Decision Tree with Optimal Feature Selection for Bearing Fault Detection

  • Nguyen, Ngoc-Tu (School of Electrical Engineering, University of Ulsan) ;
  • Lee, Hong-Hee (School of Electrical Engineering, University of Ulsan)
  • Published : 2008.02.20

Abstract

In this paper, the features extracted from vibration time signals are used to detect the bearing fault condition. The decision tree is applied to diagnose the bearing status, which has the benefits of being an expert system that is based on knowledge history and is simple to understand. This paper also suggests a genetic algorithm (GA) as a method to reduce the number of features. In order to show the potentials of this method in both aspects of accuracy and simplicity, the reduced-feature decision tree is compared with the non reduced-feature decision tree and the PCA-based decision tree.

References

  1. V. Sugumaran, V. Muralidharan, K. I. Ramachandran, 'Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing', Mechanical Systems and Signal Processing 21, pp. 930-942, 2007 https://doi.org/10.1016/j.ymssp.2006.05.004
  2. W. Sun, J. Chen, J. Li, 'Decision tree and PCA-based fault diagnosis of rotating machinery', Mechanical Systems and Signal Processing, 2006
  3. B. S. Yang, C. H. Park, H. J. Kim, 'An Efficient Method of Vibration Diagnostics for Rotating Machinery using a Decision Tree', International Journal of Rotating Machinery, Vol.6, No.1, pp. 19-27, 2000 https://doi.org/10.1155/S1023621X00000038
  4. D. S. Lim, B. S. Yang, D. J. Kim, 'An Expert System for Vibration Diagnosis of Rotating Machinery using Decision Tree', International Journal of COMADEM, pp.31-36, 2000
  5. D. S. Lim, B. S. Yang, D. J. Kim, 'An Expert System for Vibration Diagnosis of Rotating Machinery using Decision Tree', International Journal of COMADEM, pp.31-36, 2000
  6. A. Widodo, B. S. Yang, T. Han, 'Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors', Expert Systems with Applications 32, pp. 299-312, 2007 https://doi.org/10.1016/j.eswa.2005.11.031
  7. J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publisher, Inc., 1993
  8. B. Samanta, K. R. Al-Balushi, 'Artificial Neural Network based fault diagnostics of rolling element bearings using time-domain features', Mechanical Systems and Signal Processing 17, pp. 317-328, 2003 https://doi.org/10.1006/mssp.2001.1462
  9. H. Gu, Z. Gao, F. Wu, 'Selection of Optimal Features for Iris Recognition', in International Symposium on Neural Networks, China, pp. 81-86, 2005
  10. B. Samanta, K. R. Al-Balushi, S. A. Al-Araimi, 'Artificial neural networks and genetic algorithm for bearing fault detection', Soft Coput., pp. 264-271, 2006
  11. T. Lindh, J. Ahola, P. Spatenka, A-L Rautiainen, 'Automatic bearing fault classification combining statistical classification and fuzzy logic', in NORPIE, 2004
  12. J. S. Rao, Vibratory Condition Monitoring of Machines, Alpha Science International Ltd., pp. 361-382, 2000