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

Korean Society of Computer Information (한국컴퓨터정보학회)
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Bearing Multi-Faults Detection of an Induction Motor using Acoustic Emission Signals and Texture Analysis

음향 방출 신호와 질감 분석을 이용한 유도전동기의 베어링 복합 결함 검출

  • Jang, Won-Chul (School of Electrical and Computer Engineering, University of Ulsan) ;
  • Kim, Jong-Myon (School of Electrical and Computer Engineering, University of Ulsan)
  • 장원철 (울산대학교 전기전자컴퓨터공학과) ;
  • 김종면 (울산대학교 전기전자컴퓨터공학과)
  • Received : 2014.02.18
  • Accepted : 2014.03.27
  • Published : 2014.04.30
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Abstract

This paper proposes a fault detection method utilizing converted images of acoustic emission signals and texture analysis for identifying bearing's multi-faults which frequently occur in an induction motor. The proposed method analyzes three texture features from the converted images of multi-faults: multi-faults image's entropy, homogeneity, and energy. These extracted features are then used as inputs of a fuzzy-ARTMAP to identify each multi-fault including outer-inner, inner-roller, and outer-roller. The experimental results using ten times trials indicate that the proposed method achieves 100% accuracy in the fault classification.

본 논문에서는 유도 전동기 결함 중 가장 많은 비중을 차지하는 베어링의 복합 결함을 검출하기 위해 음향 방출 신호와 이를 영상화하여 질감 분석을 이용한 결함 검출 방법을 제안한다. 영상화된 결함 신호가 갖는 엔트로피, 픽셀의 동질성 및 에너지 특징을 분석하고, 그레이-레벨 동시발생 행렬을 통해 영상의 에너지, 동질성 및 다양성의 세 가지 질감 특징을 추출한다. 추출된 세 가지 질감 특징을 퍼지-ARTMAP(Fuzzy-ARTMAP)의 입력으로 사용하여 베어링의 외륜-내륜, 내륜-롤러 및 외륜-롤러에 대한 복합 결함을 분류한다. 총 10회에 걸쳐 제안한 방법의 분류 성능을 평가한 결과, 100%의 분류 정확성을 보였다.

Keywords

References

  1. K. Shahzad, P. Cheng, and B. Oelmann, "Architecture Exploration for a High-Performance and Low-Power Wireless Vibration Analyzer", IEEE Sensors Journal, Vol. 13, No. 2, pp. 670-682, February 2013. https://doi.org/10.1109/JSEN.2012.2226238
  2. 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. https://doi.org/10.9708/jksci.2011.16.12.187
  3. 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.
  4. 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.
  5. W. Zhou, B. Lu, T. G. Habetler, and R. G. Harley, "Incipient Bearing Fault Detection via Motor Stator Current Noise Cancellation Using Wiener Filter", IEEE Transactions on Industry Applications, Vol. 45, No. 4, pp. 1309-1317, July 2009. https://doi.org/10.1109/TIA.2009.2023566
  6. S. Lu, Q. He, F. Hu, and F. Kong, "Sequential Multiscale Noise Tuning Stochastic Resonance for Train Bearing Fault Diagnosis in an Embedded System", IEEE Transactions on Instrumentation and Measurement, Vol. 63, No. 1, pp. 106-116, Jan. 2014. https://doi.org/10.1109/TIM.2013.2275241
  7. X. Jin, M. Zhao, T.W.S. Chow, and M. Pecht, "Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis", IEEE Transaction on Industrial Electronics, Vol. 61, No. 5, pp. 2441-2451, May 2014. https://doi.org/10.1109/TIE.2013.2273471
  8. T. H. Loutas, D. Roulias, and G. Georgoulas, "Remaining Useful Life Estimation in Rolling Bearings Utilizing Data-Driven Probabilistic E-Support Vectors Regression", IEEE Transactions on Reliability, Vol. 62, No. 4, pp. 821-832, Dec. 2013. https://doi.org/10.1109/TR.2013.2285318
  9. 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
  10. R. M. Haralick, K. Shanmugam, and Its Hak Dinstein, "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
  11. G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, "Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps", IEEE Trans. Neural Networks, Vol 3, No. 5, pp. 698-713, Sep. 1992. https://doi.org/10.1109/72.159059
  12. Do, V. T. and Chong, U.-P., "Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two-Dimension Domain," Journal of Mechanical Engineering, Vol. 57, No. 9, pp. 655-666, 2011. https://doi.org/10.5545/sv-jme.2010.162

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