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http://dx.doi.org/10.9708/jksci.2013.18.12.011

Mechanical Fault Classification of an Induction Motor using Texture Analysis  

Jang, Won-Chul (School of Electrical, Eletronics and Computer Engineering, University of Ulsan)
Park, Yong-Hoon (School of Electrical, Eletronics and Computer Engineering, University of Ulsan)
Kang, Myeong-Su (School of Electrical, Eletronics and Computer Engineering, University of Ulsan)
Kim, Jong-Myon (School of Electrical, Eletronics and Computer Engineering, University of Ulsan)
Publication Information
Journal of the Korea Society of Computer and Information / v.18, no.12, 2013 , pp. 11-19 More about this Journal
Abstract
This paper proposes an algorithm using vibration signals and texture analysis for mechanical fault diagnosis of an induction motor. We analyze characteristics of contrast and pattern of an image converted from vibration signal and extract three texture features using gray-level co-occurrence model(GLCM). Then, the extracted features are used as inputs of a multi-level support vector machine(MLSVM) which utilizes the radial basis function(RBF) kernel function to classify each fault type. In addition, we evaluate the classification performance with varying the parameter from 0.3 to 1.0 for the RBF kernel function of MLSVM, and the proposed algorithm achieved 100% classification accuracy with the parameter of the RBF from 0.3 to 1.0. Moreover, the proposed algorithm achieved about 98% classification accuracy with 15dB and 20dB noise inserted vibration signals.
Keywords
Fault diagnosis; Induction motor; Gray-level co-occurrence matrix; Texture analysis; Support vector machine;
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Times Cited By KSCI : 1  (Citation Analysis)
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