文献类型：会议论文

英文题名：Motor Fault Detection and Diagnosis Based on Negative Selection Algorithm

作者：Zhou, Lihua[1];Dai, Zhongjian[1];Dai, Yaping[1];Zhao, Linhui[2]

第一作者：Zhou, Lihua

通讯作者：Zhao, LH[1]

机构：[1]Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China;[2]Beijing Union Univ, Sch Mechatron, Beijing, Peoples R China

第一机构：Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Sch Mechatron, Beijing, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

会议论文集：5th International Conference on Information Engineering for Mechanics and Materials (ICIMM)

会议日期：JUL 25-26, 2015

会议地点：Huhhot, PEOPLES R CHINA

语种：英文

外文关键词：artificial immune system; negative selection algorithm; fault detection; fault diagnosis

摘要：This paper presents a motor fault diagnosis method based on negative selection algorithm. It has the structure of two-level detectors, the first level detector detecting the presence of faults and the second level detector detecting the type of faults. Therefore the first level detectors are trained by using motor normal signals, and the second level detectors are trained by using several types of fault signals. During the process of detecting, only the test results of first level detectors are abnormal, the second level detectors are activated and implement fault detection to identify fault type. In this paper, normal vibration signals of motor bearing and three types of fault signals from American Case Western Reserve University bearing fault database are used to verify the fault diagnosis method. The experimental results show that the method can effectively detect early failure and can correctly identify the fault type.