Prediction of the Remaining Life of Rolling Bearings based on the Classification of Degradation States

view of the fact that the healthy running time of rolling bearings in actual working conditions lasts for a long time, and the vibration signal fluctuation in the healthy operation stage is relatively stable, and it is difficult to extract useful degradation information from them, a remaining life prediction model based on the classification of degradation states based on the hidden Markov model is proposed. Firstly, the characteristics of the bearing vibration signal are extracted and the dimension reduction is carried out, and then the RMS is used as the observation sequence to divide the degradation state. Finally, the LightGBM optimized by the whale algorithm is used to predict the remaining life of the degraded stage after division. The experimental data set of XJTU-XY bearing is used for verification, and the results show that the prediction performance of LightGBM after whale algorithm optimization is significantly improved.