| Citation: | LI Junxing, ZHU Wenjin, QIU Ming, et al. Remaining life prediction of rolling bearings based on an EM-IKF collaborative algorithm[J]. Journal of Aerospace Power, 2025, 40（2）：20230251 doi: 10.13224/j.cnki.jasp.20230251
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In view of the characteristics reflecting the two-stage performance degradation process of rolling bearings, a remaining life prediction method of rolling bearings was proposed based on an EM-IKF algorithm. In the stationary stage, to determine the initial degradation point of the bearing, the Schwarz information criterion (SIC) was used to identify the change point of bearing health state. In the degradation stage, a performance degradation characterization model was established based on Wiener process. To overcome the problem that the traditional Kalman filtering method ignored the parameter’s volatility between the adjacent times, the state space equation was established based on an incremental Kalman filter (IKF) algorithm. Meanwhile, to fully develop and utilize the historical data and the online monitoring data, and accurately determine the initial parameters of the state-space equation, a parameter adaptive updating method was proposed based on an EM-IKF collaborative algorithm. Then, the adaptive online prediction of bearing remaining life was realized. Finally, the effectiveness of the proposed method was verified and analyzed by an engineering example involving the rolling bearings. The results showed that compared with the traditional method, the prediction accuracy of the proposed method can be improved by at least 24.64%.
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