{"title":"基于在线机器学习的机器故障检测新方法","authors":"Jiang Yi, Lei Lin","doi":"10.1109/ICCCAS.2018.8768985","DOIUrl":null,"url":null,"abstract":"The article first analyzes many existing machine running fault diagnosis methods, which may not be good for on-line detection in view of the complexity of the algorithm. At the same time, studies the characteristics of random noise modulation. Then, designs a new on-line self-learning evolutionary fault detection method based on the FFT algorithm. The simulation shows that the method has good detection performance for a weak fault signal.","PeriodicalId":166878,"journal":{"name":"2018 10th International Conference on Communications, Circuits and Systems (ICCCAS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A New Method of Machine Fault Detection Based on Machine Learning on Line\",\"authors\":\"Jiang Yi, Lei Lin\",\"doi\":\"10.1109/ICCCAS.2018.8768985\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article first analyzes many existing machine running fault diagnosis methods, which may not be good for on-line detection in view of the complexity of the algorithm. At the same time, studies the characteristics of random noise modulation. Then, designs a new on-line self-learning evolutionary fault detection method based on the FFT algorithm. The simulation shows that the method has good detection performance for a weak fault signal.\",\"PeriodicalId\":166878,\"journal\":{\"name\":\"2018 10th International Conference on Communications, Circuits and Systems (ICCCAS)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 10th International Conference on Communications, Circuits and Systems (ICCCAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCAS.2018.8768985\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 10th International Conference on Communications, Circuits and Systems (ICCCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCAS.2018.8768985","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Method of Machine Fault Detection Based on Machine Learning on Line
The article first analyzes many existing machine running fault diagnosis methods, which may not be good for on-line detection in view of the complexity of the algorithm. At the same time, studies the characteristics of random noise modulation. Then, designs a new on-line self-learning evolutionary fault detection method based on the FFT algorithm. The simulation shows that the method has good detection performance for a weak fault signal.
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