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Real-time residual life prediction based on semi-stochastic filter and expectation maximization algorithm

FENG Lei;WANG Hongli;SI Xiaosheng;YANG Xiaojun;WANG Biaobiao;The Second Artillery Engineering College;Unit 96275;  
The prediction of residual life(RL)is the key of the predictive maintenance for engineering equipment.Accurate and real-time prediction can provide more effective decision support to the subsequent maintenance schedule and avoid the failure effectively.In engineering practice,the performance index reflecting the degradation process of the equipment is generally not observed directly.To tackle the residual life problem under hidden degradation,aprediction method based on semi-stochastic and expectation maximization(EM)algorithm is proposed in this paper.First,the residual life is taken as the hidden state and the prediction model is constructed by building the stochastic relationship between the residual life and monitoring data.Secondly,based on the monitoring data up to the current time,a collaborative method by the extended Kalman filter(EKF)and expectation maximization algorithm is presented to achieve a real-time estimation and updating of the residual life distribution and unknown model parameters.Finally,the proposed method is validated by the application to the inertial measurement unit(IMU)and the results indicate that the method can improve the accuracy and reduce the uncertainty of the estimated residual life.
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