SIA OpenIR  > 工业信息学研究室  > 工业控制系统研究室
Broken Rotor Bars Fault Detection in Induction Motors Using Park's Vector Modulus and FWNN Approach
Guo QJ(郭前进); Li XL(李晓利); Yu HB(于海斌); Hu W(胡为); Hu JT(胡静涛)
Conference Name5th International Symposium on Neural Networks
Conference DateSeptember 24-28, 2008
Conference PlaceBeijing, China
Publication PlaceBERLIN
Indexed ByEI ; CPCI(ISTP)
EI Accession number20090611891660
WOS IDWOS:000260605100092
Contribution Rank1
KeywordFault Detection Induction Motors Park's Vector Modulus Fwnn
AbstractIn this paper a new integrated diagnostic method based on the current Park's Vector modulus analysis and fuzzy wavelet neural network classifier is proposed for the diagnosis of rotor, cage faults in operating three-phase induction motors. Detection of broken rotor bars has long been an important but difficult job in the detection area of induction motor faults. The characteristic frequency components of a faulted rotor in the stator current spectrum are very close to the power frequency component but by far less in amplitude, which brings about great difficulty for accurate detection. In order to overcome the shortage of broken rotor bars characteristic components being submerged by the fundamental one in the spectrum of the stator line current, Park's Vector modulus(PVM) analysis is used to detect the occurrence of broken rotor bar faults in our work. Simulation and experimental results are presented to show the merits of this novel approach for the detection of cage induction motor broken rotor bars.
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Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Corresponding AuthorGuo QJ(郭前进)
Affiliation1.Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Department of Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
Recommended Citation
GB/T 7714
Guo QJ,Li XL,Yu HB,et al. Broken Rotor Bars Fault Detection in Induction Motors Using Park's Vector Modulus and FWNN Approach[C]. BERLIN:SPRINGER-VERLAG,2008:809-821.
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