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Appl. Sci. 2017, 7(1), 31; doi:10.3390/app7010031

Implementation of a Motor Diagnosis System for Rotor Failure Using Genetic Algorithm and Fuzzy Classification

Department of Electrical Engineering, National Taiwan University of Science and Technology 43, Sec. 4, Keelung Rd., Taipei 106, Taiwan
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Author to whom correspondence should be addressed.
Academic Editor: Stephen D. Prior
Received: 26 September 2016 / Revised: 15 December 2016 / Accepted: 21 December 2016 / Published: 27 December 2016

Abstract

In this paper, the diagnosis of induction motor rotor failure with fuzzy theory and genetic algorithm is presented. The proposed method can evaluate the status of an operating motor. According to the measurement of electrical data, this research establishes the relationship of rotor failures with spectrum features. Through the learning of genetic algorithm, membership parameters can be adjusted to optimal positions. The simulation that combines fuzzy theory and a genetic algorithm has preferable diagnostic results for the rotor failures. The designed processes will be applied as a reference for building the diagnostic methods of other motor failures. View Full-Text
Keywords: squirrel-cage induction motor; diagnosis of rotor bar failures; electrical detection; fuzzy theory; genetic algorithm squirrel-cage induction motor; diagnosis of rotor bar failures; electrical detection; fuzzy theory; genetic algorithm
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MDPI and ACS Style

Kuo, C.-C.; Liu, C.-H.; Chang, H.-C.; Lin, K.-J. Implementation of a Motor Diagnosis System for Rotor Failure Using Genetic Algorithm and Fuzzy Classification. Appl. Sci. 2017, 7, 31.

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