Abstract:In order to improve the prediction accuracy of the bearing remaining useful life,a prediction method of bearing remaining useful life based on improved PSO-SVR model was proposed.The degradation characteristics were constructed by selecting the root mean square,crest factor,kurtosis factor of bearing horizontal and vertical vibration signals,and the prediction model of bearing remaining useful life based on SVR was established.In order to optimize the parameters of SVR,a dynamic adaptive asynchronous particle swarm optimization algorithm was designed,Gworst was introduced to correct the velocity and position formula,an adaptive inertial weight coefficient based on the inverted S-type function was proposed,and an adaptive asynchronous learning factor based on the inertial weight coefficient was presented,which could effectively overcome the local optimization capability and accelerate the convergence efficiency,enhance the regression accuracy.The simulation results show that the proposed method has more prediction efficiency and accuracy than GS-SVR,GA-SVR,PSO-SVR,MPSO-SVR,it is superior to the classical regression methods,such as GBDT,RF,DT and GP.