Identification of immune models for fault detection

Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering

Volumn 218, Issue 5, 2004, Pages 353-367

  Luh, G C ^a   Cheng, W C ^a  

^a TATUNG UNIVERSITY   (Taiwan)

Author keywords

Fault detection; Immune model; Non linear system identification; Orthogonal least squares method

Indexed keywords

COMPUTER SIMULATION; FRICTION; IDENTIFICATION (CONTROL SYSTEMS); LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; ROBOTICS; VECTORS;

EPITOPE VECTORS; FAULT DETECTION; IMMUNE MODELS; NON-LINEAR SYSTEM IDENTIFICATION;

NONLINEAR SYSTEMS;

EID: 4043131766     PISSN: 09596518     EISSN: None     Source Type: Journal    
DOI: 10.1243/0959651041568524     Document Type: Conference Paper

References (54)

1. Basseville, M. Detecting changes in signals and systems -a survey. Automatica, 1988, 24(3), 309-326.
2. Frank, P. M. On-line fault detection in uncertain nonlinear systems using diagnostic observers - a survey. Int. J. Systems Sci., 1994, 25, 2129-2154.
3. Isermann, R. Process fault detection based on modeling and estimation methods: a survey. Automatica, 1984, 20(4), 387-404.
4. Willsky, A. S. A survey of design methods for failure detection in dynamic systems. Automatica, 1976, 12, 601-611.
5. Gertler, J. J. Survey of model-based failure detection and isolation in complex plants. IEEE Control System Mag., 1988, 8(6), 3-11.
6. Patton, R. J. and Chen, J. A survey of robustness in quantitative model-based fault diagnosis. Appl. Math. Computer. Sci., 1993, 3(3), 399-416.
7. Gertler, J. J. Fault Detection and Diagnosis in Engineering Systems, 1998 (Marcel Dekker, New York).
8. Patton, R. J., Frank, P. M. and Clark, R. N. (Eds) Fault Diagnosis in Dynamic Systems, Theory and Applications, 1989 (Prentice-Hall, Englewood Cliffs, New Jersey).
9. Chen, J. and Patton, R. J. Robust Model-based Fault Diagnosis for Dynamic Systems, 1999 (Kluwer, Dordrecht, The Netherlands).
10. Basseville, M. and Nikiforov, I. V. (Eds) Detection of Abrupt Changes, Theory and Applications, 1993 (Prentice-Hall, Englewood Cliffs, New Jersey).
11. Isermann, R. and Ballé, P. Trends in the applications of model-based fault detection and diagnosis of technical processes. Control Engng Practice, 1997, 5(5), 709-719.
12. Sneider, H. and Frank, P. M. Observer-based supervision and fault detection in robots using nonlinear and fuzzy logic residual evaluation. IEEE Trans. Control Systems Technol., 1996, 4(3), 274-282.
13. Frank, P. M. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy - a survey of some new results. Automatica, 1990, 26, 459-474.
14. Krishnaswami, V. and Rizzoni, G. Nonlinear parity equation residual generation for fault detection and isolation. In Proceedings of the IFAC-IMACS Symposium on Fault Detection, Supervision and Safety for Technical Processes, Espoo, Finland, 1994, pp. 317-322.
15. Krishnaswami, V., Luh, G.-C. and Rizzoni, G. Nonlinear parity equation based residual generation for diagnosis of automotive engine fault. Control Engng Practice, 1995, 3(10), 1385-1392.
16. Puig, V. and Quevedo, J. Passive robust fault detection using fuzzy parity equations. Math. Computers Simulation, 2002, 60(3-5), 193-207.
17. Medvedev, A. Fault detection and isolation by a continuous parity space method. Automatica, 1995, 31(7), 1039-1044.
18. Frank, P. M. and Ding, X. Survey of robust residual generation and evaluation methods in observer-based fault detection systems. J. Process Control, 1997, 7(6), 403-424.
19. Patton, R. J. and Chen, J. Observer-based fault detection and isolation: robustness and applications. Control Engng Practice, 1996, 5(5), 671-682.
20. Tarantino, R., Szigeti, F. and Colina-Morles, E. Generalized Luenberger observer-based fault-detection filter design: an industrial application. Control Engng Practice, 2000, 8, 665-671.
21. Chen, Y. M. and Lee, M. L. Neural networks-based scheme for system failure detection and diagnosis. Math. Computers Simulation, 2002, 58, 101-109.
22. Thomas, P. and Lefebvre, D. Fault detection and isolation in non-linear systems by using oversized neural networks. Math. Computers Simulation, 2002, 60, 181-192.
23. Costa Branco, P. J., Dente, J. A. and Vilela Mendes, R. Using immunology principles for fault detection. IEEE Trans. Ind. Electron., 2003, 50(2), 362-373.
24. Dasgupta, D. and Forrest, S. Novelty detection in time series data using ideas from immunology. In Proceedings of the International Conference on Intelligent Systems, Reno, Nevada, 19-21 June 1996, pp. 82-87.
25. Gonzalez, F., Dasgupta, D. and Kozma, R. 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 (IEEE, New York).
26. Ishiguro, A., Watanabe, Y. and Uchikawa, Y. Fault diagnosis of plant systems using immune networks. In Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Las Vegas, Nevada, 2-5 October 1994, pp. 34-42 (IEEE, New York).
27. Dasgupta, D. Artificial neural networks and artificial immune systems: similarities and differences. In Proceedings of the IEEE International Conference on System, Man, and Cybernetics, Orlando, Florida, 12-15 October 1997, pp. 873-878 (IEEE, New York).
28. de Castro, L. N. Comparing immune and neural networks. In Proceedings of the Seventh Brazilian Symposium on Neural Networks, Brazil, 11-14 November 2002, pp. 250-255.
29. Luh, G. C. and Cheng, W. C. Non-linear system identification using an artificial immune system. Proc. Instn Mech. Engrs, Part I: J. Systems and Control Engineering, 2001, 215(16), 569-585.
30. Dasgupta, D. Artificial Immune Systems and Their Applications, 1999 (Springer-Verlag, Berlin, Germany).
31. de Castro, L. N. and Von Zuben, F. J. Artificial immune systems. Part I: basic theory and applications. Technical Report TR-DCA 01/99, State University of Campinas, Brazil, 1999.
32. de Castro, L. N. and Jonathan, T. Artificial Immune Systems: A New Computational Intelligence Approach, 2002 (Springer-Verlag, Berlin, Germany).
33. Jerne, N. K. The immune system. Scient. Am., 1973, 229(1), 52-60.
34. Roitt, I., Brostoff, J. and Male, D. K. Immunology, 5th edition, 1998 (Mosby International Limited).
35. Hightower, R., Forrest, S. and Perelson, A. S. The evolution of emergent organization in immune system gene libraries. In Proceedings of the Sixth International Conference on Genetic Algorithms, San Mateo, California, 1995, pp. 344-350 (Morgan Kaufmann, San Mateo, California).
36. Forrest, S., Perelson, A. S., Allen, L. and Cherukuri, R. Self-nonself discrimination in a computer. In Proceedings of IEEE Symposium on Research in Security and Privacy, Oakland, California, 16-18 May 1994, pp. 202-212 (IEEE, New York).
37. Taniguchi, S. and Dote, Y. Sensor fault detection for uninterruptible power supply (UPS) control system using fast fuzzy-neural network and immune network. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Tucson, Arizona, 7-10 October 2001, pp. 99-104 (IEEE, New York).
38. Hunt, J. E. and Cooke, D. E. An adaptive distributed learning system based on immune system. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 22-25 October 1995, Vancouver, British Columbia, Canada, pp. 2494-2499 (IEEE, New York).
39. Dasgupta, D. and Attoh-Okln, N. Immunity-based systems: a survey. Computational Cybernetics and Simulation. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Orlando, Florida, 12-15 October 1997, pp. 369-374 (IEEE, New York).
40. Forrest, S., Hightower, R. and Perelson, A. S. The Baldwin effect in the immune system: learning by somatic hypermutation. In Individual Plasticity in Evolving Populations: Models and Algorithms, 1996 (Addison-Wesley, Reading, Massachusetts).
41. Timmis, J., Neal, M. and Hunt, J. Data analysis using artificial immune systems, cluster analysis and Kohonen networks: some comparisons. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Tokyo, Japan, 12-15 October 1999, pp. 922-927 (IEEE, New York).
42. Knight, T. and Timmis, J. AINE: an immunological approach to data mining. In Proceedings of the International Conference on Data Mining, San Jose, California, 29 November-2 December 2001, pp. 297-304 (IEEE, New York).
43. Baldi, P. F. and Soren, B. Bioinformatics: The Machine Learning Approach, 1999 (MIT Press, Cambridge, Massachusetts).
44. Lin, J. H. and Isik, C. A. Maximum entropy radial basis function network based neuro-fuzzy controller, In Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, New Orleans, Louisiana, 8-11 September 1996, pp. 156-161 (IEEE, New York).
45. Oprea, M. L. Antibody repertories and pathogen recognition: the role of germline diversity and somatic hypermutation. PhD dissertation, Department of Computer Science, The University of New Mexico, Albuquerque, New Mexico, 1999.
46. Sherstinsky, A. and Picard, R. W. On the efficiency of the orthogonal least squares training method for radial basis function networks. IEEE Trans. Neural Networks, 1996, 7(1), 195-200.
47. Chen, S., Cowan, C. F. N. and Grant, P. M. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Networks, 1991, 2(2), 302-309.
48. Eng-Siong, C., Yang, H. H. and Bos, S. Orthogonal leastsquares learning algorithm with local adaptation process for the radial basis function networks. IEEE Signal Processing Lett., 1996, 3(8), 253-255.
49. Hong, X. and Harris, C. J. Nonlinear model structure design and construction using orthogonal least squares and D-optimality design. IEEE Trans. Neural Networks, 2002, 13(5), 1245-1250.
50. Brouwn, G. G. and Krijgsman, A. J. Hybrid neural networks for nonlinear system identification. In Proceedings of the International Conference on Control, Coventry, 21-24 March 1994, pp. 504-509 (IEEE, New York).
51. Chen, S., Grant, P. M. and Cowan, C. F. N. Orthogonal least squares algorithm for training multi-output radial basis function networks. In Proceedings of the International Conference on Artificial Neural Networks, Bournemouth, November 1991, pp. 336-339 (IEEE, New York).
52. Sneider, H. and Frank, P. M. Observer-based supervision and fault detection in robots using nonlinear and fuzzy logic residual evaluation. IEEE Trans. Control Systems Technol., 1996, 4(3), 274-282.
53. Yaochu, J. Decentralized adaptive fuzzy control of robot manipulators. IEEE Trans. Systems, Man Cybernetics B, 1998, 28(1), 47-57.
54. Li, W. and Shah, S. Structured residual vector-based approach to sensor fault detection and isolation. J. Process Control, 2002, 12(3), 429-443.