ADAPTIVE DYNAMIC CLONE SELECTION NEURAL NETWORK ALGORITHM FOR MOTOR FAULT DIAGNOSIS

Publications

Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

GET ALERTS

eISSN: 1178-5608

DESCRIPTION

1
Reader(s)
6
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 6 , ISSUE 2 (April 2013) > List of articles

ADAPTIVE DYNAMIC CLONE SELECTION NEURAL NETWORK ALGORITHM FOR MOTOR FAULT DIAGNOSIS

Wu Hongbing * / Lou Peihuang / Tang Dunbing / Wu Hongbing * / Lou Peihuang / Tang Dunbing

Keywords : Adaptive, neural network, fault diagnosis, motor, clone selection algorithm

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 6, Issue 2, Pages 482-504, DOI: https://doi.org/10.21307/ijssis-2017-551

License : (CC BY-NC-ND 4.0)

Received Date : 14-January-2013 / Accepted: 16-March-2013 / Published Online: 10-April-2013

ARTICLE

ABSTRACT

A fault diagnosis method based on adaptive dynamic clone selection neural network (ADCSNN) is proposed in this paper. In this method the weights of neural network is encoded as the antibody, and the network error is considered as the antigen. The algorithm is then applied to fault detection of motor equipment. The experiments results show that the fault diagnosis method based on ADCS neural network has the capability in escaping local minimum and improving the algorithm speed, this gives better performance.

Content not available PDF Share

FIGURES & TABLES

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): 719-729
[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): 1847-1855
[3] Denker, J.S, Neural Network Models of Learning and Adaptation, Physica 22D, 1986.
[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.
[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.
[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.
[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.
[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] 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.
[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.
[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.
[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.
[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 2012
[16] Farmer J D, Packard N H, Perelson A A. The immune system adaptation and machine learning. Physica, 1986, 22D:187-204.
[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): 304-316.
[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.
[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 2011
[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):247-254
[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.
[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.
[26] Thomson, W. T., & Fenger, M. Current signature analysis to detect induction motor faults. IEEE Industrial Applications Magazine, 2001, 7, 26-34.
[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.

EXTRA FILES

COMMENTS