Ma Yabin, Chen Chen, Shu Qiqi, Wang Jian, Li Hongliang, Huang Darong
{"title":"基于HHT包络和小波变换的EMD滚动轴承故障诊断","authors":"Ma Yabin, Chen Chen, Shu Qiqi, Wang Jian, Li Hongliang, Huang Darong","doi":"10.1109/DDCLS.2018.8516038","DOIUrl":null,"url":null,"abstract":"A novel method based on Hilbert Transform (HT) and Empirical Mode Decomposition (EMD) algorithm is proposed in this paper, which separates time series into intrinsic mode functions (IMFs) with different time scales and applies the Hilbert transformation for every IMF to obtain the Hilbert spectrum. Firstly, relevant theories of the proposed method are introduced. Then, based on these theoretical introductions, the fault vibration signals of rolling bearing are dealt with related algorithm. The research results demonstrate that the characteristic frequency of bearing fault can be obtained by proposed method, which is more effective compared with existing algorithm.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"26 1","pages":"481-485"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Fault Diagnosis of Rolling Bearing based on EMD Combined with HHT Envelope and Wavelet Spectrum Transform\",\"authors\":\"Ma Yabin, Chen Chen, Shu Qiqi, Wang Jian, Li Hongliang, Huang Darong\",\"doi\":\"10.1109/DDCLS.2018.8516038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A novel method based on Hilbert Transform (HT) and Empirical Mode Decomposition (EMD) algorithm is proposed in this paper, which separates time series into intrinsic mode functions (IMFs) with different time scales and applies the Hilbert transformation for every IMF to obtain the Hilbert spectrum. Firstly, relevant theories of the proposed method are introduced. Then, based on these theoretical introductions, the fault vibration signals of rolling bearing are dealt with related algorithm. The research results demonstrate that the characteristic frequency of bearing fault can be obtained by proposed method, which is more effective compared with existing algorithm.\",\"PeriodicalId\":6565,\"journal\":{\"name\":\"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)\",\"volume\":\"26 1\",\"pages\":\"481-485\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DDCLS.2018.8516038\",\"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 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DDCLS.2018.8516038","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fault Diagnosis of Rolling Bearing based on EMD Combined with HHT Envelope and Wavelet Spectrum Transform
A novel method based on Hilbert Transform (HT) and Empirical Mode Decomposition (EMD) algorithm is proposed in this paper, which separates time series into intrinsic mode functions (IMFs) with different time scales and applies the Hilbert transformation for every IMF to obtain the Hilbert spectrum. Firstly, relevant theories of the proposed method are introduced. Then, based on these theoretical introductions, the fault vibration signals of rolling bearing are dealt with related algorithm. The research results demonstrate that the characteristic frequency of bearing fault can be obtained by proposed method, which is more effective compared with existing algorithm.