{"title":"Outer race bearing fault identification of induction motor based on stator current signature by wavelet transform","authors":"S. Yeolekar, G. Mulay, J. Helonde","doi":"10.1109/RTEICT.2017.8256951","DOIUrl":null,"url":null,"abstract":"This paper presents the results of laboratory work carried out for identifying the outer race bearing fault occurred in an induction motor. The knowledge about fault behavior of an induction motor is extremely important for overall operational life of the machine. The paper refers to spectral analysis of the motor stator current, which includes routine stator current, noise and specific fault current signature. Using separate healthy and faulty bearing on the machine, testing is carried out for obtaining set of healthy and faulty currents for different load conditions. The specific fault signature can be separated using feature extraction in time domain Wavelet and after getting spectral information using classification technique ANN fault is identified.","PeriodicalId":342831,"journal":{"name":"2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RTEICT.2017.8256951","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
This paper presents the results of laboratory work carried out for identifying the outer race bearing fault occurred in an induction motor. The knowledge about fault behavior of an induction motor is extremely important for overall operational life of the machine. The paper refers to spectral analysis of the motor stator current, which includes routine stator current, noise and specific fault current signature. Using separate healthy and faulty bearing on the machine, testing is carried out for obtaining set of healthy and faulty currents for different load conditions. The specific fault signature can be separated using feature extraction in time domain Wavelet and after getting spectral information using classification technique ANN fault is identified.