{"title":"基于振动的反向传播神经网络感应电机故障识别","authors":"Kuspijani Kuspijani, Richa Watiasih, Prihastono Prihastono","doi":"10.1109/ICoSTA48221.2020.1570615779","DOIUrl":null,"url":null,"abstract":"Vibration analysis is very important in predictive maintenance in the industry today. In this paper, has designed a vibration detection system that can identify the condition of the induction motor by online. Induction motor vibration data is read from two vibration sensors, accelerometer1 and accelerometer2, displayed using ATMEGA16 microcontroller and can directly identify the condition of the induction motor by using artificial intelligence-based analysis that is Backpropagation-Neural Network (B-NN). After testing, the system managed to perform fault identification induction motor with the rank of success of 95 percent.","PeriodicalId":375166,"journal":{"name":"2020 International Conference on Smart Technology and Applications (ICoSTA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Faults Identification of Induction Motor Based On Vibration Using Backpropagation Neural Network\",\"authors\":\"Kuspijani Kuspijani, Richa Watiasih, Prihastono Prihastono\",\"doi\":\"10.1109/ICoSTA48221.2020.1570615779\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Vibration analysis is very important in predictive maintenance in the industry today. In this paper, has designed a vibration detection system that can identify the condition of the induction motor by online. Induction motor vibration data is read from two vibration sensors, accelerometer1 and accelerometer2, displayed using ATMEGA16 microcontroller and can directly identify the condition of the induction motor by using artificial intelligence-based analysis that is Backpropagation-Neural Network (B-NN). After testing, the system managed to perform fault identification induction motor with the rank of success of 95 percent.\",\"PeriodicalId\":375166,\"journal\":{\"name\":\"2020 International Conference on Smart Technology and Applications (ICoSTA)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Smart Technology and Applications (ICoSTA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICoSTA48221.2020.1570615779\",\"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/ICoSTA48221.2020.1570615779","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Faults Identification of Induction Motor Based On Vibration Using Backpropagation Neural Network
Vibration analysis is very important in predictive maintenance in the industry today. In this paper, has designed a vibration detection system that can identify the condition of the induction motor by online. Induction motor vibration data is read from two vibration sensors, accelerometer1 and accelerometer2, displayed using ATMEGA16 microcontroller and can directly identify the condition of the induction motor by using artificial intelligence-based analysis that is Backpropagation-Neural Network (B-NN). After testing, the system managed to perform fault identification induction motor with the rank of success of 95 percent.