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http://dx.doi.org/10.9708/jksci.2014.19.2.021

High-Reliable Classification of Multiple Induction Motor Faults using Robust Vibration Signatures in Noisy Environments based on a LPC Analysis and an EM Algorithm  

Kang, Myeongsu (School of Electrical, Eletronics and Computer Engineering, University of Ulsan)
Jang, Won-Chul (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.19, no.2, 2014 , pp. 21-30 More about this Journal
Abstract
The use of induction motors has been recently increasing in a variety of industrial sites, and they play a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of induction motors in order to reduce economical damage caused by their faults. To early identify induction motor faults, this paper effectively estimates spectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC) analysis technique and an expectation maximization (EM) algorithm. Moreover, this paper classifies induction motor faults into their corresponding categories by calculating Mahalanobis distance using the estimated spectral envelopes and finding the minimum distance. Experimental results show that the proposed approach yields higher classification accuracies than the state-of-the-art conventional approach for both noiseless and noisy environments for identifying the induction motor faults.
Keywords
Induction motor; Fault classification; LPC Analysis; EM algorithm; Mahalanobis distance;
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Times Cited By KSCI : 5  (Citation Analysis)
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