BLDC电机温度退化多段Wiener过程建模

Brushless DC(BLDC) motor is widely used and its temperature degradation process is multistage. It is necessary to establish a multistage degradation model. When the model has several parameters, the parameter estimation process is sensitive to the initial value and easy to end up with a local optimization. This study was based on accelerated degradation data of motors. The normal weighted average filter (Gauss filter) was used to improve the results of estimation for the model parameters. A multistage Wiener model was established by using the transition function to modify linear model. Then, to maximize likelihood function for parameter estimation, the numerical optimization method, improved particle swarm optimization (PSO), was used for cycle calculation. The rationality of multistage model is verified by comparison of the normality of residual with widely used nonlinear Wiener model, and by analysis of theoretical life distribution of models and actual failure distribution of this batch. The modeling results show that the degradation mechanism changes at high speed during the degradation of the motor. Finally, prediction for motor life under this stress was gained by life distribution in different moments of time calculated by nonlinear model, which is important for the prognostics and health management (PHM) of motors.