{"title":"基于离散小波变换和模糊推理系统的异步电动机故障分类","authors":"Kuspijani Kuspijani, Richa Watiasih, Prihastono Prihastono","doi":"10.1109/ICoSTA48221.2020.1570615773","DOIUrl":null,"url":null,"abstract":"This paper develops a real-time condition-monitoring algorithm for induction motor. The condition monitoring mechanism is based on the discrete wavelet transform (DWT) and the fuzzy inference system (FIS). In this method, the stator currents are used as an input to the system. No direct access to the induction motor is required. The developed system has been rigorously assessed theoretically and experimentally, and it has been shown that the system is robust and reliable.","PeriodicalId":375166,"journal":{"name":"2020 International Conference on Smart Technology and Applications (ICoSTA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Fault Classification of Induction Motor Using Discrete Wavelet Transform and Fuzzy Inference System\",\"authors\":\"Kuspijani Kuspijani, Richa Watiasih, Prihastono Prihastono\",\"doi\":\"10.1109/ICoSTA48221.2020.1570615773\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper develops a real-time condition-monitoring algorithm for induction motor. The condition monitoring mechanism is based on the discrete wavelet transform (DWT) and the fuzzy inference system (FIS). In this method, the stator currents are used as an input to the system. No direct access to the induction motor is required. The developed system has been rigorously assessed theoretically and experimentally, and it has been shown that the system is robust and reliable.\",\"PeriodicalId\":375166,\"journal\":{\"name\":\"2020 International Conference on Smart Technology and Applications (ICoSTA)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Smart Technology and Applications (ICoSTA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICoSTA48221.2020.1570615773\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Smart Technology and Applications (ICoSTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICoSTA48221.2020.1570615773","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fault Classification of Induction Motor Using Discrete Wavelet Transform and Fuzzy Inference System
This paper develops a real-time condition-monitoring algorithm for induction motor. The condition monitoring mechanism is based on the discrete wavelet transform (DWT) and the fuzzy inference system (FIS). In this method, the stator currents are used as an input to the system. No direct access to the induction motor is required. The developed system has been rigorously assessed theoretically and experimentally, and it has been shown that the system is robust and reliable.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
