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http://dx.doi.org/10.9708/jksci.2013.18.7.023

An Effective Feature Extraction Method for Fault Diagnosis of Induction Motors  

Nguyen, Hung N. (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.18, no.7, 2013 , pp. 23-35 More about this Journal
Abstract
This paper proposes an effective technique that is used to automatically extract feature vectors from vibration signals for fault classification systems. Conventional mel-frequency cepstral coefficients (MFCCs) are sensitive to noise of vibration signals, degrading classification accuracy. To solve this problem, this paper proposes spectral envelope cepstral coefficients (SECC) analysis, where a 4-step filter bank based on spectral envelopes of vibration signals is used: (1) a linear predictive coding (LPC) algorithm is used to specify spectral envelopes of all faulty vibration signals, (2) all envelopes are averaged to get general spectral shape, (3) a gradient descent method is used to find extremes of the average envelope and its frequencies, (4) a non-overlapped filter is used to have centers calculated from distances between valley frequencies of the envelope. This 4-step filter bank is then used in cepstral coefficients computation to extract feature vectors. Finally, a multi-layer support vector machine (MLSVM) with various sigma values uses these special parameters to identify faulty types of induction motors. Experimental results indicate that the proposed extraction method outperforms other feature extraction algorithms, yielding more than about 99.65% of classification accuracy.
Keywords
fault classification; feature extraction; cepstral coefficients; support vector machines;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 F. V. Nelwamonodo, T. Marwala, and U. Mahola, "Early Classifications of Bearing Faults Using Hidden Markov Models, Gaussian Mixture Models, Mel-Frequency Cepstral Coefficients and Fractals," Journal of Innovative Computing, Information and Control, Vol. 2, pp. 1281-1299, 2006.
2 M. Ge, G. C. Zhang, and Y. Yu, "Feature Extraction from Energy Distribution of Stamping Processes Using Wavelet Transform," Journal of Vibration and Control, Vol. 8, pp. 1323-1032, 2002.
3 K. M. Silva, B. A. Souza, N. S. D. Brito, "Fault Detection and Classification in Transmission Lines Based on Wavelet Transform and ANN," IEEE Trans. on Power Delivery, Vol. 21, pp. 2058-2063, 2006.   DOI   ScienceOn
4 B.-S. Yang, T. Han, and W.W. Hwang, "Fault Diagnosis of Rotating Machinery based on Multi-Class Support Vector Machines," Journal of Mechanical Science and Technology, Vol. 19, No. 3, pp. 845-858, 2005.
5 B. -S. Yang, D.S Lim, and J.L. An, "Vibration Diagnostic System of Rotating Machinery using Artificial Neural Network and Wavelet Transform," Proc. of 13th Intl. Congress on COMADEM, pp. 12-20, 2000.
6 A. Widodo, B.-S. Yang, and T. Han, "Combination of Independent Component Analysis and Support Vector Machine for Intelligent Faults Diagnosis of Induction Motors," Expert System with Application, Vol. 32, pp. 299-312, 2007.   DOI   ScienceOn
7 A. Widodo, B. -S. Yang, T. Han, and D. J. Kim, "Fault Diagnosis of Induction Motor using Independent Component Analysis and Multi-Class Support Vector Machine," Proceedings of the 11th Asia-Pacific Vibration Conference, pp. 144-149, 2005.
8 S. Poyhonen, Support Vector Machine Based Classification in Condition Monitoring of Induction Motors, Thesis Presented at Helsinki University of Technology, 2004.
9 M. Deriche, "Bearing Fault Diagnosis Using Wavelet Analysis," International Conference on Computers, Communication and Signal Processing with Special Track on Biomedical Engineering, pp. 197-201, 2005.
10 N. Mehla and R. Dahiya, "An Approach of Condition Monitoring of Induction Motor Using MCSA," International Journal of Systems Applications, Engineering and Development, Vol. 1, pp. 13-17, 2007.
11 R. O. Duda, P. E. Hart, and D. G. Stock, Pattern Classification, Wiley, New York, 2001.
12 A. M. Da Silva, R. J. Povinelli, N.A.O. Demerdash, "Induction Machine Broken Bar and Stator Short-Circuit Fault Diagnostics Based on Three-Phase Stator Current Envelopes," IEEE Transaction on Industrial Electronics, Vol. 55, pp. 1310-1318, 2008.   DOI   ScienceOn
13 T. Han, B.-S. Yang, and Z.-J. Yin, "Feature-based Fault Diagnosis System of Induction Motors using Vibration Signal," Journal of Quality in Maintenance Engineering, Vol. 13, No. 2, pp. 163-175, 2007.   DOI   ScienceOn
14 T. H. Park, Introduction to Signal Processing: Computer Musically Speaking, World Scientific, Singapore, 2010.
15 M. Kang, N. Nguyen, Y. Kim, C. Kim, and J. Kim, "Feature Vector Extraction and Classification Performance Comparison According to Various Settings of Classifiers for Fault Detection and Classification of Induction Motor," J. of Acoustical Society of Korea, Vol. 30, No. 8, pp. 446-460, Dec. 2011.   과학기술학회마을   DOI   ScienceOn
16 C.-H. Hwang, Y,-M. Kim, C.-H. Kim, J.-M. Kim, "Fault Detection and Diagnosis of Induction Motors using LPC and DTWMethods," Journal of the Korea Society of Computer and Information, Vol. 16, No. 3, pp. 141-147, 2011.
17 C.-H. Hwang, M. Kang, J.-M. Kim, "A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance," Journal of the Korea Society of Computer and Information, Vol. 16, No. 12, pp. 187-196, 2011.   과학기술학회마을   DOI   ScienceOn
18 P. A. Laggan, "Vibration Monitoring," IEE Colloquium on Understanding Your Condition Monitoring, pp. 1-11, 1999.
19 H. Han, S. Cho, and U. Chong, "Fault Diagnosis System Using LPC Coefficients and Neural Network," Proc. of International Forum on Strategic Technology, pp. 87-90, 2010.
20 F. V. Nelwamonodo and T. Marwala, "Fault Detection Using Gaussian Mixture Models, Mel-Frequency Cepstral Coefficients and Kurtosis," IEEE International Conference on Systems, Man and Cybernetics, pp. 290-295, 2006.
21 T. Boukra and A. Lebaroud, "Classification on Induction Machine Faults," International Multi-Conference on Systems and Devices, pp. 1-6, 2007.
22 F. Li, G. Meng, L. Ye, and P. Chen, "Wavelet Transform-Based Higher-Order Statistics for Fault Diagnosis in Rolling Element Bearings," Journal of Vibration and Control, Vol. 14, pp. 1691-1709, 2008.   DOI   ScienceOn
23 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, pp. 515-533, 2004.   DOI   ScienceOn