An Effective Feature Extraction Method for Fault Diagnosis of Induction Motors

Hung Nguyen (홍뉘엔); Jong Myon Kim (김종면)

An Effective Feature Extraction Method for Fault Diagnosis of Induction Motors

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2013, 18(7), pp.23-35
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

Hung Nguyen ¹, Jong Myon Kim ¹

¹울산대학교

Accredited

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

Citation status

* References for papers published after 2023 are currently being built.

[journal] A. M. Da Silva / 2008 / Induction Machine Broken Bar and Stator Short-Circuit Fault Diagnostics Based on Three-Phase Stator Current Envelopes / IEEE Transaction on Industrial Electronics 55 : 1310~1318

[journal] 황철희 / 2011 / Fault Detection and Diagnosis of Induction Motors using LPC and DTW Methods / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 16(3) : 141~148

[journal] 황철희 / 2011 / A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 16(12) : 187~196

[confproc] P. A. Laggan / 1999 / Vibration Monitoring / IEE Colloquium on Understanding Your Condition Monitoring : 1~11

[confproc] H. Han / 2010 / Fault Diagnosis System Using LPC Coefficients and Neural Network / Proc. of International Forum on Strategic Technology : 87~90

[confproc] F. V. Nelwamonodo / 2006 / Fault Detection Using Gaussian Mixture Models, Mel-Frequency Cepstral Coefficients and Kurtosis / IEEE International Conference on Systems : 290~295

[confproc] T. Boukra / 2007 / Classification on Induction Machine Faults / International Multi-Conference on Systems and Devices : 1~6

[journal] F. Li / 2008 / Wavelet Transform-Based Higher-Order Statistics for Fault Diagnosis in Rolling Element Bearings / Journal of Vibration and Control 14 : 1691~1709

[journal] H. Ocak / 2004 / Estimation of the Running Speed and Bearing Defect Frequencies of an Induction Motor from Vibration Data / Mechanical Systems and Signal Processing 18 : 515~533

[journal] F. V. Nelwamonodo / 2006 / 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 2 : 1281~1299

[journal] M. Ge / 2002 / Feature Extraction from Energy Distribution of Stamping Processes Using Wavelet Transform / Journal of Vibration and Control 8 : 1323~1032

[journal] K. M. Silva / 2006 / Fault Detection and Classification in Transmission Lines Based on Wavelet Transform and ANN / IEEE Trans. on Power Delivery 21 : 2058~2063

[journal] Bo-Suk Yang / 2005 / Fault Diagnosis of Rotating Machinery Based on Multi-Class Support Vector Machines / Journal of Mechanical Science and Technology / 대한기계학회 19(3) : 846~859

[confproc] B. –S. Yang / 2000 / Vibration Diagnostic System of Rotating Machinery using Artificial Neural Network and Wavelet Transform / Proc. of 13th Intl. Congress on COMADEM

[journal] A. Widodo / 2007 / Combination of Independent Component Analysis and Support Vector Machine for Intelligent Faults Diagnosis of Induction Motors / Expert System with Application 32 : 299~312

[confproc] A. Widodo / 2005 / Fault Diagnosis of Induction Motor using Independent Component Analysis and Multi-Class Support Vector Machine / Proceedings of the 11th Asia-Pacific Vibration Conference : 144~149

[confproc] S. Poyhonen / 2004 / Support Vector Machine Based Classification in Condition Monitoring of Induction Motors / Thesis Presented at Helsinki University of Technology

[confproc] M. Deriche / 2005 / Bearing Fault Diagnosis Using Wavelet Analysis / International Conference on Computers, Communication and Signal Processing with Special Track on Biomedical Engineering : 197~201

[journal] N. Mehla / 2007 / An Approach of Condition Monitoring of Induction Motor Using MCSA / International Journal of Systems Applications, Engineering and Development 1 : 13~17

[book] R. O. Duda / 2001 / Pattern Classification / Wiley

[journal] T. Han / 2007 / Feature-based Fault Diagnosis System of Induction Motors using Vibration Signal / Journal of Quality in Maintenance Engineering 13(2) : 163~175

[book] T. H. Park / 2010 / Introduction to Signal Processing: Computer Musically Speaking / World Scientific

[journal] 강명수 / 2011 / Feature Vector Extraction and Classification Performance Comparison According to Various Settings of Classifiers for Fault Detection and Classification of Induction Motor / 한국음향학회지 / 한국음향학회 30(8) : 446~460

This paper was written with support from the National Research Foundation of Korea.

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65