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Title: An Unbalance Component Technique for Induction Motor Fault Detection
Author(s): De Z. Li, Wilson Wang
Pages: 1-8 Paper ID:171906-8484-IJECS-IJENS Published: December, 2017
Abstract: Although many fault detection techniques have been proposed in literature for induction motor (IM) condition monitoring, reliable IM fault detection still remains a challenging task especially in real industrial applications. An unbalance component analysis (UCA) technique is developed in this paper to extract representative features from line currents for fault detection in IMs. The UCA is composed of two processing procedures: closed loop current analysis and spectrum aggregation. A closed loop current analysis approach is proposed to reduce balanced components in each phase, and reveal the fault features due to unbalance. A spectrum aggregation method is suggested to integrate the fault features among three phases for broken rotor bars and bearing fault detection. The effectiveness of the developed UCA technique is verified by experiments corresponding to IMs with broken rotor bars and the bearing defect.
Keywords: Induction motors, fault detection, rolling element bearings, broken rotor bars, symmetrical component analysis.
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