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http://dx.doi.org/10.5370/JEET.2007.2.3.353

Bearing Fault Diagnosis Using Fuzzy Inference Optimized by Neural Network and Genetic Algorithm  

Lee, Hong-Hee (School of Electrical Engineering, University of Ulsan)
Nguyen, Ngoc-Tu (School of Electrical Engineering, University of Ulsan)
Kwon, Jeong-Min (School of Electrical Engineering, University of Ulsan)
Publication Information
Journal of Electrical Engineering and Technology / v.2, no.3, 2007 , pp. 353-357 More about this Journal
Abstract
The bearing diagnostics method is presented in this paper using fuzzy inference based on vibration data. Both time-domain and frequency-domain features are used as input data for bearing fault detection. The Adaptive Network based Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) have been proposed to select the fuzzy model input and output parameters. Training results give the optimized fuzzy inference system for bearing diagnosis based on measured vibration data. The result is also tested with other sets of bearing data to illustrate the reliability of the chosen model.
Keywords
Bearing diagnosis; Fuzzy; Genetic algorithm; Neural network; Vibration;
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1 Bo Li, Yodyium Tipsuwan, James C. Hung, 'Neural-Network-Based Motor Rolling Bearing Fault Diagnosis', IEEE Transactions on Industrial Electronics, Vol. 47, No. 5, 2000
2 Abhinav Saxena, Ashraf Saad, 'Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems', Applied Soft Computing, 2006
3 B. Samanta, K. R. Al-Balushi, S. A. Al-Araimi, 'Artificial neural networks and genetic algorithm for bearing fault detection', Soft Comput, vol. 10, 264-271, 2006   DOI
4 Xinsheng Lou, Kenneth A. Loparo, 'Bearing fault diagnosis based on wavelet transform and fuzzy inference', Mechanical Systems and Signal Processing, vol. 18, 1077-1095, 2004   DOI   ScienceOn
5 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, vol. 17, 317-328, 2003   DOI   ScienceOn
6 Changzheng Chen, Changtao Mo, 'A method for intelligent fault diagnosis of rotating machinery', Digital Signal Processing, vol. 14, 203-217, 2004   DOI   ScienceOn
7 Z. Ye, A. Sadeghian, B. Wu, 'Mechanical fault diagnostics for induction motor with variable speed drives using Adaptive Neuro-fuzzy Inference System', Electric Power Systems Research, vol. 76, 742-752, 2006   DOI   ScienceOn
8 Gregory Goddu, Bo Li, Mo-Yuen Chow, James C. Hung, 'Motor Bearing Fault Diagnosis by a Fundamental Frequency Amplitude Based Fuzzy Decision System', in IECON'98 Proceedings of the 24th Annual Conference of the IEEE, Volume 4, 1961-1965, 1998
9 Jyh-Shing, Roger Jang, 'ANFIS: Adaptive-Network-Based Fuzzy Inference System', IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23 No. 3, 1993
10 Branimir B. Jovanovic, Irini S. Reljin, Branimir D. Reljin, 'Modified ANFIS Architecture - Improving Efficiency of ANFIS Technique', 7th Seminar on Neural Network Applications in Electrical Engineering, NEUREL-2004
11 Cornelius T. Leondes, Fuzzy Theory Systems: Techniques and Applications, Academic Press, 1999, Volume 1, 205-221
12 Keith A. Woodbury, 'Application of Genetic Algorithms and Neural Networks to the Solution of Inverse Heat Conduction Problems', A Tutorial
13 J. S. Rao, Vibratory Condition Monitoring of Machines, Alpha Science International Ltd., 2000