A study of negative selection algorithm-based motor fault detection and diagnosis

International Journal of Innovative Computing, Information and Control

Volumn 9, Issue 2, 2013, Pages 875-901

  Gao, Xiao Zhi ^a   Xu, He ^b   Wang, Xiaolei ^a   Zenger, Kai ^a  

^a AALTO UNIVERSITY   (Finland)

^b HARBIN ENGINEERING UNIVERSITY   (China)

Author keywords

Artificial immune systems (AIS); Electrical machines; Fault detection; Fault diagnosis; Mobile robots; Motors; Negative selection algorithm (NSA)

Indexed keywords

ARTIFICIAL IMMUNE SYSTEM; ELECTRICAL MACHINE; MOTOR FAULT; MOTOR FAULT DETECTION AND DIAGNOSIS; NEGATIVE SELECTION; NEGATIVE SELECTION ALGORITHM; PRIOR KNOWLEDGE; REAL-WORLD;

COMPUTER SIMULATION; ELECTRIC MACHINERY; FAILURE ANALYSIS; MOBILE ROBOTS; MOTORS;

FAULT DETECTION;

EID: 84877822074     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (27)

1. C. A. Janeway, P. Travers, M. Walport and M. Shlomchik, Immunobiology: The Immune System in Health and Disease, 5th Edition, Garland Publishing, New York, 2001.
2. L. N. de. Castro and J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach, Springer-Verlag, London, 2002.
3. D. Dasgupta, Advances in artificial immune systems, IEEE Computational Intelligence Magazine, vol.1, no.4, pp.40-49, 2006.
4. E.Ulker, NURBS curve fitting using artificial immune system, International Journal of Innovative Computing, Information and Control, vol.8, no.4, pp.2875-2888, 2012.
5. S. Forrest, A. S. Perelson, L. Allen and R. Cherukuri, Self-nonself discrimination in a computer, Proc. of the IEEE Symposium on Research in Security and Privacy, Los Alamos, CA, USA, pp.202-212, 1994.
6. D. Dasgupta and F. Gonzalez, An immunity-based technique to characterize intrusions in computer networks, IEEE Transactions on Evolutionary Computation, vol.6, no.3, pp.281-291, 2002.
7. D. Dasgupta and S. Forrest, Tool breakage detection in milling operations using a negative selection algorithm, Technical Report CS95-5, Department of Computer Science, University of New Mexico, 1995.
8. D. Dasgupta, K. Krishnakumar, D. Wong and M. Berry, Negative selection algorithm for aircraft fault detection, Proc. of the 3rd International Conference on Artificial Immune Systems, Catania, Sicily, Italy, pp.1-13, 2004.
9. Z. Ji and D. Dasgupta, Revisiting negative selection algorithm, Evolutionary Computation, vol.15, no.2, pp.223-251, 2007.
10. F.Gonzalez, A Study of Artificial Immune Systems Applied to Anomaly Detection, Ph.D. Thesis, Division of Computer Science, University of Memphis, Memphis, 2003.
11. Z. Ji and D. Dasgupta, Real-valued negative selection algorithm with variable-sized detectors, Proc. of Conference on Genetic and Evolutionary Computation, Seattle, WA, USA, pp.287-298, 2004.
12. Z. Ji, Negative Selection Algorithms: From the Thymus to V-Detectors, Ph.D. Thesis, University of Memphis, 2006.
13. T. Stibor, J. Timmis and C. Eckert, A comparative study of real-valued negative selection to statistical anomaly detection techniques, Proc. of the International Conference on Artificial Immune Systems, Banff, AL, Canada, pp.262-275, 2005.
14. S. Nandi, H. A. Toliyat and X. Li, Condition monitoring and fault diagnosis of electrical motors -A review, IEEE Transactions on Energy Conversion, vol.20, no.4, pp.719-729, 2005.
15. D. Basak, A. Tiwari and S. P. Das, Fault diagnosis and condition monitoring of electrical machines - A review, Proc. of the IEEE International Conference on Industrial Technology, Mumbai, India, pp.3061-3066, 2006.
16. Y. L. Murphey, M. A. Masrur, Z. Chen and B. Zhang, Model-based fault diagnosis in electric drives using machine learning, IEEE/ASME Transactions on Mechatronics, vol.11, no.3, pp.290-303, 2006.
17. S. Kim and H. Bae, Real-time anomaly detection for business process monitoring, ICIC Express Letters, vol.6, no.4, pp.1109-1114, 2012.
18. X. Z. Gao, S. J. Ovaska, X. Wang and M.-Y. Chow, A neural networks-based negative selection algorithm in fault diagnosis, Neural Computing and Applications, vol.17, no.l, pp.91-98, 2008.
19. X. Z. Gao, S. J. Ovaska and X. Wang, A GA-based negative selection algorithm, International Journal of Innovative Computing, Information and Control, vol.4, no.4, pp.971-979, 2008.
20. X. Z. Gao, S. J. Ovaska, X. Wang and M.-Y. Chow, Clonal optimization-based negative selection algorithm with applications in motor fault detection, Neural Computing and Applications, vol.18, no.7, pp.719-729, 2009.
21. X. Z. Gao, S. J. Ovaska, X. Wang and M.-Y. Chow, Multi-level optimization of negative selection algorithm detectors with application in motor fault detection, International Journal of Intelligent Automation and Soft Computing, vol.15, no.3, pp.353-375, 2010.
22. C. Liu, X. Wei and J. Yang, A path planning method based on improved genetic algorithm for mobile robot in static environment, ICIC Express Letters, vol.5, no.12, pp.4283-4290, 2011.
23. H. Xu, Z. Zhang, D. Tan, X. Z. Gao, G. Peng and S. Yu, Optimization of mobile robot based on projection method and harmony search, Proc. of the 2008 IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand, pp.1653-1658, 2009.
24. H. Xu, Z. Zhang, K. Alipour, K. Xue and X. Z. Gao, Prototypes selection by multi-objective optimal design: Application to a reconfigurable robot in sandy terrain, Industrial Robot, vol.38, no.6, pp.599-613, 2011.
25. R. A. Fisher, The use of multiple measurements in taxonomic problems, Annals of Eugenics, vol.7, pp.179-188, 1936.
26. Fisher's Iris Data, http://ftp.ics.uci.edu/pub/machine-learning-databases/iris/.
27. B. Li, M.-Y. Chow, Y. Tipsuwan and J. C. Hung, Neural-network-based motor rolling bearing fault diagnosis, IEEE Transactions on Industrial Electronics, vol.47, no.5, pp.1060-1069, 2000.