点击次数：

影响因子：5.6

DOI码：10.1016/j.measurement.2019.05.013

所属单位：南京航空航天大学

教研室：航空学院-精密驱动与控制研究所

发表刊物：MEASUREMENT

关键字：Ultrasonic motor Performance degradation Elman neural network Remaining useful life prediction Particle optimization algorithm

摘要：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 lambda 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. (C) 2019 Elsevier Ltd. All rights reserved.

论文类型：期刊论文

学科门类：工学

文献类型：J

卷号：143

是否译文：否

发表时间：2019-09-01

收录刊物：EI、SCIE

合写作者：Wang, Feng,Zhang, Jiaojiao,Ren, Weihao

通讯作者：Yang, Lin