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Detection of Bearing Outer Race Fault in Induction Motors using Motor Current Signature Analysis
Song XJ(宋向金)1; Wang ZW(王照伟)1; Hu JT(胡静涛)2
Department数字工厂研究室
Conference Name22nd International Conference on Electrical Machines and Systems, ICEMS 2019
Conference DateAugust 11-14, 2019
Conference PlaceHarbin, China
Source Publication2019 22nd International Conference on Electrical Machines and Systems, ICEMS 2019
PublisherIEEE
Publication PlaceNew York
2019
Pages1-5
Indexed ByEI ; CPCI(ISTP)
EI Accession number20200107954515
WOS IDWOS:000537504802029
Contribution Rank2
ISBN978-1-7281-3398-0
Keywordinduction motor bearing fault stator current signature analysis CEEMDAN
AbstractBearing damage is the most common fault type in induction motors. In recent years, bearing fault detection based on motor current signature analysis (MCSA) has been gained extensive attention. However, the changes in the stator current signal which is caused by the bearing fault are usually very weak. In order to detect bearing fault effectively, a method for bearing fault detection based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) analysis of motor current signals is proposed. The CEEMDAN is used to decompose the stator current signal into several independent intrinsic mode functions (IMF), then the most sensitive IMF can be extracted. The experimental results show that the proposed approach is effective for bearing outer race fault detection. © 2019 IEEE.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/26173
Collection数字工厂研究室
Corresponding AuthorSong XJ(宋向金)
Affiliation1.Jiangsu University, School of Electrical and Information Engineering, Zhenjiang 212013, China
2.Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang 110016, China
Recommended Citation
GB/T 7714
Song XJ,Wang ZW,Hu JT. Detection of Bearing Outer Race Fault in Induction Motors using Motor Current Signature Analysis[C]. New York:IEEE,2019:1-5.
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