A Study on Machine Fault Diagnosis using Decision Tree

  • Nguyen, Ngoc-Tu ;
  • Kwon, Jeong-Min ;
  • Lee, Hong-Hee
  • Published : 2007.12.31


The paper describes a way to diagnose machine condition based on the expert system. In this paper, an expert system-decision tree is built and experimented to diagnose and to detect machine defects. The main objective of this study is to provide a simple way to monitor machine status by synthesizing the knowledge and experiences on the diagnostic case histories of the rotating machinery. A traditional decision tree has been constructed using vibration-based inputs. Some case studies are provided to illustrate the application and advantages of the decision tree system for machine fault diagnosis.


Decision tree;Expert system;Fault diagnosis;Machine;Vibration


  1. W. T. Thomson, R. J. Gilmore, 'Motor current signature analysis to detect faults in induction motor drives-Fundamentals, data interpretation, and Industrial case histories', Proceeding of the Thirty-second Turbomachinery Symposium, 2003
  2. W. R. Finley, M. M. Hodowanec, W. G. Holter, 'An Analytical Approach to Solving Motor Vibration Problems', IEEE, 1999
  3. Chun Siu, Quiang Shen, Robert Milne, 'A Fuzzy Expert System for Vibration Cause Identification in Rotating Machines', IEEE, 1997
  4. G. K. Singh, Sa'ad Ahmed Saleh Al Kazzaz, 'Induction machine drive condition monitoring and diagnostic research-a survey', Electric Power Systems Research 64, pp. 145-158, 2003
  5. J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publisher, Inc., 1993
  6. A. Dimarogonas, Sam Haddad, Vibration for Engineers, Prentice Hall, Inc., pp. 675-705, 1992
  7. Bo Suk Yang, Chul Hyun Park, Ho Jong 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
  8. Dong Soo Lim, Bo Suk Yang, Dong Jo Kim, 'An Expert System for Vibration Diagnosis of Rotating Machinery using Decision Tree', International Journal of COMADEM, pp. 31- 36, 2000
  9. J. S. Rao, Vibratory Condition Monitoring of Machines, Alpha Science International Ltd., 2000
  10. C. Olaru, L. Wehenkel, 'A Complete Fuzzy Decision Tree Technique', Fuzzy Sets and Systems 138, pp. 221-254, 2003
  11. K. Crockett, Z. Bandar, D. Mclean, J. O'Shea, 'On constructing a fuzzy inference framework using crisp decision trees', Fuzzy Sets and Systems, 2006
  12. Koen-Myung Lee, Kyung-Mi Lee, Jee-Hyong Lee, Hyung Lee-Kwang, 'A Fuzzy Decision Tree Induction Method for Fuzzy Data', IEEE International Fuzzy Systems Conference Proceedings, 1999
  13. Bo Suk Yang, Dong Soo Lim, Andy Chit Chiow Tan, 'VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table', Expert Systems with Applications 28, pp. 735-742, 2005

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