Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

Mechanical Fault Classification of an Induction Motor using Texture Analysis

질감 분석을 이용한 유도 전동기의 기계적 결함 분류

  • Jang, Won-Chul (School of Electrical, Eletronics and Computer Engineering, University of Ulsan) ;
  • Park, Yong-Hoon (School of Electrical, Eletronics and Computer Engineering, University of Ulsan) ;
  • Kang, Myeong-Su (School of Electrical, Eletronics and Computer Engineering, University of Ulsan) ;
  • Kim, Jong-Myon (School of Electrical, Eletronics and Computer Engineering, University of Ulsan)
  • 장원철 (울산대학교 전기전자컴퓨터공학과) ;
  • 박용훈 (울산대학교 전기전자컴퓨터공학과) ;
  • 강명수 (울산대학교 전기전자컴퓨터공학과) ;
  • 김종면 (울산대학교 전기전자컴퓨터공학과)
  • Received : 2013.07.30
  • Accepted : 2013.11.07
  • Published : 2013.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes an algorithm using vibration signals and texture analysis for mechanical fault diagnosis of an induction motor. We analyze characteristics of contrast and pattern of an image converted from vibration signal and extract three texture features using gray-level co-occurrence model(GLCM). Then, the extracted features are used as inputs of a multi-level support vector machine(MLSVM) which utilizes the radial basis function(RBF) kernel function to classify each fault type. In addition, we evaluate the classification performance with varying the parameter from 0.3 to 1.0 for the RBF kernel function of MLSVM, and the proposed algorithm achieved 100% classification accuracy with the parameter of the RBF from 0.3 to 1.0. Moreover, the proposed algorithm achieved about 98% classification accuracy with 15dB and 20dB noise inserted vibration signals.

본 논문에서는 유도 전동기의 기계적 결함을 진단하기 위해 진동신호와 질감 분석을 이용한 알고리즘을 제안한다. 영상화된 결함 신호가 갖는 무늬, 색상 대비의 특징을 분석하고, 그레이레벨 동시발생행렬(Gray-Level Co-occurrence Model, GLCM)을통해 세 가지 질감특징을추출한다. 추출된 세 가지질감 특징을 RBF(Radial Basis Function) 커널 함수를 사용하는 다중레벨 서포터 벡터 머신(Multi-Level Support Vector Machine, MLSVM)의 입력으로 사용하여 결함 유형을 분류한다. 결함 유형을 분류하는 최적의 MLSVM을 위한 RBF 커널 함수의 매개변수를 찾기 위해 매개변수 값을 0.3부터 1.0으로 바꿔가며 분류성능을 평가한 결과, 결함 유형별로 0.3에서 0.6사이의 매개변수 값에서 100%에 가까운 분류 정확성을 보였다. 또한 15dB, 20dB의 잡음이 첨가된 진동신호를 이용한 실험에서도 평균 98%이상의 높은 분류 정확성을 보였다.

Keywords

References

  1. Bilal Akin, Seungdeog Choi, Umut Orguner, Hamid A. Toliyat, "A Simple Real-Time Fault Signature Monitoring Tool for Motor-Drive- Embedded Fault Diagnosis Systems", IEEE Transactions On Industrial Electronics, Vol. 58, No. 5, pp. 1990-2001, May 2011. https://doi.org/10.1109/TIE.2010.2051936
  2. Khurram Shahzad, Peng Cheng, Bengt Oelmann, "Architecture Exploration for a High- Performance and Low-Power Wireless Vibration Analyzer", IEEE Sensors Journal, Vol. 13, No. 2, pp. 670-682, Feb. 2013. https://doi.org/10.1109/JSEN.2012.2226238
  3. Subhasis Nandi, Hamid A. Toliyat, Xiaodong Li, "Condition Monitoring and Fault Diagnosis of Electrical Motors", IEEE Transactions On Energy Conversion, Vol. 20, No. 4, pp. 719-729, Dec. 2005. https://doi.org/10.1109/TEC.2005.847955
  4. Arfat Siddique, G. S. Yadava, Bhim Singh, "A Review of Stator Fault Monitoring Techniques of Induction Motors", IEEE Transactions On Energy Conversion, Vol. 20, No. 1, pp. 106-114, March 2005. https://doi.org/10.1109/TEC.2004.837304
  5. 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, March 2008. https://doi.org/10.1109/TIE.2007.909060
  6. J. Cusido, J. Rosero, E. Aldabas, J.A. Ortega, and L. Romeral, "Fault detection techniques for induction motors," Intl. Conf. Comp. Power Elec., pp. 85-90 June 2005.
  7. N. Mehala, and R. Dahiya, "Rotor faults detection in induction motor by Wavelets Analysis," Intl. J. Engi. Scie. Tech., vol. 1, no. 3,pp. 90-99, March 2009.
  8. M. Blodt, P. Granjon, B. Raison, J. Regnier, "Mechanical fault detection in induction motor drives through stator current monitoring-theory and application example," Fault Detection, book edited by Wei Zhang, Intech, pp. 451-488, March 2010.
  9. 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. https://doi.org/10.1016/S0888-3270(03)00052-9
  10. V. T. Do and U. -P. Chong, "Signal Model- Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two-Dimension Domain," J. Mech. Eng., vol. 57, no. 9, pp. 655-666, 2011. https://doi.org/10.5545/sv-jme.2010.162
  11. Haralick, R.M., Shanmugam, K., Dinstein, Its'Hak, "Textural Features for Image Classification", IEEE Transactions On Systems, Man and Cybernetics, vol. SMC-3, no. 6, pp. 610-621, Nov. 1973. https://doi.org/10.1109/TSMC.1973.4309314
  12. 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. https://doi.org/10.5050/KSNVN.2004.14.11.1091
  13. 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, Nov. 2005.

Cited by

  1. Aging feature extraction of oil-impregnated insulating paper using image texture analysis vol.24, pp.3, 2017, https://doi.org/10.1109/TDEI.2017.006319