Diagnosis Strategies and Systems: Principles, Fuzzy and Neural Approaches

  • Paul M. Frank
  • Teodor Marcu
Part of the International Series in Intelligent Technologies book series (ISIT, volume 15)


Fault tolerance of automatic control systems is gaining increasing importance. This is due to the increasing complexity of modern control systems and the growing demands for quality, cost efficiency, availability, reliability and safety. The use of knowledge based systems and of various“intelligent technologies” demonstrated significant improvements over the classic techniques. In this chapter, we review the state of this development along with the enumeration of some successful applications.


Fault Detection Fault Diagnosis Diagnosis System Unknown Input Actuator Fault 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Amann, P. and P.M. Frank (1997).“On Fuzzy Model-building in Observers for Fault Diagnosis,” IMACS World Congress, Berlin, vol.6, pp.695–700.Google Scholar
  2. Amann, P. et al. (1997).“Fuzzy Observer for Fault Detection in Complex Systems Applied to Detection of Critical Driving Situations.” ESF Third Joint COSY Workshop Control of Complex Systems, Budapest, pp. 153–158.Google Scholar
  3. Ayoubi, M. (1996a). “Fuzzy Systems Design Based on a Hybrid Neural Structure and Application to Fault Diagnosis of Technical Processes.” Control Engineering Practice 4(1), pp. 35–42.CrossRefGoogle Scholar
  4. Ayoubi, M. (1996b).“Nonlinear System Identification Based on Neural Networks with Locally Distributed Dynamics and Application to Technical Processes,” Forstschritt-Berichte VDI, Reihe 8, Nr.591, Düsseldorf: VDI-Verlag.Google Scholar
  5. Ayoubi, M. and R. Isermann (1997).“Neuro-fuzzy Systems for Diagnosis.” Fuzzy Sets and Systems 89, pp. 289–307.CrossRefGoogle Scholar
  6. Babuska, R. and H.B. Verbruggen (1996). “An Overview of Fuzzy Modeling for Control.” Control Engineering Practice 4(11), pp. 1593–1606.CrossRefGoogle Scholar
  7. Ballé, P. et al. (1998).“Integrated Control, Diagnosis and Reconfiguration of a Heat Exchanger.” IEEE Control Systems 18(3), pp. 52–63.CrossRefGoogle Scholar
  8. Bezdek, J.C. and S.K. Pal (1992).“Fuzzy Models for Pattern Recognition” New York: IEEE Press.Google Scholar
  9. Chang, J., S. DiCesare and G. Goldbogen (1991).“Failure Propagation Trees for Diagnosis in Manufacturing Systems”. IEEE Trans. Systems, Man, and Cybernetics 21, pp. 767–776.CrossRefGoogle Scholar
  10. Chen, J. and R.J. Patton (1998).“Robust Model-based Fault Diagnosis for Dynamic Systems,” Massachusetts: Kluwer Academic Publishers.Google Scholar
  11. Frank, P.M. (1990).“Fault Diagnosis in Dynamic Systems Using Analytical and Knowledge-based Redundancy.” Automatica 26(3), pp. 459–474.zbMATHCrossRefGoogle Scholar
  12. Frank, P.M. and X. Ding (1994).“Frequency Domain Approach to Optimally Robust Residual Generation and Evaluation for Model-based Fault Diagnosis.” Automatica 30(5), pp. 789–804.zbMATHCrossRefGoogle Scholar
  13. Frank, P.M. (1996).“Analytical and Qualitative Model-based Fault Diagnosis — A Survey and Some New Results.” European Journal of Control 2, pp. 6–28.zbMATHGoogle Scholar
  14. Frank, P.M. and X. Ding (1997).“Survey of Robust Residual Generation and Evaluation Methods”. Journal of Process Control 7(6), pp. 403–424.CrossRefGoogle Scholar
  15. Frank, P.M., E. Alcorta-Garcia and B. Koppen-Seliger (1997).“Modeling for Fault Detection and Isolation”. ESF Third Joint COSY Workshop Control of Complex Systems, Budapest, pp.111–129.Google Scholar
  16. Frank, P.M., G. Schreier and E. Alcorta Garcia (1999).“Nonlinear Observers for Fault Detection and Isolation”. In H. Nijmeijer and T.I. Fossen (Eds.), New Directions in Nonlinear Observer Design, Berlin: Springer Verlag.Google Scholar
  17. Füssel, D., P. Ballé and R. Isermann (1997).“Closed Loop Fault Diagnosis Based on a Nonlinear Process Model and Automatic Fuzzy Rule Generation”. IFAC Symp. Fault Detection, Supervision and Safety for Technical Processes, Kingston upon Hull, vol. 1, pp.359–364.Google Scholar
  18. Gertler, J. (1998).“Fault Detection and Diagnosis in Engineering Systems,” New York: Marcel Dekker.Google Scholar
  19. Haykin, S. (1994). Neural Networks:“A Comprehensive Foundation,” New York: Macmillan College Publishing Company.zbMATHGoogle Scholar
  20. Heiming, B. and J. Lunze (1999). “Three-Tank Benchmark Problem of Controller Reconfiguration.” EUCA/IFAC/IEEE European Control Conference, Karlsruhe.Google Scholar
  21. Isermann, R. (1993).“Fault Diagnosis of Machines via Parameter Estimation and Knowledge Processing.” Automatica 29(4), pp. 815–836.MathSciNetzbMATHCrossRefGoogle Scholar
  22. Isermann, R. and P. Ballé (1997).“Trends in the Application of Model-based Fault Detection and Diagnosis of Technical Processes.” Control Engineering Practice 5(5), pp. 709–719.CrossRefGoogle Scholar
  23. Isermann, R., S. Ernst and. O. Nelles (1997).“Identification with Dynamic Neural Networks — Architectures, Comparisons, Applications.” IFAC Symp. System Identification, Fukuoka, plenary paper.Google Scholar
  24. Jang, J.-S.R. and C.-T. Sun (1995).“Neuro-fuzzy Modeling and Control.” Proc. IEEE 83(3), pp. 378–405.CrossRefGoogle Scholar
  25. Joo, Y.H. et al. (1997).“Fuzzy Systems Modeling by Fuzzy Partition and GA Hybrid Schemes.” Fuzzy Sets and Systems 86, pp. 279–288.CrossRefGoogle Scholar
  26. Kandel, A. (1986).“Fuzzy Mathematical Techniques with Applications” Massachusetts: Addison-Wesley.Google Scholar
  27. Kiupel, N. et al. (1995).“Fuzzy Residual Evaluation Concept.” IEEE Int. Conf. Systems, Man, and Cybernetics, Vancouver, vol. 1, pp. 13–18.Google Scholar
  28. Kiupel, N. and P.M. Frank (1996a).“An Algorithm for a Filter Design for Fuzzy Supervision.” World Automation Congress, Montpellier, vol.1, pp. 417–422.Google Scholar
  29. Kiupel, N. and P.M. Frank (1996b).“Fuzzy Supervision for an Anaerobic Waste-water Plant.” IEEE Conf Computational Engineering in Systems Applications, Lille, vol.1, pp. 362–367.Google Scholar
  30. Klotzek, P., T. Dalton and P.M. Frank (1998).“Application of Sensitivity Theory to Fuzzy Logic Based FDI.” EC-INCO Copernicus 1Q2FD Workshop, Kazimierz Dolny, pp. 58–65.Google Scholar
  31. Köppen-Seliger, B. (1997).“Fehlerdiagnose mit künstlichen neuronalen Netzen,” Fortschritt-Berichte VDI, Reihe 8, Nr.632, Düsseldorf: VDI-Verlag.Google Scholar
  32. Marcu, T. (1996).“Pattern Recognition Techniques Using Fuzzily Labeled Data for Process Fault Diagnosis.” Journal of Applied Mathematics and Computer Science 6(4), pp. 815–840.MathSciNetzbMATHGoogle Scholar
  33. Marcu, T. and L. Mirea (1997).“Robust Detection and Isolation of Process Faults Using Neural Networks.” IEEE Control Systems, 17(5), pp. 72–79.CrossRefGoogle Scholar
  34. Marcu, T., L. Mirea and P.M. Frank (1999a).“Development of Dynamic Neural Networks with Application to Observer-based Fault Detection and Isolation.” Journal of Applied Mathematics and Computer Science 9(3).Google Scholar
  35. Marcu, T., M.H. Matcovschi and P.M. Frank (1999b).“Neural Observer-based Approach to Fault-tolerant Control of a Three-tank System.” EUCA/IFAC/IEEE European Control Conference, Karlsruhe.Google Scholar
  36. Mendel, J.M. (1995).“Fuzzy Logic Systems for Engineering.” Proc. IEEE 83(3), pp. 345–376.CrossRefGoogle Scholar
  37. Nelles, O. and M. Fischer (1996).“Local Linear Model Trees (LOLIMOT) for Nonlinear System Identification of a Cooling Blast,” European Congress on Intelligent Techniques and Soft Computing, Aachen, vol.2, pp. 1187–1191.Google Scholar
  38. Patton, R.J., P.M. Frank and R.N. Clark (Eds., 1989).“Fault Diagnosis in Dynamic Systems: Theory and Applications,” New Jersey: Prentice-Hall.Google Scholar
  39. Patton, R.J. (1994).“Robust Model-based Fault Diagnosis: The State of the Art.” IFAC Symp. Fault Detection, Supervision and Safety for Technical Processes, Espoo, vol.1, pp. 1–24.Google Scholar
  40. Pedrycz, W. (Ed., 1997). “Fuzzy Evolutionary Computation,” Massachusetts: Kluwer Academic Publishers.Google Scholar
  41. Peltier, M.A. and B. Dubuisson (1994).“A Fuzzy Diagnosis Process to Detect Evolution of a Car Driver’ s Behavior.” IFAC Symp. Fault Detection, Supervision and Safety for Technical Processes, Espoo, vol.2, pp. 786–801.Google Scholar
  42. Querelle, R. et al. (1997).“Fault Diagnosis on a Winding-machine.” IFAC Symp. Fault Detection, Supervision and Safety for Technical Processes, Kingston upon Hull, vol.1, pp. 480–485.Google Scholar
  43. Quipers, B. (1986).“Qualitative Simulation.” Artificial Intelligence 29, pp. 289–338.MathSciNetCrossRefGoogle Scholar
  44. Russo, M. (1998).“FuGeNeSys — A Fuzzy Genetic Neural System for Fuzzy Modeling.” IEEE Trans. Fuzzy Systems 6(3), pp. 373–388.CrossRefGoogle Scholar
  45. Schneider, H. and P.M. Frank (1996).“Observer-based Supervision and Fault Detection in Robots Using Nonlinear and Fuzzy Logic Residual Evaluation.” IEEE Trans. Control Systems Technology 4(3), pp. 274–282.CrossRefGoogle Scholar
  46. Shen, Q. and R. Leitcn (1993).“Fuzzy Qualitative Simulation.” IEEE Trans. Systems, Man, and Cybernetics 23(4), pp. 1038–1064.CrossRefGoogle Scholar
  47. Sorsa, T. and H.N. Koivo (1993).“Application of Artificial Neural Networks in Process Fault Diagnosis.” Automatica 29(4), pp. 843–849.zbMATHCrossRefGoogle Scholar
  48. Takagi, T. and M. Sugeno (1985).“Fuzzy Identification of Systems and Its Application to Modeling and Control.” IEEE Trans. Systems, Man, and Cybernetics 15(1), pp. 116–132.zbMATHCrossRefGoogle Scholar
  49. Vaidyanathan, R. and V. Venkatasubramanian (1992).“Representing and Diagnosing Process Data Using Neural Networks.” Engng. Applic. Artificial Intelligence 5(1), pp. 11–21.CrossRefGoogle Scholar
  50. Williams, R.J. and D. Zipser (1990).“Gradient-based Learning Algorithms for Recurrent Connectionist Networks” (Technical Report NU-CCS-90-9), Massachusetts: Northeastern University, College of Computer Science.Google Scholar
  51. Wünnenberg, J. (1990).“Observer-based Fault Detection in Dynamic Systems” Fortschritt-Berichte VDI, Reihe 8, Nr.222, Düsseldorf: VDI-Verlag.Google Scholar
  52. Zhuang, Z. and P.M. Frank (1997).“Qualitative Observer and Its Application to Fault Detection and Isolation Systems,” Proc. Instr. Mechn. Engnrs 211(I), pp. 253–262.Google Scholar
  53. Zimmermann, H.J. (1991).“Fuzzy Set Theory and Its Applications” Massachusetts: Kluwer Academic Publishers.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2000

Authors and Affiliations

  • Paul M. Frank
  • Teodor Marcu

There are no affiliations available

Personalised recommendations