A neural networks-based negative selection algorithm in fault diagnosis

Neural Computing and Applications

Volumn 17, Issue 1, 2008, Pages 91-98

  Gao, X Z ^a   Ovaska, S J ^a   Wang, X ^a   Chow, M Y ^b  

^a AALTO UNIVERSITY   (Finland)

^b North Carolina State University   (United States)

Author keywords

Anomaly detection; Artificial immune systems; Fault diagnosis; Negative selection algorithm; Neural networks

Indexed keywords

EID: 36549068363     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-007-0092-z     Document Type: Article

References (19)

1. de Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence approach. Springer, London
2. Dasgupta D, Attoh-Okine N (1997) Immunity-based systems: a survey. In: Proceedings of the IEEE international conference on systems, man, and cybernetics, Orlando, FL, pp 369-374
3. Garrett SM (2005) How do we evaluate artificial immune systems?. Evol Comput 13(2):145-178
4. Dasgupta D (2006) Advances in artificial immune systems. IEEE Comput Intell Mag 1(4):40-49
5. Forrest S, Perelson AS, Allen L, Cherukuri R (1994) Self-nonself discrimination in a computer. In: Proceedings of the IEEE symposium on research in security and privacy, Los Alamos, CA, pp 202-212
6. Stibor T, Mohr P, Timmis J, Eckert C (2005) Is negative selection appropriate for anomaly detection? In: Proceedings of the genetic and evolutionary computation conference, Washington D.C., pp 321-328
7. Dasgupta D, González F (2002) An immunity-based technique to characterize intrusions in computer networks. IEEE Trans Evol Comput 6(3):281-291
8. Dasgupta D, Forrest S (1995) Tool breakage detection in milling operations using a negative selection algorithm. Technical Report CS95-5, Department of Computer Science, University of New Mexico, Albuquerque, NM
9. Dasgupta D, KrishnaKumar K, Wong D, Berry M (2004) Negative selection algorithm for aircraft fault detection. In: Proceedings of the 3rd international conference on artificial immune systems, Catania, Sicily, Italy, pp 1-13
10. Ji Z, Dasgupta D (2006) Applicability issues of the real-valued negative selection algorithms. In: Proceedings of the genetic and evolutionary computation conference, Seattle, WA, pp 111-118
11. González F (2003) A study of artificial immune systems applied to anomaly detection. Ph.D. Dissertation, Division of Computer Science, University of Memphis, Memphis, TN
12. González F, Dasgupta D, Nino LF (2003) A randomized real-value negative selection algorithm. In: Proceedings of the 2nd international conference on artificial immune systems, Edinburgh, UK, pp 261-272
13. Stibor T, Timmis J, Eckert C (2005) A comparative study of real-valued negative selection to statistical anomaly detection techniques. In: Proceedings of the 4th international conference on artificial immune systems, Banff, Alberta, Canada, pp 262-275
14. Haykin S (1998) Neural networks, a comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle River
15. Frank PM (1990) Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy-a survey and some new results. Automatica 26(3):459-474
16. Chow MY (1997) Methodologies of using neural network and fuzzy logic technologies for motor incipient fault detection. World Scientific Publishing, Singapore
17. Mackey MC, Glass L (1977) Oscillation and chaos in physiological control systems. Science 197:287-289
18. Li B, Chow MY, Tipsuwan Y, Hung JC (2000) Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans Ind Electron 47(5):1060-1069
19. Gao XZ, Ovaska SJ, Wang X, Chow MY (2006) Clonal optimization of negative selection algorithm with applications in motor fault detection. In: Proceedings of the IEEE international conference on systems, man, and cybernetics, Taipei, Taiwan, pp 5118-5123