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A Fuzzy Neural Network Fault Diagnostic System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 94 - Number 1 |
| Year of Publication: 2014 |
| Authors: Mohamed, A. H |
10.5120/16305-5531
|
Mohamed, A. H . A Fuzzy Neural Network Fault Diagnostic System. International Journal of Computer Applications. 94, 1 ( May 2014), 9-13. DOI=10.5120/16305-5531
Abstract
Fault diagnosis of the modern complex devices is one of the most important tasks in many application areas. Recently, it is found that, neural networks and fuzzy logic control have widely used for the diagnostic devices. Fuzzy logic systems provide high processing speed but lower computing power. While, the neural networks can achieve high computing power but slower convergence rate. Therefore, the proposed system introduces the fuzzy neural network fault diagnostic system for diagnosis the complex devices. The integration between the fuzzy logic and the neural network enables the diagnostic system to have the advantages of both of these techniques and overcome the limitations of each of them. The suggested system has been applied for diagnosis a production line of radiated surgery tools as a non-linear complex faulty system. The obtained results show that the proposed diagnostic system can diagnose the complex devices with more accurate and speedy results rather than the traditional diagnostic system. Therefore, it can be applied for the practical applications effectively.
References
- Wong K. , Fung, C. and Myers D. (2002). An Integrated Neural Fuzzy Approach with Reduced Rules for Well Log Analysis. International Journal of Fuzzy Systems, Vol. 4, No. 1, pp. 592-603.
- Wilamowski, B. M. (2002). Neural Networks and Fuzzy Systems. Mechatronics Handbook edited by Robert R. Bishop, CRC Press, pp. 1-50.
- Jun, G. , (2005). Artificial Neural Network Theory and Simulation. Mechanical Industry Press, Beijing, pp. 44-68.
- Huynh, H. T. and Won, Y. (2011). Online Training for Single Hidden-Layer Feed-Forward Neural Networks using RLS-ELM. IEEE International Symposium on Computational Intelligence in Robotics and Automation. International Journal of Scientific & Engineering Research. Vol. 2, No. 10, pp. 469-478.
- Wiswedel, B. and Berthold, M. (2007). Fuzzy Clustering in Parallel Universes, International Journal of Approximate Reasoning. Vol. 45, No. 3, pp. 439-448.
- Huifang, K. ; Ren Guoqing; Jizhu, H. and Benxian, X. (2011). The Application of Fuzzy Neural Network in Fault Self-diagnosis System of Automatic Transmission. Journal of Software, Vol. 6, No. 2, pp. 209-212.
- Stathacopoulou R. ; Magoulas G. D. ; Grigoriadou, M. and Samarakou, M. (2011): Neural-Fuzzy Knowledge Processing in Intelligent Learning Interactive Neuro-Fuzzy Expert System for Diagnosis of Leukemiam Global, Journals Inc. pp. 112-130.
- Wang, W. ; Ismail, F. and Golnaraghi, F. (2004): A Neuro-Fuzzy Approach to Gear System Monitoring, IEEE Transactions on Fuzzy Systems, Vol. 12, No. 5, pp. 710-723.
- Kar, S. ; Das, S. ; Ghosh, P. K. (2014): Applications of Neuro-Fuzzy Systems: A Brief Review and Future Outline, Applied Soft Computing, Vol. 15, pp. 243-259.
- Tian, J. ; Gao, M. ; Cao, L. and Li, K. (2007): Fault detection of oil Pump Based on Fuzzy Neural Network. In Third International Conference on Natural Computation. Vol. 2, pp. 636-640.
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