Fault Classification for Rotating Machinery Using Support Vector Machines with Optimal Features Corresponding to Each Fault Type

결함유형별 최적 특징과 Support Vector Machine 을 이용한 회전기계 결함 분류

  • Received : 2010.06.07
  • Accepted : 2010.08.27
  • Published : 2010.11.01


Several studies on the use of Support Vector Machines (SVMs) for diagnosing rotating machinery have been successfully carried out, but the fault classification depends on the input features as well as a multi-classification scheme, binary optimizer, kernel function, and the parameter to be used in the kernel function. Most of the published papers on multiclass SVM applications report the use of the same features to classify the faults. In this study, simple statistical features are determined on the basis of time domain vibration signals for various fault conditions, and the optimal features for each fault condition are selected. Then, the optimal features are used in the SVM training and in the classification of each fault condition. Simulation results using experimental data show that the results of the proposed stepwise classification approach with a relatively short training time are comparable to those for a single multi-class SVM.


  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.
  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.
  3. Jack, L.B. and Nandi, A.K., 2002, "Fault Detection Using Support Vector Machines and Artificial Neural Networks, Augmented by Genetic Algorithms," Mechanical Systems and Signal Processing, Vol.16, pp.373-390.
  4. Hwang, W.W. and Yang, B.S., 2004, "Fault Diagnosis of Rotating Machinery Using Multi-Class Support Vector Machines," Trans. of the KSNVE, Vol. 14, No.12, pp.1233-1240.
  5. Samanta, B., 2004, "Gear Fault Detection Using Artificial Neural Networks and Support Vector Machines with Genetic Algorithms," Mechanical Systems and Signal Processing, Vol.18, pp.625-644.
  6. Yang, B.S., Hwang, W.W., Kim, D.J. and Tan, A., 2005, "Condition Classification of Small Reciprocating Compressor for Refrigerators Using Artificial Neural Networks and Support Vector Machines," Mechanical Systems and Signal Processing, Vol.19, pp.371-390.
  7. Hu, Q, He, Z.J, Zhang, Z.S. and Zi, Y.Y., 2007, "Fault Diagnosis of Rotating Machinery Based on Improved Wavelet Package Transform and SVMs ensemble," Mechanical Systems and Signal Processing, Vol.21, pp.688-705.
  8. 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.
  9. 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.
  10. 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.
  11. Hu, Z.H, Cai, Y.Z., Li, Y.G. and Xu, X.M., 2005, "Data Fusion for Fault Diagnosis Using Multi-class Support Vector Machines," J. Zhejiang University Science, Vol. 10, No.6A, pp.1030-1039.
  12. Yan W.W., Shao, H.H. and Wang, X.F., 2003, "Parallel Decision Models Based on Support Vector Machines and Their Application to Distributed Fault Diagnosis," Proceedings of the American Control Conference, Denver, Colorado, June 4-6, 2003, pp.1770-1775.
  13. Kim, H.C., Pang, S.N., Je, H.M., Kim, D.J. and Bang S.Y., 2003, "Constructing Support Vector Machine Ensemble," Pattern Recognition, Vol. 36, pp.2757-2767.
  14. Rovero. D, 2002, "ARTD: Autonomous Recursive Task Decomposition for Many-Class Learning," International J. Knowledge-Based Intelligent Engineering Systems, Vol. 6, No. 4.
  15. Cristianini, N. and Shawe-Taylor, J., 2000, An Introduction to Support Vector Machines and other Kernel-Based Learning Methods, Cambridge University Press, Cambridge.
  16. 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.
  17. Platt, J.C., 1998, Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, Technical Report MSR-TR-98-14.

Cited by

  1. Prognostic Technique for Ball Bearing Damage vol.37, pp.11, 2013,