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Mechanical Fault Classification of an Induction Motor using Texture Analysis
Journal of The Korea Society of Computer and Information
Abbr :
JKSCI
2013, 18(12), pp.11-19
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
2013, 18(12), pp.11-19
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
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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.
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