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http://dx.doi.org/10.7776/ASK.2011.30.8.446

Feature Vector Extraction and Classification Performance Comparison According to Various Settings of Classifiers for Fault Detection and Classification of Induction Motor  

Kang, Myeong-Su (울산대학교 전기공학부)
Nguyen, Thu-Ngoc (울산대학교 전기공학부)
Kim, Yong-Min (울산대학교 전기공학부)
Kim, Cheol-Hong (전남대학교 전자컴퓨터공학부)
Kim, Jong-Myon (울산대학교 전기공학부)
Publication Information
The Journal of the Acoustical Society of Korea / v.30, no.8, 2011 , pp. 446-460 More about this Journal
Abstract
The use of induction motors has been recently increasing with automation in aeronautical and automotive industries, and it playes a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of an induction motor in order to minimize economical damage caused by its fault. With this reason, this paper proposed feature vector extraction methods based on STE (short-time energy)+SVD (singular value decomposition) and DCT (discrete cosine transform)+SVD techniques to early detect and diagnose faults of induction motors, and classified faults of an induction motor into different types of them by using extracted features as inputs of BPNN (back propagation neural network) and multi-layer SVM (support vector machine). When BPNN and multi-lay SVM are used as classifiers for fault classification, there are many settings that affect classification performance: the number of input layers, the number of hidden layers and learning algorithms for BPNN, and standard deviation values of Gaussian radial basis function for multi-layer SVM. Therefore, this paper quantitatively simulated to find appropriate settings for those classifiers yielding higher classification performance than others.
Keywords
Induction motor; Feature vector extraction; Back propagation neural network; Multi-layer support vector machine;
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Times Cited By KSCI : 4  (Citation Analysis)
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