Fault diagnosis method for inwheel motor based on wolf pack algorithm
1. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; 2. School of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
Abstract：In view of the complex and changeable driving conditions of electric vehicles and the special operating environment of inwheel motors, it is very important to develop an effective fault diagnosis method for inwheel motors. Because the information transmitted by single feature parameter often had limitations, a multifeature parameter fusion diagnosis method was proposed based on the wolf pack algorithm. Based on the overlap degree of Weibull distribution of common fault diagnosis feature parameters in different operation states of inwheel motors, several feature parameters were chosen with high sensitivity, and several feature parameters were fused through wolf pack algorithm to diagnose faults and fault degree according to Weibull distribution of fusion information of multifeature parameters. The method was verified by building a leakage fault test system of inwheel motor. The experimental data show that the method of multifeature parameter fusion diagnosis based on wolf pack algorithm can effectively identify and distinguish the electrical faults of inwheel motor.
薛红涛, 周宇, 王满, 李仲兴. 基于狼群算法的轮毂电机故障诊断方法[J]. 江苏大学学报（自然科学版）, 2019, 40(5): 579-584.
XUE Hong-Tao, ZHOU Yu, WANG Man, LI Zhong-Xing. Fault diagnosis method for inwheel motor based on wolf pack algorithm[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2019, 40(5): 579-584.
2019, Vol. 40 
