K. C. Deekshit Kompella, M. V. Gopala Rao, R. S. Rao, R. N. Sreenivasu
{"title":"基于Daubechies小波分析的MCSA感应电机轴承故障估计","authors":"K. C. Deekshit Kompella, M. V. Gopala Rao, R. S. Rao, R. N. Sreenivasu","doi":"10.1109/ISEG.2014.7005577","DOIUrl":null,"url":null,"abstract":"This paper presents the current based monitoring of induction motor for identification of bearing faults. Sensor less monitoring has many advantages over conventional vibration monitoring. The method however does not give good performance due to variable load and speed of induction motor. Due to non stationary nature of stator current, Fourier transform problems may occur. Therefore, this work presents the motor current signature analysis using wavelet analysis and compares with the FFT analysis. The proposed method has been applied to detect the bearing faults in 3 phase induction motor. It is difficult to extract the bearing fault component from stator current spectrum especially at incipient stage. Therefore, here the bearing fault can be identified by cancelling nonbearing fault component from stator current signature. The results have affirmed the effectiveness of the method.","PeriodicalId":105826,"journal":{"name":"2014 International Conference on Smart Electric Grid (ISEG)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Estimation of bearing faults in induction motor by MCSA using Daubechies wavelet analysis\",\"authors\":\"K. C. Deekshit Kompella, M. V. Gopala Rao, R. S. Rao, R. N. Sreenivasu\",\"doi\":\"10.1109/ISEG.2014.7005577\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the current based monitoring of induction motor for identification of bearing faults. Sensor less monitoring has many advantages over conventional vibration monitoring. The method however does not give good performance due to variable load and speed of induction motor. Due to non stationary nature of stator current, Fourier transform problems may occur. Therefore, this work presents the motor current signature analysis using wavelet analysis and compares with the FFT analysis. The proposed method has been applied to detect the bearing faults in 3 phase induction motor. It is difficult to extract the bearing fault component from stator current spectrum especially at incipient stage. Therefore, here the bearing fault can be identified by cancelling nonbearing fault component from stator current signature. The results have affirmed the effectiveness of the method.\",\"PeriodicalId\":105826,\"journal\":{\"name\":\"2014 International Conference on Smart Electric Grid (ISEG)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Smart Electric Grid (ISEG)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISEG.2014.7005577\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Smart Electric Grid (ISEG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISEG.2014.7005577","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Estimation of bearing faults in induction motor by MCSA using Daubechies wavelet analysis
This paper presents the current based monitoring of induction motor for identification of bearing faults. Sensor less monitoring has many advantages over conventional vibration monitoring. The method however does not give good performance due to variable load and speed of induction motor. Due to non stationary nature of stator current, Fourier transform problems may occur. Therefore, this work presents the motor current signature analysis using wavelet analysis and compares with the FFT analysis. The proposed method has been applied to detect the bearing faults in 3 phase induction motor. It is difficult to extract the bearing fault component from stator current spectrum especially at incipient stage. Therefore, here the bearing fault can be identified by cancelling nonbearing fault component from stator current signature. The results have affirmed the effectiveness of the method.