Immune model-based fault diagnosis

Mathematics and Computers in Simulation

Volumn 67, Issue 6, 2005, Pages 515-539

  Luh, Guan Chun ^a   Cheng, Wei Chong ^a  

^a TATUNG UNIVERSITY   (Taiwan)

Author keywords

Artificial immune regulation; Fault alarm concentration; Fault detection and isolation; Immune model; Model based fault diagnosis

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATA PROCESSING; NONLINEAR SYSTEMS; SENSORS;

ARTIFICIAL IMMUNE REGULATION; FAULT ALARM CONCENTRATION; IMMUNE MODELS;

FAULT TOLERANT COMPUTER SYSTEMS;

EID: 10644295517     PISSN: 03784754     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.matcom.2004.07.004     Document Type: Article

References (49)

1. P.M. Frank, On-line fault detection in uncertain nonlinear systems using diagnostic observers-a survey, Int. J. Syst. Sci. 25 (1994) 2129-2154.
2. P.M. Frank, B. Koppen-Seliger, New developments using AI in fault diagnosis, Eng. Appl. Artif. Intell. 10 (1) (1997) 3-14.
3. J.J. Gertler, Survey of model-based failure detection and isolation in complex plants, IEEE Control Syst. Mag. 8 (6) (1988) 3-11.
4. J. Chen, R. J. Patton, Robust Model-based Fault Diagnosis for Dynamic Systems, Kluwer Academic Publishers, Dordrecht, 1999.
5. J.J. Gertler, Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker Inc, New York, 1998.
6. R.J. Patton, P.M. Frank, R.N. Clark, Fault Diagnosis in Dynamic Systems: Theory and Applications, Prentice Hall, 1989.
7. V. Krishnaswami, G.C. Luh, G. Rizzoni, Nonlinear parity equation based residual generation for diagnosis of automotive engine fault, Control Eng. Pract. 3 (10) (1995) 1385-1392.
8. V. Puig, J. Quevedo, Passive robust fault detection using fuzzy parity equations, Math. Comput. Simul. 60 (3/5) (2002) 193-207.
9. A. Medvedev, Fault detection and isolation by a continuous parity space method, Automatica 31 (1995) 1039-1044.
10. H. Schneider, P.M. Frank, Observer-based supervision and fault detection in robots using nonlinear and fuzzy logic residual evaluation, IEEE Trans. Control Syst. Technol. 4 (3) (1996) 274-282.
11. R.J. Patton, J. Chen, Observer-based fault detection and isolation: robustness and applications, Control Eng. Pract. 5 (5) (1996) 671-682.
12. P. Balle, Fuzzy-model-based parity equations for fault isolation, Control Eng. Pract. 7 (2) (1999) 261-270.
13. P.M. Frank, Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy -a survey of some new results, Automatica 26 (1990) 459-474.
14. Y.M. Chen, M.L. Lee, Neural networks-based scheme for system failure detection and diagnosis, Math. Comput. Simul. 58 (2002) 101-109.
15. P. Thomas, D. Lefebvre, Fault detection and isolation in non-linear systems by using oversized neural networks, Math. Comput. Simul. 60 (2002) 181-192.
16. M. Ronen, Y. Shabtai, H. Guterman, Hybrid model building methodology using unsupervised fuzzy clustering and supervised neural networks, Biotechnol. Bioeng. 77 (4) (2002) 420-429.
17. M.K. Pakhira, S. Bandyopadhyay, U. Maulik, Validity index for crisp and fuzzy clusters, Pattern Recog. 37 (3) (2004) 487-501.
18. T.N. Yang, S.D. Wang, Competitive algorithms for the clustering of noisy data, Fuzzy Sets Syst. 141 (2) (2004) 281-299.
19. [ 19] P. J. Costa Branco, J. A. Dente, R. Vilela Mendes, Using immunology principles for fault detection, IEEE Trans. Ind. Electron. 50 (2) (2003) 362-373.
20. D. Dasgupta, S. Forrest, Novelty detection in time series data using ideas from immunology., in: Proceedings of the International Conference on Intelligent Systems, Reno, 1996, pp. 82-87.
21. F. Gonzalez, D. Dasgupta, R. Kozma, Combining negative selection and classification techniques for anomaly detection, in: Proceedings of the IEEE Congress on Evolutionary Computation, Hawaii, 12-17 May, 2002, pp. 705-710.
22. A. Ishiguro, Y. Watanabe, Y. Uchikawa, in: Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Las Vegas, 2-5 October, Fault diagnosis of plant systems using immune networks. (1994) 34-22.
23. I. Roitt, J. Brostoff, D.K. Male, Immunology, 5th ed., Mosby International Limited, 1998.
24. E. Benjamini, R Coico, G. Sunshine, Immunology: a short course, 4th ed., Wiley-Liss, 2000.
25. D. Dasgupta, Artificial Immune Systems and Their Applications, Springer-Verlag, Berlin, Heidelberg, 1999.
26. L.N. de Castro, F. J. Von Zuben, Artificial immune systems: Part I - basic theory and applications, Technical Report TR-DCA 01/99, 1999.
27. L.N. de Castro, T. Jonathan, Artificial immune systems: a new computational intelligence approach, Springer-Verlag, Berlin, Heidelberg, 2002.
28. G.C. Luh, W.C. Cheng, Non-linear system identification using an artificial immune system, Proc. Inst. Mech. Eng. I: J. Syst. Control Eng. 215 (2001) 569-585.
29. J.E. Hunt, D.E. Cooke, An adaptive distributed learning system based on immune system, in: IEEE International Conference on Systems, Man, and Cybernetics, 22-25 October, 1995, pp. 2494-2499.
30. D. Dasgupta, N. Attoh-Okin, Immunity-based systems: a survey. Computational cybernetics and simulation, in: IEEE International Conference on Systems, Man, and Cybernetics, 12-15 October, 1997, pp. 369-374.
31. N.K. Jerne, The immune system, Sci. Am. 229 (1) (1973) 52-60.
32. R. Hightower, S. Forrest, A.S. Perelson, The evolution of emergent organization in immune system gene libraries, in: Proceedings of Sixth International Conference on Genetic Algorithms, 1995, pp. 344-350.
33. R. Hightower, S. Forrest, A.S. Perelson, The Baldwin effect in the immune system: learning by somatic hypermutation, in: R.K. Belew, M. Mitchell (Eds.), Adaptive Individuals in Evolving Populations, Addison-Wesley, Reading, MA, 1996, pp. 159-167.
34. J. Timmis, M. Neal, J. Hunt, Data analysis using artificial immune systems, cluster analysis and Kohonen networks: some comparisons, in: IEEE International Conference on Systems, Man, and Cybernetics, 1999, pp. 922-927.
35. T. Knight, J. Timmis, AINE: an immunological approach to data mining, in: Proceedings of International Conference on Data Mining, 2001, pp. 297-304.
36. G.C. Luh, W.C. Cheng, Artificial immune network-based nonlinear system identification, in: The 20th National Conference on Mechanical Engineering, The Chinese Society of Mechanical Engineers, 2003, pp. 253-260.
37. P.P. Baldi, B. Soren, Bioinformatics: The Machine Learning Approach, The MIT Press, 1999.
38. J.H. Lin, C.A. Isik, Maximum entropy radial basis function network based neuro-fuzzy controller, in: Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1996, pp. 156-161.
39. M. Fazle Azeem, M. Hanmandlu, N. Ahmad, Structure identification of generalized adaptive neuro-fuzzy inference systems, IEEE Trans. Fuzzy Syst. 11 (5) (2003) 666-681.
40. R.P. Velthuizen, L.O. Hall, L.P. Clarke, M.L. Silbiger, An investigation of mountain method clustering for large data sets, Pattern Recog. 30 (7) (1997) 1121-1135.
41. S. Barada, H. Singh, Generating optimal adaptive fuzzy-neural models of dynamical systems with applications to control, IEEE Trans. Syst. Man Cybern. C 28 (3) (1998) 371-391.
42. T. Kohonen, Self-organization and Associative Memory, Springer-verlag, Berlin, 1984.
43. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
44. J. Yaochu, Decentralized adaptive fuzzy control of robot manipulators, IEEE Trans. Syst. Man Cybern. B 28 (1) (1998) 47-57.
45. W. Li, S. Shah, Structured residual vector-based approach to sensor fault detection and isolation, J. Process Control 12 (3) (2002) 429-443.
46. I.J. Leontaritis, S.A. Billings, Input-output parametric models for non-linear systems. I. Deterministic non-linear systems, Int. J. Control. 41 (2) (1985) 303-328.
47. I.J. Leontaritis, S.A. Billings, Input-Output parametric models for non-linear systems. II. Stochastic non-linear systems, Int. J. Control. 41 (2) (1985) 329-344.
48. K.S. Narendra, K. Parthasarathy, Identification and control of dynamical systems using neural networks, IEEE Trans. Neural Network 1(1) (1990) 4-27.
49. G.C. Luh, C. Y. Wu, Inversion control of non-linear systems with an inverse NARX model identified using genetic algorithms, Proc. Inst. Mech. Eng. I: J. Syst. Control Eng. 214 (2000) 259-271.