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Fault Severity Diagnosis of Ball Bearing by Support Vector Machine

서포트 벡터 머신을 이용한 볼 베어링의 결함 정도 진단

  • Kim, Yang-Seok (Central Research Institute, Korea Hydro & Nuclear Co., Ltd.) ;
  • Lee, Do-Hwan (Central Research Institute, Korea Hydro & Nuclear Co., Ltd.) ;
  • Kim, Dae-Woong (Central Research Institute, Korea Hydro & Nuclear Co., Ltd.)
  • 김양석 (한국수력원자력(주) 중앙연구원) ;
  • 이도환 (한국수력원자력(주) 중앙연구원) ;
  • 김대웅 (한국수력원자력(주) 중앙연구원)
  • Received : 2012.06.14
  • Accepted : 2013.03.19
  • Published : 2013.06.01

Abstract

A support vector machine (SVM) is a very powerful classification algorithm when a set of training data, each marked as belonging to one of several categories, is given. Therefore, SVM techniques have been used as one of the diagnostic tools in machine learning as well as in pattern recognition. In this paper, we present the results of classifying ball bearing fault types and severities using SVM with an optimized feature set based on the minimum distance rule. A feature set as an input for SVM includes twelve time-domain and nine frequency-domain features that are extracted from the measured vibration signals and their decomposed details and approximations with discrete wavelet transform. The vibration signals were obtained from a test rig to simulate various bearing fault conditions.

서포트 벡터 머신(Support Vector Machine, SVM)은 학습용 데이터 집합이 확보되어 있을 경우, 매우 강력한 분류 알고리즘이다. 따라서 패턴인식은 물론 기계학습 분야에서 결함진단 도구의 하나로 이용되고 있다. 본 논문에서는 최적 특징과 SVM 을 이용하여 볼 베어링의 결함유형과 결함의 정도를 진단한 결과를 기술하였다. SVM 학습용 특징데이터에는 12 개의 시간영역 특징과 9 개의 주파수영역 특징들이 포함되어 있으며 이들 특징들은 다양한 베어링 결함조건에서 측정된 진동신호와 진동신호의 이산 웨이블렛 변환신호로부터 추출되었다.

Keywords

References

  1. Tse, P.W., Peng, Y.H. and Yam, R., 2001, "Wavelet Analysis and Envelope Detection for Rolling Element Bearing Fault Diagnosis - Their Effectiveness and Flexibilities," Journal of Vibration and Acoustics, Vol.123, pp.303-310. https://doi.org/10.1115/1.1379745
  2. Zhang, Y.X. and Randall, R.B., 2009, "Rolling Element Bearing Fault Diagnosis Based on the Combination of Genetic Algorithm and Fast Kurtogram," Mechanical Systems and Signal Processing, Vol.23, pp.1509-1517. https://doi.org/10.1016/j.ymssp.2009.02.003
  3. Samanta, B., Al-Balushi, K.R. and Al-Araimi, S.A., 2003, "Artificial Neural Networks and Support Vector Machines with Genetic Algorithm for Bearing Fault Detection," Engineering Applications of Artificial Intelligence, Vol. 16, pp.657-665. https://doi.org/10.1016/j.engappai.2003.09.006
  4. Yang, B.S., Han, T. and Hwang, W.W., 2005, "Fault Diagnosis of Rotating Machinery Based on Multi-Class Support Vector Machines," KSME Int. J., Vol. 19, No.31, pp.846-859. https://doi.org/10.1007/BF02916133
  5. Tyagi, C.S., 2008, "A Comparative Study of SVM Classifiers and Artificial Neural Networks Application for Rolling Element Bearing Fault Diagnosis using Wavelet Transform Preprocessing," Proceedings of World Academy of Science, Engineering and Technology, Vol.33, pp.319-327.
  6. Kankar, P.K., Sharma, Satish C. and Harsha, S.P., 2011, "Fault Diagnosis of Ball Bearings Using Continuous Wavelet Transform," Applied Soft Computing, Vol. 11, pp.2300-2312. https://doi.org/10.1016/j.asoc.2010.08.011
  7. Vapnik, V.N., 1999, An Overview of Statistical Learning Theory, IEEE Transactions on Neural Networks, Vol.10, No.5, pp.988-999. https://doi.org/10.1109/72.788640
  8. Burges, C.J.C., 1998, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, Vol.2, p.121-167. https://doi.org/10.1023/A:1009715923555
  9. Cristianini, N. and Shawe-Taylor, J., 2000, An Introduction to Support Vector Machines and other Kernel-Based Learning Methods, Cambridge University Press, Cambridge.
  10. Hsu, C.W. and Lin, C.J., 2002, "A Comparison of Methods for Multiclass Support Vector Machines," IEEE Transactions on Neural Networks, Vol. 13, No.2, pp.415-425. https://doi.org/10.1109/72.991427
  11. Platt, J.C., 1998, Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, Technical Report MSR-TR-98-14.
  12. Fukunaga, K., 1990, Introduction to Statistical Pattern Recognition, Academic Press.
  13. Kim, Y.S., Lee, D.H. and Park, S.K., 2012, "Fault Size Classification of Rotating Machinery Using Support Vector Machine," The 18th Pacific Basian Nuclear Conference (PBNC 2012), Busan, Korea, March 18-22, 2012.