Modelling and estimation strategies for fault diagnosis of non-linear systems: From analytical to soft computing approaches

Lecture Notes in Control and Information Sciences

Volumn 354, Issue , 2007, Pages 1-208

  Witczak, Marcin ^a  

^a UNIVERSITY OF ZIELONA GÓRA   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

FAULT DETECTION; SOFT COMPUTING;

ESTIMATION STRATEGIES; FAULTS DIAGNOSIS; MODELLING STRATEGIES; NON LINEAR SYSTEM; SOFT COMPUTING APPROACHES;

LINEAR SYSTEMS;

EID: 34547371525     PISSN: 01708643     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-71116-2     Document Type: Article

References (198)

1. C. Aboky, G. Sallet, and J. C. Vivalda. Observers for Lipschitz nonlinear systems. International Journal of Control, 75(3):204-212, 2002.
2. E. Alcorta Garcia and P. M. Frank. Deterministic nonlinear observer-based approaches to fault diagnosis. Control Engineering Practice, 5(5):663-670, 1997.
3. A. Alessandri, T. Parisini, and R. Zoppoli. Neural approximators for non-linear finite-memory state estimation. International Journal of Control, 67(2): 275-302, 1997.
4. B. D. O. Andersen and J. B. Moore. Optimal Filtering. Prentice-Hall, New Jersey, 1979.
5. L. Angrisani, A. Baccigalupi, and A. Pietrosanto. A digital signal-processing instrument for impedance measurement. IEEE Transactions on Instrumentation and Measurement, 45(6):930-934, 1996.
6. S. A. Ashton, D. N. Shields, and S. Daley. Design of a robust fault detection observer for polynomial non-linearities. In Proc. 14th IFAC World Congress, Beijing, P. R. China, 1999. CD-ROM.
7. A. C. Atkinson and A. N. Donev. Optimum Experimental Designs. Oxford University Press, New York, 1992.
8. S. S. Awad, N. Narasimhamurthi, and W. H. Ward. Analysis, design, and implementation of an AC bridge for impedance measurements. IEEE Transactions on Instrumentation and Measurement, 43(6):894-899, 1994.
9. M. Ayoubi. Fault diagnosis with dynamic neural structure and application to a turbo-charger. In Proc. 2nd IFAC Symp. Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS'94, volume 2, pages 618-623, Espoo, Finland, 1994.
10. A. D. Back and A. C. Tsoi. FIR and IIR synapses. A new neural network architecture for time series modelling. Neural Computation, 3(3):375-385, 1991.
11. T. Back, U. Hammel, and H. P. Schwefel. Evolutionary computation: comments on the history and current state. IEEE Transactions on Evolutionary Computation, 1(1):3-17, 1997.
12. M. Bartyś, R. Patton, M. Syfert, S. Heras, and J. Quevedo. Introduction to the DAMADICS actuator FDI study. Control Engineering Practice, 14(6): 577-596, 2006.
13. M. Basseville and I. V. Nikiforov. Detection of Abrupt Changes: Theory and Application. Prentice Hall, New York, 1993.
14. S. P. Bhattacharyya. Observer design for linear systems with unknown inputs. IEEE Transactions on Automatic Control, 23(3):483-484, 1978.
15. M. Blanke, S. Bogh, R.. B. Jorgensen, and R,. J. Patton. Fault detection for diesel engine actuator - a benchmark for FDI. In Proc. 2nd IFAC Symp. Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS'94, volume 2, pages 498-506, Espoo, Finland, 1994.
16. M. Blanke, M. Kinnaert, J. Lunze, and M. Staroswiecki. Diagnosis and FaultTolemnt Control. Springer-Verlag, New York, 2003.
17. E. P. J. Boer and E. M. T. Hendrix. Global optimization problems in optimal design of experiments in regression models. Journal of Global Optimization, 18(2):385-398, 2000.
18. M. Boutayeb. Identification of non-linear systemn in the presence of unknown but bounded disturbances. IEEE Transactions on Automatic Control, 45(8): 1503-1507, 2000.
19. M. Boutayeb and D. Aubry. A strong tracking extended Kalman observer for nonlinear discrete-time systems. IEEE Transactions on Automatic Control, 44(8): 1550-1556, 1999.
20. S. Boyd, E. Feron, L. E. Ghaoui, and V. Balakrishnan. Linear Matrix Inequalities in System and Control Theory. Siam, Philadelphia, 1994.
21. Z. Bubnicki. General approach to stability and stabilization for a class of uncertain discrete non-linear systems. International Journal of Control, 73(14): 1298-1306, 2000.
22. K. Busawon, M. Saif, and M. Farza. A discrete-time observer for a class of nonlinear systems. In Proc. 36th IEEE Conference on Decision and Control, CDC, pages 4796-4801, 1997.
23. C. Califano, S. Monaco, and D. Normand-Cyrot. On the observer design in discrete-time. System and Control Letters, 49(4):255-266, 2003.
24. S. L. Campbell and R. Nikoukhah. Auxiliary Signal Design for Failure Detection. Princeton University Press, Princeton, 2004.
25. P. Chandra and Y. Sing. Feedforward sigmoidal networks - equicontinuity and fault tolerance. IEEE Transactions on Neural Networks, 15(6): 1350-1366, 2004.
26. H. G. Chen and K. W. Han. Improved quantitative measures of robustness for multivariable systems. IEEE Transactions on Automatic Control, 39(4):807-810, 1994.
27. J. Chen and R. J. Patton. Robust Model Based Fault Diagnosis for Dynamic Systems. Kluwer Academic Publishers, London, 1999.
28. J. Chen, R. J. Patton, and G. P. Liu. Optimal residual design for fault diagnosis using multi-objective optimization and genetic algorithms. International Journal of Systems Science, 27(6): 567-576, 1996.
29. J. Chen, R. J. Patton, and H. Zhang. Design of unknown input observers and fault detection filters. International Journal of Control, 63(1):85-105, 1996.
30. Z. Chen, Y. He, F. Chu, and J. Huang. Evolutionary strategy for classification problems and its application in fault diagnosis. Engineering Applications of Artificial Intelligence, 16(1):31-38, 2003.
31. E. Y. Chow and A. A. Willsky. Analytical redundancy and the design of robust detection systems. IEEE Transactions on Automatic Control, 29(7):603-614, 1984.
32. G. Chryssolouris, M. Lee, and A. Ramsey. Confidence interval prediction for neural network models. IEEE Transactions on Neural Networks, 7(1):229-232, 1996.
33. T. Clement and S. Gentil. Reformulation of parameter identification with unknown-but-bounded errors. Mathematics and Computers in Simulation, 30(3):257-270, 1988.
34. T. Clement and S. Gentil. Recursive membership set estimation for output-error models. Mathematics and Computers in Simulation, 32(5-6):505-513, 1990.
35. D. A. Cohn. Neural network exploration using optimal experimental design. In Advances in Neural Information Processing Systems 6 (Cowan, J. et al., (Eds.)). Morgan Kaufman, San Mateo, 1994.
36. DAMADICS. Website of the research training network on Development and application of methods for actuator diagnosis in industrial control systems http://diag.mchtr.pw.edu.pl/damadics, 2004.
37. M. Darouach and M. Zasadzinski. Unbiased minimum variance estimation for systems with unknown exogenous inputs. Automatica, 33(4), 1997.
38. G. Dascupta and A. Huang. Assymptotically convergent modified least-squares with data dependent updating and forgetting factor for systems with bounded noise. IEEE Transactions on Information Theory, 33(3):383-391, 1987.
39. C. de Boor. A Practical Guide to Splines. Springer-Verlag, New York, 1978.
40. F. Delebecque, R. Nikoukah, and H. Rubio Scola. Test signal design for failure detection: A linear programming approach. International Journal of Applied Mathematics and Computer Science, 13(4):515-526, 2003.
41. I. S. Dhillon and S. Sra. Modeling Data Using Directional Distributions. Technical Report TR - 03 - 06, University of Texas of Austin, Austin, 2003.
42. S. X. Ding, Guo L., and Jeinsch T. A characterization of parity space and its application to robust fault detection. IEEE Trans. Automatic Control, 44(2):337-343, 1999.
43. W. Duch, J. Korbicz, L. Rutkowski, and R. Tadeusiewicz. Biocybernetics and Biomedical Engineeering 2000, Neural Networks., volume 6. Academic Publishing Office EXIT, Warsaw, 2000.
44. M. Dutta, A. Rakshid, N. S. Battacharyya, and J. K. Choudhury. An application of the LMS adaptive algorithm for a digital AC bridge. IEEE Transactions on Instrumentation and Measurement, 36(2):894-897, 1987.
45. M. Dutta, A. Rakshid, and S.N. Battacharyya. Development and study of an automatic AC bridge for impedance measurement. IEEE Transactions on Instrumentation and Measurement, 50(5), 2001.
46. D. Erdogmus, O. Fontenela-Romero, and J. C. Principe. Linear-least-square initialization of multilayer perceptions through backpropagation of the desired response. IEEE Transactions on Neural Networks, 16(2):325-337, 2005.
47. A. I. Esparcia-Alcazar. Genetic Programming for Adaptive Digital Signal Processing. PhD thesis, Glasgow University, 1998.
48. P. Fasconi, M. Gori, and G. Soda. Local feedback multilayered networks. Neural Computation, 4(1):120-130, 1992.
49. V. V. Fedorov. Theory of Optimal Experiments. Academic Press, New York, 1972.
50. V. V. Fedorov and P. Hackl. Model-oriented Design of Experiments. Springer, New York, 1997.
51. P. J Fleming and R.. C. Purshouse. Evolutionary algorithms in control systems engineering: a survey. Control Engineering Practice, 10(11):1223-1241, 2002.
52. R. Fletcher. Practical Methods of Optimization. John Wiley and Sons, Chichester, New York, Brisbane, Toronto, 1981.
53. D. B. Fogel. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. Willey-IEEE Press, New York, 1995.
54. L. J. Fogel, A. J. Owens, and M. J. Walsh. An overview of evolutionary programming. In Evolutionary Algorithms (Davis L. D., De Jong M. D., Vose M. D. and Whitley L. D. (Eds.), pages 89-109. Springer-Verlag, Heidelberg, 1999.
55. I. Ford, D. M. Titterington, and C. P. Kitsos. Recent advances in nonlinear experimental design. Technometrics, 31(1):49-60, 1989.
56. P. M. Frank and S. X. Ding. Survey of robust residual generation and evaluation methods in observer-based fault detection systems. Journal of Process Control, 7(6):403-424, 1997.
57. P. M. Frank, G. Schreier, and E. A. Garcia. Nonlinear observers for fault detection and isolation. In New Directions in Nonlinear Observer Design (H.Nijmeijer, T.Fassen (Eds.)). Springer-Verlag, Berlin, 1999.
58. M. J. Fuente and S. Saludes. Fault detection and isolation in a non-linear plant via neural networks. In Proc. 4th IFAC Symp. Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2000, volume 1, pages 472-477, Budapest, Hungary, 2000.
59. K. Fukumizu. Active learning in multilayer perceptions. In Advances in Neuml Information Processing Systems (Touretzky D. S. (Ed.)), pages 295-301. MIT Press, Cambridge, 1996.
60. K. Fukumizu. A regularity condition of the information matrix of a multi-layer perceptron network. Neural Networks, 9(5):871-879, 1996.
61. K. Fukumizu. Statistical active learning in multilayer perceptions. IEEE Transactions on Neural Networks, 11(1): 17-26, 2000.
62. P. Gahinet, A. Nemirovski, A. J. Laub, and M. Chilali. LMI Control Toolbox. For Use With Matlab. The Math Works Inc., Natick, MA, 1995.
63. R. Galar. Evolutionary search with soft selection. Biological Cybernetics, 60(1):124-141, 1989.
64. X. Z. Gao and S. J. Ovaska. Motor fault detection and diagnosis using soft computing method. In Soft Computing in Industrial Electronics (S. J. Ovaska (Ed.)), pages 3-45. Springer, Heidelberg, 2002.
65. J. Gertler. Fault Detection and Diagnosis in Engineering Systems. Marcel Dekker, New York, 1998.
66. G. C. Goodwin and R. L. Payne. Dynamic Systems Identification. Experiment Design and Data Analysis. Acadamic Press, New York, 1977.
67. G. C. Goodwin and K. S. Sin, Adaptive Filtering, Prediction and Control. Prentice-Hall, New Jersey, 1984.
68. M. Gori, Y. Bengio, and R. D. Mori. BPS: A learning algorithm for capturing the dynamic nature of speech. In Proc. Int. Joint Conf. Neural Networks, volume II, pages 417-423, 1989.
69. G. J. Gray, D. J. Murray-Smith, Y. Li, K. C. Sharman, and T. Weinbrenner. Non-linear model structure identification using genetic programming. Control Engineering Practice, 6(11):1341-1352, 1998.
70. C. Guernez, J. Ph. Cassar, and M. Staroswiecki. Extension of parity space to nonlinear polynomial dynamic systems. In Proc. 3rd IFAC Symp. Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS'97, volume 2, pages 861-866, Hull, UK, 1997.
71. G. Guglielmi, T. Parisini, and G. Rossi. Fault diagnosis and neural networks: A power plant application. Control Engineering Practice, 3(5):601-620, 1995.
72. L. Z. Guo and Q. M. Zhu. A fast convergent extended Kalman observer for non-linear discrete-time systems. International Journal of Systems Science, 33(13):1051-1058, 2002.
73. M. M. Gupta, L. Jin, and N. Homma. Static and Dynamic Neural, Networks. From Fundamentals to Advanced Theory. Wiley, New Jersey, 2003.
74. M. Gutowski. Lévy flights as an underlying mechanism for a global optimization algorithm. In Proc. 5th Conf. Evolutionary Algorithms and Global Optimization, Warsaw University of Technology Press, pages 79-86, Jastrzȩbia Góra, Poland, 2001.
75. A. Hac. Design of disturbance decoupled observers for bilinear systems. ASME Journal of Dynamic Systems, Measurement, and Control, 114:556-562, 1992.
76. J. Hertz, R. Krogh, and G. Palmer. Introduction to the Neural Computation. Addison-Wesley Publishing Company Inc., New York, 1991.
77. J. H. Holland. Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor, MI, 1975.
78. K. Hornik, M. Stinchombe, and H. White. Multi-layers feed-forward networks are universal approximators. Neural Computation, 2:359-366, 1989.
79. M. Hou and P. C. Mueller. Design of observers for linear systems with unknown inputs. IEEE Trans. Automatic Control, 37(6):871-875, 1992.
80. M. Hou and R.J. Patton. Optimal filtering for systems with unknown inputs. IEEE Trans. Automatic Control, 43(3):445-449, 1998.
81. M. Hou and A. C. Pugh. Observing state in bilinear systems: an UIO approach. In Proc. IFAC Symp. Fault Detection, Supervision and Safety of Technical Processes: SAFEPROCESS'97, volume 2, pages 783-788, Hull, UK, 1997.
82. S. Hui and S.H. Zak. Observer design for systems with unknown input. International Journal of Applied Mathematics and Computer Science, 15(4):431-446, 2005.
83. K. J. Hunt, D. Sbarbaro, R. Zbikowski, and P. J. Gawthrop. Neural networks for control systems - a survey. Automatica, 28(6):1083-1112, 1992.
84. R. Iserman. Fault diagnosis systems. An introduction from fault detection to fault tolerance. Springer-Verlag, New York, 2006.
85. A. G. Ivakhnenko and J. A. Mueller. Self-organizing of nets of active neurons. System Analysis Modelling Simulation, 20:93-106, 1995.
86. A. Janczak. Identification of Nonlinear Systems Using Neural Networks and Polynomial Models. A Block-oriented Approach. Lecture Notes in Control and Information Sciences. Springer-Verlag, Berlin, 2005. M. Thoma, M. Morari (Eds.).
87. J. Kaczmarek, R. Rybski, and D. Uciński. A recursive DSP approach to impedance measurement. In Proc. Symp. Development in Digital Measuring Instrumentation, IMEKO TC-4, Naples, Italy, 1998.
88. I. Karcz-Dulȩba. Asymptotic behaviour of a discrete dynamical system generated by a simple evolutionary process. International Journal of Applied Mathematics and Computer Science, 14(1):79-90, 2004.
89. M. Karpenko, N. Sepehri, and D. Scuse. Diagnosis of process valve actuator faults using a multilayer neural network. Control Engineering Practice, 11(11):1289-1299, 2003.
90. M. P. Kazimierkowski and T. Orlowska-Kowalska. Neural network estimation and N-F control in converter-fed induction motor drives. In Soft Computing in Industrial Electronics (S. J. Ovaska (Ed.)), pages 45-95. Springer, Heidelberg, 2002.
91. J. Y. Keller and M. Darouach. Two-stage Kalman estimator with unknown exogenous inputs. Automatica, 35(2), 1999.
92. M. Kinneart. Robust fault detection based on observers for bilinear systems. Automatica, 35(11):1829-1842, 1999.
93. N. Kobayashi and Nakamizo R.. An observer design for linear systems with unknown inputs. International Journal of Control, 17(3):471-479, 1982.
94. D. Koenig and S. Mammar. Desing of a class of reduced unknown inputs nonlinear observer for fault diagnosis. In Proc. American Control Conference, ACC, Arlington, USA, 2002.
95. J. Korbicz. Fault detection using analytical and soft computing methods. Bulletin of the Polish Academy of Sciences: Technical Sciences, 54(1):75-88, 2006.
96. J. Korbicz, J. Kościelny, Z. Kowalczuk, and W. Cholewa (Eds.). Fault diagnosis. Models, Artificial Intelligence, Applications. Springer-Verlag, Berlin, 2004.
97. J. Korbicz (Ed.). Special section on soft computing in control and fault diagnosis. International Journal of Applied Mathematics and Computer Science, 16(1):7-115, 2006.
98. J. M. Kościelny. Diagnostics of Automatic Industrial Processes. Academic Publishing Office EXIT, Warsaw, 2001.
99. M. Kowal. Optimization of neuro-fuzy structures in technical diagnostics. Lecture Notes in Control and Computer Science, Vol. 9. University of Zielona Gora Press, Zielona Gora, 2005.
100. Z. Kowalczuk and K. Gunawickrama. Leak detection and isolation for transission pipelines via non-linear state estimation. In Proc. 4th IFAC Symp., Fault Detection. Supervision and Safety of Technical Processes, SAFEPROCESS 2000, volume 2, pages 943-948, Budapest, Hungary, 2000.
101. Z. Kowalczuk and P. Suchomski. Analytical redundancy: Kalman filter as a basis for robust detection. In Proc. 5th Nat. Conf. Diagnostics of Industrial Processes, DPP 2001, Lagow Lubuski, Poland, 2001.
102. Z. Kowalczuk, P. Suchomski, and T. Bialaszewski. Evolutionary muti-objective pareto optimization of diagnostic state observers. International Journal of Applied Mathematics and Computer Science, 9(3):689-709, 1999.
103. J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge, 1992.
104. V. Krishnaswami and G. Rizzoni. Non-linear parity equation residual generation for fault detection and isolation. In Proc. 2nd IFAC Symp. Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS'94, volume 1, pages 317-322, Espoo, Finland, 1994.
105. T. T. Le, J. Watton, and D. T. Pham. An artificial neural network-based approach to fault diagnosis and classification of fluid power systems. Journal of Systems and Control Engineering, 211(4):307-317, 1997.
106. T. T. Le, J. Watton, and D. T. Pham. Fault classification of fluid power systems using a dynamics feature extraction technique and neural networks. Journal of Systems and Control Engineering, 212(2):87-97, 1998.
107. Ch.Y. Lee and X. Yao. Evolutionary programming using mutations based on the Levy probability distribution. 8(1):1-13, 2004.
108. D. MacKay. Information-based objective functions for active data selection. Neural Computation, 4(4):305-318, 1992.
109. D. Maksarow and J. Norton. State bounding with ellipsoidal set description of the uncertainty. International Journal of Control, 65(5):847-866, 1996.
110. T. Marcu. A multiobjective evolutionary approach to pattern recognition for robust diagnosis of process faults. In Proc. 3rd IFAC Symp. Fault detection, supervision and safety for technical processes, SAFEPROCESS'97, pages 1183-1188, Hull, UK, 1997.
111. K. V. Mardia and P. Jupp. Directional Statistics. John Willey and Sons, New York, 2000.
112. Ch. I. Marrison and R. F. Stengel. Robust control system design using random search and genetic algorithms. IEEE Transactions on Automatic Control, 42(6):835-839, 1997.
113. B. K. Massoumnia and W. E. Vander Velde. Generating parity relations for detecting and identifying control system components failures. Journal of Guidance, Control and Dynamics, 11(1):60-65, 1988.
114. M. Metenidis, M. Witczak, and J. Korbicz. A novel genetic programming approach to nonlinear system modelling: application to the DAMADICS benchmark problem. Engineering Applications of Artificial Intelligence, 17(4):363-370, 2004.
115. Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin, 1996.
116. M. Milanese, J. Norton, H. Piet-Lahanier, and E. Walter. Bounding Approaches to System Identification. Plenum Press, New York, 1996.
117. J. A. Miller, W. D. Potter, R. V. Gandham, and C. N. Lapena. An evaluation of local improvement operators for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 23(5):1340-1351, 1993.
118. S. H. Mo and J. P. Norton. Fast and robust algorithm to compute exact polytope parameter bounds. Mathematics and Computers in Simulation, 32:481-493, 1990.
119. M. Mrugalski. Neural Network Based Modelling of Non-linear Systems in Fault Detection Schemes. PhD thesis, Faculty of Electrical Engineering, Computer Science and Telecommunications, University of Zielona Góra, Zielona Góra, 2004.
120. M. Mrugalski and M. Witczak. Parameter estimation of dynamic GMDH neural networks with the bounded-error technique. Journal of Applied Computer Science, 10(1):77-90, 2002.
121. J. E. Mueller and F. Lemke. Self-organising Data Mining. Libri, Hamburg, 2000.
122. K. S. Narendra and K. Parthasarathy. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1(1):12-18, 1990.
123. O. Neues. Non-linear Systems Identification. From Classical Approaches to Neural Networks and Fuzzy Models. Springer, Berlin, 2001.
124. R. Nikoukhah, S. L. Campbell, and F. Delebecque. Detection signal design for failure detection: a robust approach. International Journal of Adaptive Control and Signal Processing, (14):701-724, 2000.
125. A. Obuchowicz. Evolutionary Algorithms in Global Optimization and Dynamic System Diagnosis. ISBN: 89-88917-02-4. Monographs, 3. Lubuskie Scientific Society, Zielona Góra, 2003.
126. A. Obuchowicz and J. Korbicz. Evolutionary methods in designing diagnostic systems. In Fault Diagnosis. Models, Artificial Intelligence, Applications. (Korbicz J., Kościelny J.M., Kowalczuk Z., and Cholewa W. (Eds.)), pages 301-331. Springer-Verlag, Berlin, 2004.
127. A. Obuchowicz and P. Prȩtki. Phenotypic evolution with mutation based on symmetric a-stable distribution. International Journal of Applied Mathematics and Computer Science, 14(3):289-316, 2004.
128. Papers of the special sessions. DAMADICS I, II, III. In Proc. 5th IFAC Symp. Fault Detection Supervision and Safety of Technical Processes, SAFEPROCESS 2003, Washington DC, USA, 2003. June 9-11.
129. T. Orlowska-Kowalska,. Sensorless driving systems with induction motors. Technical University of Wroclaw Press, Wroclaw, 2003.
130. S. Osowski, J. Herault, and P. Demartines. Fault location in analogue circuits using Kohonen nerual network. Bulletin of the Polish Academy of Sciences: Technical Sciences, 43(1):111-123, 1995.
131. S. Osowski and R. Salat. Fault location in transmission line using hybrid neural networks. Compel, 21(1):13-30, 2002.
132. S. J. Ovaska (Ed.). Soft Computing in Industrial Electronics. Springer, Heidelberg, 2002.
133. G. Papadopoulos, P. J. Edawrds, and A. F. Murray. Confidence estimation methods for neural networks: A practical comparison. IEEE Transactions on Neural Networks, 12(6): 1279-1287, 2001.
134. P. N. Paraskevopoulos. Digital Control Systems. Prentice Hall, London, 1996.
135. T Parisini, Polycarpou M., Sanguineti M., and Vemuri A. Robust parametric and non-parametric fault diagnosis in nonlinear input-output systems. In Proc. 36th Conf. Decision and Control, pages 4481-4482, San Diego, USA, 1997.
136. K. Patan and J. Korbicz. Artificial neural networks in fault diagnosis. In Fault Diagnosis. Models, Artificial Intelligence, Applications. (Korbicz J., Kościelny J.M., Kowalczuk Z., and Cholewa W. (Eds.)), pages 333-379. Springer-Verlag, Berlin, 2004.
137. K. Patan and T. Parisini. Identification of neural dynamic models for fault detection and isolation: the case of a real sugar evaporation process. Journal of Process Control, 15(l):67-79, 2005.
138. R. J. Patton, P. M. Frank, and R. N. Clark. Issues of Fault Diagnosis for Dynamic Systems. Springer-Verlag, Berlin, 2000.
139. R. J. Patton and J. Korbicz. Special issue: Advances in computational intelligence for fault dignosis systems. International Journal of Applied Mathematics and Computer Science, 9(3), 1999.
140. R. J. Patton, J. Korbicz, M. Witczak, and F. J. Uppal. Combined computational intelligence and analytical methods in fault diagnosis. In Intelligent Control Systems using Computational Intelligence Techniques, (Ruano, A. E. (Ed.)), pages 349-385. The IEE Press, London, 2005.
141. Y. B. Peng, A. Youssouf, P. Arte, and M. Kinnaert. A complete procedure for residual generation and evaluation with application to a heat exchanger. IEEE Transactions on Control Systems Technology, 5(6):542-555, 1997.
142. A. M. Pertew, H. J. Marquez, and Q. Zhao. Tioo synthesis of unknown input observers for non-linear lipschitz systems. International Journal of Control, 78(15):1155-1165, 2005.
143. D. T. Pham and L. Xing. Neural Networks for Identification, Prediction and Control. Springer-Verlag, London, 1995.
144. L. L. Porter and K. M. Passino. Genetic adaptive observers. Engineering Applications of Artificial Intelligence, 8(3):261-269, 1995.
145. P. Prçtki and A. Obuchowicz. Directional distribution and their application to evolutionary algorithms. In Lecture Notes in Artificial Intelligence: Artificial Intelligence and Soft Computing, pages 440-449. Springer-Verlag, Berlin, 2006.
146. L. Pronzato and E. Walter. Sequential experimental design for parameter bounding. In Proc. European Control Conference, ECC, pages 1181-1186, Grenoble, 1991.
147. L. Pronzato and E. Walter. Eliminating suboptimal local minimizers in nonlinear parameter estimation. Technometrics, 43(4):434-442, 2001.
148. S. Qiang, X. Z. Gao, and X. Zhunag. State-of-the-art in soft computing-based motor fault diagnosis. In Proc. IEE Int. Conf. Control Applications, pages 1381-1386, Istanbul, Turkey, 2003.
149. R. Rajamani. Observers for Lipschitz non-linear systems. IEEE Transactions on Automatic Control, 43(3):397-401, 1998.
150. R. Rajamani and Y. M. Cho. Existence and design of observers for nonlinear systems: relation to distance to unobservability. International Journal of Control, 69(5):717-731, 1998.
151. A. E. Ruano (Ed.). Intelligent Control Systems using Computational Intelligence Techniques. The IEE Press, London, 2005.
152. N.T. Russell, H.H.C. Bakker, and R.I. Chaplin. Modular neural network modelling for long-range prediction of an evaporator. Control Engineering Practice, 8(1):49-59, 2000.
153. D. Rutkowska. Neuro-Fuzzy Architectures and Hybrid Learning. Springer, Berlin, 2002.
154. L. Rutkowski. New Soft Computing Techniques for System Modelling, Patern Classification and Image Processing. Springer, Berlin, 2004.
155. G. Schreier, J. Ragot, R. J. Patton, and P. M. Frank. Observer design for a class of non-linear systems. In Proc. 3rd IFAC Symp. Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS'97, volume 1, pages 483-488, Hull, UK, 1997.
156. G. A. F. Seber and C. J. Wild. Nonlinear Regression. John Wiley and Sons, New York, 1989.
157. R. Seliger and P. Frank. Robust observer-based fault diagnosis in non-linear uncertain systems. In Issues of Fault Diagnosis for Dynamic Systems (Patton, Frank and Clark, (Eds.)). Springer-Verlag, Berlin, 2000.
158. D. N. Shields and S. Ashton. A fault detection observer method for non-linear systems. In Proc. 4th IFAC Symp. Fault Detection, Supervision and Safety of Technical, Processes, SAFEPROCESS 2000, volume 1, pages 226-231, Budapest, Hungary, 2000.
159. A. Shumsky. Robust residual generation for diagnosis of non-linear systems: parity relation approach. In Proc. 3rd IFAC Symp. Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS'97, volume 2, pages 867-872, Hull, UK, 1997.
160. J. Sjoberg, Q. Zhang, L. Ljung, A. Benveniste, B. Delyon, P. Y. Glorennec, H. Hjalmarsson, and A. Juditsky. Non-linear black-box modeling in system identification: A unified overview. Automatica, 31(12):1691-1724, 1995.
161. T. Soderstrom and P. Stoica. System Identification. Prentice-Hall International, Hemel Hempstead, 1989.
162. R. Sun, F. Tsung, and L. Qu. Combining bootstrap and genetic programming for feature discovery in diesel engine diagnosis. International Journal of Industrial Engineering, 11(3):273-281, 2004.
163. P. Supavatanakul. Timed discrete-event approach to the diagnosis of the actuator benchmark. In Proc. 4th DAMADICS Workshop on Qualitative Approach for Fault Diagnosis, Bochum, Germany, 2002.
164. H. J. Sussmann. Uniqueness of the weights for minimal feedworward nets with a given input-output map. Neural Networks, 5:589-593, 1992.
165. R. Tadeusiewicz and Ogiela M. R,. Medical Image Understanding Technology. Artificial Intelligence and Soft Computing for Image Understanding. Springer, Berlin, 2004.
166. F. E. Thau. Observing the state of nonlinear dynamic systems. International Journal of Control, 17(3):471-479, 1973.
167. D. Uciński. Optimal Measurement Methods for Distributed Parameter System Identification. Systems and Control Series. CRC Press, Boca Raton, 2005.
168. F.J. Uppal, R. J. Patton, and M. Witczak. A neuro-fuzzy multiple-model observer approach to robust fault diagnosis based on the DAMADICS benchmark problem. Control Engineering Practice, 14(6): 699-717, 2006.
169. B.K. Walker and H. Kuang-Yang. FD1 by extended Kalman filter parameter estimation for an industrial actuator benchmark. Control Engineering Practice., 3(12):1769-1774, 1995.
170. E. Walter and L. Pronzato. Identification of Parametric Models from Experimental Data. Springer, London, 1996.
171. S. H. Wang, E. J. Davison, and P. Dorato. Observing the states of systems with unmeasurable disturbances. IEEE Transactions on Automatic Control, 20(5):716-717, 1975.
172. Z. Wang and H. Unbehauen. A class of non-linear observers for discrete-time systems with parametric uncertainty. International Journal of Systems Science, 31(1):19-26, 2000.
173. J. Watkins and S. Yurkovich. Set-membership strategies for fault detection and isolation. In Proc. IEEE-SMC Symposium on Modelling, Analysis and Simulation, CESA, pages 824-830, Lille, France, 1996.
174. J. Watton and D. T. Pham. An artificial NN based approach to fault diagnosis and classification of fluid power systems. Journal of System and Control Engineering, 211(4):307-317, 1997.
175. M. Weerasinghe, J. B. Gomm, and D. Williams. Neural networks for fault diagnosis of a nuclear fuel processing plant at different operating points. Control Engineering Practice, 6(2):281-289, 1998.
176. H. White. Learning in artificial neural networks: A statistical perspective. Neural Computation, 4(4):305-318, 1989.
177. R. J. Williams and D. Zipser. A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1(2):270-280, 1989.
178. A. S. Willsky and H. L. Jones. A generalized likelihood ratio approach to the detection of jumps in linear systems. IEEE Transactions on Automatic Control, 21(1):108-112, 1976.
179. M. Witczak. Identification and fault detection of non-linear dynamic systems. Lecture Notes in Control and Computer Science, Vol. 1. University of Zielona Góra Press, Zielona Góra, 2003.
180. M. Witczak. Developing D-optimum experimental conditions for model-based fault detection systems. In Proc. 16th IFAC World Congress, Prague, Czech Republic, 2005.
181. M. Witczak. Advances in model-based fault diagnosis with evolutionary algorithms and neural networks. International Journal of Applied Mathematics and Computer Science, 16(1):85-99, 2006.
182. M. Witczak. Toward the training of feed-forward neural networks with the Doptimum input sequence. IEEE Transactions on Neural Networks, 17(2):357-373, 2006.
183. M. Witczak and J. Korbicz. Observers and genetic programming in the identification and fault diagnosis of non-linear dynamic systems. In Fault Diagnosis. Models, Artificial Intelligence, Applications. (Korbicz J., Kościelny J. M., Kowalczuk Z., and Cholewa W. (Eds.)), pages 457-509. Springer-Verlag, Berlin, 2004.
184. M. Witczak and J. Korbicz. Design of observers for Lipschitz non-linear discretetime systems. In Proc. 14th IFAC Symp. System Identification, SYSID 2006, pages 985-990, Newcastle, Australia, 2006. + CD-RDM.
185. M. Witczak, J. Korbicz, M. Mrugalski, and R. J. Patton. A GMDH neural network-based approach to robust fault diagnosis: application to the DAMADICS benchmark problem. Control Engineering Practice, 14(6):671-683, 2006.
186. M. Witczak, J. Korbicz, and V. Puig. An LMI approach to designing observers and unknown input observers for nonlinear systems. In Proc. 6th IFAC Symposium on Fault Detection Supervision and Safety of Technical Processes, SAFEPROCESS 2006, Beijing, China, 2006. + CD-ROM.
187. M. Witczak, A. Obuchowicz, and J. Korbicz. Genetic programming based approaches to identification and fault diagnosis of non-linear dynamic systems. International Journal of Control, 75(13):1012-1031, 2002.
188. M. Witczak and P. Prȩtki. Designing neural-network-based fault detection systems with D-optimum experimental conditions. Computer Assisted Mechanics and Engineering Sciences, 12(2):279-291, 2005.
189. M. Witczak and P. Prȩtki. Design of an extended unknown input observer with stochastic robustness techniques and evolutionary algorithms. International Journal of Control, 2007. In print.
190. G. G. Yen and K. Lin. Wavelet packet feature extraction for vibration monitoring. IEEE Transactions on Industrial Electronics, 47(3):650-667, 2000.
191. D. Yu and D. N. Shileds. Bilinear fault detection observer and its application to a hydraulic system. International Journal of Control, 64:1023-1047, 1996.
192. W. Yu and L. Xiaoou. Some new results on system identification with dynamic neural networks. IEEE Transactions on Neural Networks, 12(2):412-417, 2001.
193. H. Y. Zhang, C. W. Chan, K. C. Cheung, and Hong J. Nonlinear adaptive observer design based on b-spline neural network. In 14-th IFAC World Congress, pages 213-217, Beijing, P. R. China, 1999.
194. J. Zhang, J. Ma, and Y. Yan. Assessing blockage of the sensing line in a differential-pressure flow sensor by using the wavelet transform of its output. Measurement Science and Technology, 11(3): 178-184, 2000.
195. J. Q. Zhang, S. J. Ovaska, and Z. Xinmin. Development and study of an automatic AC bridge for impedance measurement. IEEE Transactions on Instrumentation Measurement, 47(2), 1998.
196. X. J. Zhang. Auxiliary Signal Design in Fault Detection and Diagnosis. SpringerVerlag, Heidelberg, 1989.
197. J. Zhao, B. Chen, and J. Shen. Multidimensional nonorthogonal wavelet-sigmoid basis function neural networkfor dynamic process fault diagnosis. Computers and Chemical Engineering, 23(1):83-92, 1998.
198. A. Zolghadri, D. Henry, and M. Monsion. Design of nonlinear observers for fault diagnosis, a case study. Control Engineering Practice, 4(11):1535-1544, 1996.