Remaining useful life prediction of ultrasonic motor based on Elman neural network with improved particle swarm optimization

Abstract

In this paper, a data-driven prediction method combining condition monitoring data and Elman neural network is proposed, this method obtains the remaining useful life of ultrasonic motor by predicting the tendency of motor performance degradation index. Firstly, the improved particle optimization algorithm is employed to enhance the prediction accuracy of Elman neural network. Principal component analysis is used to extract the motor degradation index from condition monitoring data. Then Elman neural network prediction model is established to predict the variation trend of the degradation index, and the motor failure threshold λ is applied to evaluate the value of motor remaining useful life. Finally, the proposed model is used to perform the prediction test on three PMR60 ultrasonic motors and compare with three benchmark models. Experimental results indicate that the proposed method is a new effective method for estimating the remaining useful life of ultrasonic motor.