A study on reliability based assessment algorithm for high voltage induction stator windmgs

Chang, Jeong-Ho; Lee, Heung-Ho

doi:10.1109/ipmhvc.2012.6518815

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2016

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Cited by 2 publications

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“…The combined use of this diagnostic system and the feature frequency components diagnoses the fault type of induction motors and assesses the severity of the fault. Thus, the diagnostic system can facilitate the preparation of a maintenance program [14].…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Diagnostic System Based on Electrical Detection Method and Fuzzy Algorithm

Chang

Lin

Kuo

et al. 2016

Int. J. Fuzzy Syst.

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This study develops an electrical detection method for the diagnosis and fault detection of induction motors. An experiment constructs two types of defect models: broken bar and dynamic eccentricity. Electrical signals acquired during the operation of a motor are transformed through a fast Fourier transform to obtain the feature frequency components for identifying the type of motor fault. Subsequently, the Clark-Concordia transform is used to compare the stator current Concordia pattern between faulty and healthy motors. Finally, a fuzzy inference system is designed for assessing the severity of motor faults. The proposed method not only can diagnose the type of motor fault, but can also assess the operational state of a motor. The method is suitable for preparing a maintenance program for induction motors and for reducing their excessive maintenance cost.

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Section: Introductionmentioning

confidence: 99%