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http://dx.doi.org/10.9708/jksci.2011.16.12.187

A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance  

Hwang, Chul-Hee (School of Electrical Engineering, University of Ulsan)
Kang, Myeong-Su (School of Electrical Engineering, University of Ulsan)
Kim, Jong-Myon (School of Electrical Engineering, University of Ulsan)
Publication Information
Journal of the Korea Society of Computer and Information / v.16, no.12, 2011 , pp. 187-196 More about this Journal
Abstract
Induction motors play a vital role in aeronautical and automotive industries so that many researchers have studied on developing a fault detection and classification system of an induction motor to minimize economical damage caused by its fault. With this reason, this paper extracts robust feature vectors from the normal/abnormal vibration signals of the induction motor in noise circumstance: partial autocorrelation (PARCOR) coefficient, log spectrum powers (LSP), cepstrum coefficients mean (CCM), and mel-frequency cepstrum coefficient (MFCC). Then, we classified different types of faults of the induction motor by using the extracted feature vectors as inputs of a neural network. To find optimal feature vectors, this paper evaluated classification performance with 2 to 20 different feature vectors. Experimental results showed that five to six features were good enough to give almost 100% classification accuracy except features by CCM. Furthermore, we considered that vibration signals could include noise components caused by surroundings. Thus, we added white Gaussian noise to original vibration signals, and then evaluated classification performance. The evaluation results yielded that LSP was the most robust in noise circumstance, then PARCOR and MFCC followed by LSP, respectively.
Keywords
fault detection and classification system; partial autocorrelation coefficient; log spectrum powers; cepstrum coefficients mean; mel-frequency cepstrum coefficient; neural network;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 S.-S. Kim, H.-B. Kim, J.-W. Ryu, M.-G. Chun, "Detection and Diagnosis of Induction Motor using Conditional FCM and Radial Basis Function Network," in Proc. 2004 Korean Fuzzy and Intelligent Systems Conference, Vol. 14, No. 1, pp. 321-324, April 2004.
2 V. Crupi, E. Guglielmino, G. Milazzo, "Neural-network-based System for Novel Fault Detection in Rotating Machinery," Journal of Vibration and Control, Vol. 10, No. 8, pp. 1137-1150, Aug. 2004.   DOI   ScienceOn
3 F. Li, G. Meng, L. Ye, P. Chen, "Wavelet Transform-based Higher-order Statistics for Fault Diagnosis in Rolling Element Bearings," Journal of Vibration and Control, Vol. 14, No. 11, pp. 1691-1709, Nov. 2008.   DOI   ScienceOn
4 S. Haykin, "Neural Networks: A Comprehensive Foundation," Prentice-Hall, New Jersey, pp. 156-248, 1999.
5 R. P. Lippmam, "Pattern Classification using Neural Network," IEEE Communication Magazine, Vol. 27, No. 11, pp. 47-50, 59-64, Nov. 1989.   DOI   ScienceOn
6 B.-S. Yang, K. J. Kim, T. Han, "Fault Diagnosis of Induction Motors using Data Fusion of Vibration and Current Signal," Transaction of the Korean Society for Noise and Vibration Engineering, Vol. 14, No. 11, pp. 1091-1100, Nov. 2004.   DOI   ScienceOn
7 A. M. Kondoz, "Digital Speech Coding for Low Bit Rate Communication Systems," JOHN WILEY & SONS, pp. 42-52, 1995.
8 S. Z. Li, "Content-based Audio Classification and Retrieval using the Nearest Feature Line Method," IEEE Transactions on Speech and Audio Processing, Vol. 8, No. 5, pp. 619-625, Sep. 2000.   DOI   ScienceOn
9 Y. Hong, S. Cho, J.-M. Kim, U. Chong, "Formant Synthesis of Haegeum Sounds Using Cepstral Envelope," Journal of Acoustical Society of Korea, Vol. 28, No. 6, pp. 526-553, Aug. 2009.
10 H. Ocak, K. A. Loparo, "Estimation of the Running Speed and Bearing Defect Frequencies of an Induction Motor from Vibration Data," Mechanical Systems and Signal Processing, Vol. 18, No. 3, pp. 515-533, May 2004.   DOI   ScienceOn
11 I. A. Basheer, M. Hajmeer, "Artificial Neural Network: Fundamentals, Computing Design, and Application," Journal of Microbiological Methods, Vol. 43, Vol. 1, pp. 3-31, Dec. 2000.   DOI   ScienceOn