First A. Pan Mingzhi, Pan Hong-xia, Second B. Xu Xin, Liu Huiling
{"title":"Research on Fault Diagnosis of Rotational Automaton Based on VMD-ELM","authors":"First A. Pan Mingzhi, Pan Hong-xia, Second B. Xu Xin, Liu Huiling","doi":"10.1109/URAI.2018.8441863","DOIUrl":null,"url":null,"abstract":"Due to complex operating environment of automat, superposition of various response signals, in order to accurately, efficiently extract fault characteristics of automat signal, a automat fault analysis method using VMD and ELM was proposed. First automat signal was analyzed using VMD and compared with the result of EMD; meanwhile energy percentage of every modal component and sample entropy of different samples under various operating condition were extracted as characteristic values; extracted characteristic values were input into ELM for fault diagnosis and compared with the diagnostic result of traditional double-spectrum analysis. Finally, VMD method achieved adaptive subdivision of every component in frequency domain of signal and concluded that accuracy rate of ELM fault diagnosis is 87.5%. result of the experiment showed that VMD can effectively avoid mode aliasing and test feasibility and effectiveness of proposed method.","PeriodicalId":347727,"journal":{"name":"2018 15th International Conference on Ubiquitous Robots (UR)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 15th International Conference on Ubiquitous Robots (UR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/URAI.2018.8441863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Due to complex operating environment of automat, superposition of various response signals, in order to accurately, efficiently extract fault characteristics of automat signal, a automat fault analysis method using VMD and ELM was proposed. First automat signal was analyzed using VMD and compared with the result of EMD; meanwhile energy percentage of every modal component and sample entropy of different samples under various operating condition were extracted as characteristic values; extracted characteristic values were input into ELM for fault diagnosis and compared with the diagnostic result of traditional double-spectrum analysis. Finally, VMD method achieved adaptive subdivision of every component in frequency domain of signal and concluded that accuracy rate of ELM fault diagnosis is 87.5%. result of the experiment showed that VMD can effectively avoid mode aliasing and test feasibility and effectiveness of proposed method.