growth rate, crack length and crack depth are established to describe the degradation process of REB with three stages. In particular, the self-healing phenomenon in bearing fault stage is analysed, and the three models are used to describe the trend of fluctuating degradation process in this stage. The dynamic model parameters are updated for the degradation process of REBs with the interacting multiple-model particle filtering, which taken root mean square (RMS) as the observation. Then, the continuously growing crack length is used as the index to predict the RUL. The proposed method realises the continuity of dynamic model in the whole life time, and the fluctuating RMS is transformed into a continuously growing parameter for prediction to avoid the prediction difficulty caused by the decrease in RMS in the degradation process. Finally, The bearing datasets of Xi’an Jiaotong University are used to verify the effectiveness of the proposed method. The comparative test results show that the proposed method can predict the results more accurately without sample training and obtain a clearer degradation mechanism.