Hong-Hsiang Chang, C. Kuo, Yu-Min Hsueh, Yiche Wang, Cheng-Fu Hsieh
{"title":"基于模糊的智能电网结构感应电机故障诊断系统","authors":"Hong-Hsiang Chang, C. Kuo, Yu-Min Hsueh, Yiche Wang, Cheng-Fu Hsieh","doi":"10.1109/SEGE.2017.8052784","DOIUrl":null,"url":null,"abstract":"This study aims to develop a fuzzy algorithm-based induction motor fault diagnosis system. First, the electrical and vibration signals of motor are measured by using electrical analysis and vibration analysis methods. Secondly, the electrical indexes of international specifications are calculated and the vibrating shaft trajectory pattern is recorded, the feature values are extracted by index fitness evaluation and fractal theory, so as to design the fuzzy-based fault diagnosis system, to evaluate the probability of various types of motor faults. Finally, the feasibility is evaluated by using health and common motor (stator, rotor, bearing and eccentric) fault defect models.","PeriodicalId":404327,"journal":{"name":"2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Fuzzy-based fault diagnosis system for induction motors on smart grid structures\",\"authors\":\"Hong-Hsiang Chang, C. Kuo, Yu-Min Hsueh, Yiche Wang, Cheng-Fu Hsieh\",\"doi\":\"10.1109/SEGE.2017.8052784\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study aims to develop a fuzzy algorithm-based induction motor fault diagnosis system. First, the electrical and vibration signals of motor are measured by using electrical analysis and vibration analysis methods. Secondly, the electrical indexes of international specifications are calculated and the vibrating shaft trajectory pattern is recorded, the feature values are extracted by index fitness evaluation and fractal theory, so as to design the fuzzy-based fault diagnosis system, to evaluate the probability of various types of motor faults. Finally, the feasibility is evaluated by using health and common motor (stator, rotor, bearing and eccentric) fault defect models.\",\"PeriodicalId\":404327,\"journal\":{\"name\":\"2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SEGE.2017.8052784\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SEGE.2017.8052784","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy-based fault diagnosis system for induction motors on smart grid structures
This study aims to develop a fuzzy algorithm-based induction motor fault diagnosis system. First, the electrical and vibration signals of motor are measured by using electrical analysis and vibration analysis methods. Secondly, the electrical indexes of international specifications are calculated and the vibrating shaft trajectory pattern is recorded, the feature values are extracted by index fitness evaluation and fractal theory, so as to design the fuzzy-based fault diagnosis system, to evaluate the probability of various types of motor faults. Finally, the feasibility is evaluated by using health and common motor (stator, rotor, bearing and eccentric) fault defect models.