Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing

Mechanical Systems and Signal Processing

Volumn 21, Issue 5, 2007, Pages 2237-2247

  Sugumaran, V ^a   Ramachandran, K I ^a  

^a AMRITA SCHOOL OF ENGINEERING   (India)

Author keywords

Decision tree; Fault detection; Feature selection; Fuzzy; Roller bearing; Rule learning

Indexed keywords

AUTOMATION; DECISION TREES; FAULT DETECTION; FEATURE EXTRACTION; ROLLER BEARINGS; STATISTICAL METHODS;

FAULT CONDITIONS; FUZZY CLASSIFIERS; RULE LEARNING; STATISTICAL FEATURES;

FUZZY CLUSTERING;

EID: 34047251878     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2006.09.007     Document Type: Article

References (19)

1. S.S. Wang, Artificial intelligence and expert systems for diagnostics, Proceedings of the Institute of International Conference Machining and Diagnostics and Exhibition, Las Vegas, Neveda, 1989. pp. 516-512.
2. In: Winder R.L., and Littmann W.E. (Eds). Bearing Damage Analysis (1976), National Bureau of Standard Publication
3. James Li C., and Wu S.M. Online detection of localised defects in bearing by pattern recognition analysis. ASME Journal of Engineering Industry 111 (1989) 331-336
4. A.K. Nandi, Advanced digital vibration signal processing for condition monitoring, Proceedings of COMADEM2000, Houston, TX, USA, 2000. pp. 129-143.
5. Samanta B., and Al-Baulshi K.R. Artificial neural network based fault diagnostics of rolling element bearings using time domain features. Mechanical Systems and Signal Processing 17 2 (2003) 317-328
6. Samanta B., Al-Baulshi K.R., and Al-Araimi S.A. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence 16 2003 (2003) 657-665
7. Burgess C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 (1998) 955-974
8. L.B. Jack, A.K. Nandi, Comparison of neural networks and support vector machines in condition monitoring application, Proceedings of COMADEM 2000, Houston, TX, USA, 2000a. pp. 721-730.
9. X.Z. Shi, Z.Q. Xu, Xu, A study on the automatic recognition of vibration signal for ball bearing faults-the FFTAR feature extraction and classification methods, Proceedings of theIEEE International Workshop on Applied Time series Analysis, World Scientific, Singapore. 1988. pp. 318-321.
10. Hongyu Y., Joseph M., and Lin M. Fault diagnosis of rolling element bearings using basis pursuit. Mechanical Systems and Signal Processing 19 (2005) 341-356
11. McFadden P.D., and Smith J.D. Vibration monitoring of rolling element bearings by high frequency resonance technique-a review. Tribology International 17 (1984) 3-10
12. Y.H. Peng, P.A. Flach, P. Brazdil, C. Soares, Decision tree-based data characterization for meta-learning, ECML/PKDD-2002 Workshop IDDM -2002, Helsinki, Finland.
13. Quinlan J.R. Improved use of continuous attributes in C4.5. Journal of Artificial Research 4 (1996) 77-90
14. Cox E. The Fuzzy Systems Handbook-A Practitioner's Guide to Building, Using, and Maintaining Fuzzy Systems (1994), Academic Press, New York
15. Zeng L., and Wang Z. The International Journal of Advanced Manufacturing Technology 6 (1991) 83-94 Machine-fault classification: a fuzzy approach
16. Y.C. Huang, H.T. Yang, C.L. Huang, IEEE Transactions on Power Delivery 12. Developing a new transformer fault diagnosis system through evolutionary fuzzy logic. 1997.
17. Rao B.K.N. The Handbook of Condition Monitoring (1996), Elsevier, London
18. Andrews R., Diederich J., and Tickle A. A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems 8 6 (1995) 373-389
19. Haydemar Núñez, Cecilio Angulo, Andreu Català, Rule extraction from support vector machines, ESANN'2002 proceedings-European Symposium on Artificial Neural Networks, pp. 107-112.