Have a personal or library account? Click to login

Adaptive Dynamic Clone Selection Neural Network Algorithm for Motor Fault Diagnosis

Open Access
|Apr 2013

References

  1. Nandi S, Toliyat H.A, LI X. Condition monitoring and fault diagnosis of electrical motors–a review [J]. IEEE Trans. on Energy Conversion, 2005, 20(4): 719-72910.1109/TEC.2005.847955
  2. Diallo D, Benbouzid M.E.H, Makouf A, A fault tolerant control architecture for induction motor drives in automotive applications [J]. IEEE Trans. on Vehicular Technology, 2004, 53(6): 1847-185510.1109/TVT.2004.833610
  3. Denker, J.S, Neural Network Models of Learning and Adaptation, Physica 22D, 1986.10.1016/0167-2789(86)90242-3
  4. Lu, H., Setiono, R, and Liu, H, “Effective Data Mining Using Neural Networks”, IEEE Trans. On Knowledge and Data Engineering, 1996, 8(6), pp. 957-961.10.1109/69.553163
  5. Hopfield J J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl.Acad. Sci. USA, 1982, 79(8): 2554-2558.
  6. Rumelhart D E, McClell J L. Parallel Distributed Processing, Vol. 1-2. Cambridge, MA, USA: MIT Press, 1986.10.7551/mitpress/5236.001.0001
  7. Dong Mingchui, Cheng Takson, Chan Sileong. On-line fast motor fault diagnostics based on fuzzy neural networks.Tsinghua Science and Technology, 2009, 14(2): 225-233.10.1016/S1007-0214(09)70034-3
  8. Psillakis H E. Further results on the use of Nussbaum gains in adaptive neural network control. IEEE Transactions on Automatic Control, 2010, 55(12): 2841-2846.10.1109/TAC.2010.2078070
  9. Sun Ming, Zhao Lin, Cao Wei, et al. Novel hysteretic noisy chaotic neural network for broadcast scheduling problems in packet radio networks. IEEE Transactions on Neural Networks, 2010, 21(9): 1422-1433.10.1109/TNN.2010.2059041
  10. Liang Yao, Liang Xu. Improving signal prediction performance of neural networks through multi resolution learning approach. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2006, 36(2):341-352.10.1109/TSMCB.2005.857092
  11. Guo W W, Li M, Li Z, et al. Approximating nonlinear relations between susceptibility and magnetic contents in rocks using neural networks. Tsinghua Science and Technology, 2010, 15(3): 281-287.10.1016/S1007-0214(10)70062-6
  12. Khomfoi S, Tolbert L M. Fault diagnostic system for a multilevel inverter using a neural network. IEEE Transactions on Power Electronics, 2007, 22(3): 1062-1069.10.1109/TPEL.2007.897128
  13. Gui Liang Yin and Li Ping Xiao, “Squirrel-Cage Motors Fault Diagnosis Using Immunology Principles”, Proceedings of the Chinese Society for Electrical Engineering, Beijing, 2003, 6, pp. 132-136.
  14. Dasgupta, D., “An Overview of Artificial Immune System and Their Applications”, Artificial Immune System and Their Applications, Spring-Verlag, 1998b, pp. 3-21.10.1007/978-3-642-59901-9_1
  15. Mehdi Neshat, Ali Adeli. A Review of artificial fish swarm optimization methods and applications. INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS, VOL. 5, NO. 1, MARCH 201210.21307/ijssis-2017-474
  16. Farmer J D, Packard N H, Perelson A A. The immune system adaptation and machine learning. Physica, 1986, 22D:187-204.10.1016/0167-2789(86)90240-X
  17. Castro P A D, Von Zuben F J. Learning ensembles of neural networks by means of a Bayesian artificial immune system. IEEE Transactions on Neural Networks, 2011, 22(2): 304316.
  18. Yuan H C, Xiong F L, Huai X Y. A method for estimating the number of hidden neurons in feed-forward neural networks based on information entropy. Computers and Electronics in Agriculture, 2003, 40(1-3): 57-64.10.1016/S0168-1699(03)00011-5
  19. A. F. Salami, H. Bello-Salau. A novel biased energy distribution (BED) technique for cluster-based routing in wireless sensor networks [J]. International journal on smart sensing and intelligent systems vol. 4, NO. 2, June 201110.21307/ijssis-2017-433
  20. Chun J S, Lim J P, Jung H K. Optimal design of synchronous motor with parameter correction using immune algorithm. IEEE Trans. on Energy Conversion, 1999, 14(3):610-615.
  21. De Castro L N, von Zuben F J. The clonal selection algorithm with engineering applications. In: Whitley L D, Goldberg D E, et al, eds. Proc. of the GECCO 2000. San Fransisco: Morgan Kaufman Publishers, 2000. 36-37
  22. GONG Mao-Guo, HAO Lin. Data reduction based on artificial immune system [J]. Journal of Software, Vol.20, No.4, April 2009, pp.804-814 (in Chinese)
  23. Surya S,Mack G W,Powers E J et al. Characterization of distribution power quality events with flourier and wavelet transforms [J].IEEE Transactions on Power Delivery,2000,15 (1):247-25410.1109/61.847259
  24. M. G. Gong, H. F. Du, and L. C. Jiao, “Optimal approximation of linear systems by artificial immune response,” Sci. China Series F: Inf. Sci., vol. 49, no. 1, pp. 63–79, Jan. 2006.10.1007/s11432-005-0314-x
  25. Jiao Licheng, Li Yangyang, Gong Maoguo, et al. Quantum-inspired immune clonal algorithm for global optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2008, 38(5): 1234-1253.10.1109/TSMCB.2008.92727118784009
  26. Thomson, W. T., & Fenger, M. Current signature analysis to detect induction motor faults. IEEE Industrial Applications Magazine, 2001, 7, 26-34.10.1109/2943.930988
  27. Aydin, I., et al. A multi-objective artificial immune algorithm for parameter optimization in support vector machine. Applied Soft Computing Journal. doi:10.1016/j.asoc.2009.11.003.10.1016/j.asoc.2009.11.003
Language: English
Page range: 482 - 504
Submitted on: Jan 14, 2013
Accepted on: Mar 16, 2013
Published on: Apr 10, 2013
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2013 Wu Hongbing, Lou Peihuang, Tang Dunbing, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.