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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Broken Rotor Bars Fault Detection in Induction Motors Using Park’s Vector Modulus and FWNN Approach
verfasst von : Qianjin Guo, Xiaoli Li, Haibin Yu, Wei Hu, Jingtao Hu
Erschienen in: Advances in Neural Networks - ISNN 2008
Verlag: Springer Berlin Heidelberg
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In 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.