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Fault detection with process-identification methods

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Fault-Diagnosis Systems

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Chapter 9

  1. Armstrong-Hélouvry, B. Control of machines with friction. Kluwer, Boston, 1991.

    Book  MATH  Google Scholar 

  2. Ayoubi, M. Nonlinear system identification based on neural networks with locally distributed dynamics and application to technical processes, volume Fortschr.-Ber. VDI Reihe 8, 591. VDI Verlag, Düsseldorf, 1996.

    Google Scholar 

  3. Babuska, R. and Verbruggen, H. An overview of fuzzy modeling for control. Control Engineering Practice — CEP, 4(11):1593–1606, 1996.

    Article  Google Scholar 

  4. Bakiotis, C., Raymond, J., and Rault, A. Parameter and discriminiant analysis for jet engine mechanical state diagnosis. In Proc. of The 1979 IEEE Conf. on Decision & Control, Fort Lauderdale, USA, 1979.

    Google Scholar 

  5. Ballé, P. Fuzzy-model-based parity equations for fault isolation. Control Engineering Practice — CEP, 7:261–270, 1998.

    Article  Google Scholar 

  6. Bierman, G. Factorization methods for discrete sequential estimation, volume 128 of Mathematics in Science and Engineering. Academic Press, New York, 1977.

    MATH  Google Scholar 

  7. Bishop, C. Neural networks for pattern recognition. Oxford University Press, Oxford, 1995.

    Google Scholar 

  8. Bosch. Automotive brake systems. SAE, Warrendale, 1995.

    Google Scholar 

  9. Canudas de Wit, C. Adaptive control of partially known system. Elsevier, Boston, 1988.

    Google Scholar 

  10. Drewelow, W. Parameterschätzung nach der ausgangsfehlermethods. Messen, Steuern, Regeln — MSR, 33(1):15–23, 1990.

    Google Scholar 

  11. (Ernst), S. T. Hinging hyperplane trees for approximation and identification. In 37th IEEE Conference on Decision and Control, Tampa, FL, USA, December 1998.

    Google Scholar 

  12. Eykhoff, P. System identification. John Wiley, London, 1974.

    Google Scholar 

  13. Filbert, D. Fault diagnosis in nonlinear electromechanical systems by continuous-time parameter estimation. ISA Trans., 24(3):23–27, 1985.

    Google Scholar 

  14. Filbert, D. and Metzger, L. Quality test of systems by parameter estimation. In Proc. 9th IMEKO-Congress, Berlin, Germany, May 1982.

    Google Scholar 

  15. Golub, G. H. and Loan, C. F. V. Matrix computations. Johns Hopkins Studies in the Mathematical Sciences. Academic Press, Baltimore and London, 1996.

    MATH  Google Scholar 

  16. Goodwin, G. and Payne, R. Dynamic system identification: experiment design and data analysis, volume 136 of Mathematics in science and engineering. Academic Press, New York, 1977.

    MATH  Google Scholar 

  17. Gustavsson, I., Ljung, L., and Söderström, T. Identification of processes in closed loop identifiability and accuracy aspects. Automatica, 13(1):59–75, 1977.

    Article  MATH  MathSciNet  Google Scholar 

  18. Haber, R. and Unbehauen, H. Structure identification of nonlinear dynamic systems-a survey on input/output approaches. Automatica, 26(4):651–677, 1990.

    Article  MATH  MathSciNet  Google Scholar 

  19. Hafner, S., Geiger, H., and Kreßel, U. Anwendung künstlicher neuronaler Netze in der Automatisierungstechnik. Teil 1: Eine Einführung. Automatisierungstechnische Praxis — atp, 34(10):592–645, 1992.

    Google Scholar 

  20. Haykin, S. Neural networks. Macmillan College Publishing Company, Inc., New York, 1994.

    MATH  Google Scholar 

  21. Hecht-Nielson, R. Neurocomputing. Addison-Wesley, Reading, 1990.

    Google Scholar 

  22. Held, V. Parameterschätzung und Reglersynthese für Industrieroboter, volume Fortschr.-Ber. VDI Reihe 8, 275. VDI Verlag, Düsseldorf, 1991.

    Google Scholar 

  23. Held, V. and Maron, C. Estimation of friction characteristics, inertial and coupling coefficients in robotic joins based on current and speed measurements. In Proc. IFAC Symposium on Robot Control, Karlsruhe, Germany, 1988. Pergamon, Oxford.

    Google Scholar 

  24. Hohmann, H. Automatische Überwachung und Fehlerdiagnose an Werkzeugmaschinen. PhD thesis, Technische Hochschule, Darmstadt, 1987.

    Google Scholar 

  25. Holzmann, H., Halfmann, C., Germann, S., Würtenberger, M., and Isermann, R. Longitudinal and lateral control and supervision of autonomous intelligent vehicles. Control Engineering Practice — CEP, 5(11):1599–1605, 1997.

    Article  Google Scholar 

  26. Isermann, R. Methoden zur Fehlererkennung für die Überwachung technischer Prozesse. Regelungstechnische Praxis, (9 & 10):321–325 & 363–368, 1980.

    Google Scholar 

  27. Isermann, R. Parameter-adaptive control algorithms-a tutorial. Automatica, 18(5):513–528, 1982.

    Article  MATH  MathSciNet  Google Scholar 

  28. Isermann, R. Process fault detection on modeling and estimation methods-a survey. Automatica, 20(4):387–404, 1984.

    Article  MATH  Google Scholar 

  29. Isermann, R. Digitale Regelsysteme, volume 1 &, 2. Springer, Berlin, 1988.

    Google Scholar 

  30. Isermann, R. Identifikation dynamischer Systeme. Springer, Berlin, 1992.

    Google Scholar 

  31. Isermann, R., Lachmann, K.-H., and Matko, D. Adaptive control systems. Prentice Hall International UK, London, 1992.

    MATH  Google Scholar 

  32. Isermann, R., (Töpfer), S. E., and Nelles, O. Identification with dynamic neural networks-architecture, comparisons, applications-. In Proc. IFAC Symposium on System Identification, Fukuoka, Japan, July 1997.

    Google Scholar 

  33. Kaminski, P., Bryson, A., and Schmidt, S. Discrete square root filtering: A survey of current technologies. IEEE Trans. Auto. Control, 16:727–736, 1971.

    Article  Google Scholar 

  34. Kofahl, R. Self-tuning of PID controllers based on process parameter estimation. Journal A, 27(3), 1986.

    Google Scholar 

  35. Lachmann, K.-H. Parameteradaptive Regelalgorithmen für bestimmte Klassen nichtlinearer Prozesse mit eindeutigen Nichtlinearitäten, volume Fortschr.-Ber. VDI Reihe 8, 66. VDI Verlag, Düsseldorf, 1983.

    Google Scholar 

  36. Ljung, L., Morf, M., and Falconer, D. Fast calculation of gain matrices for recursive estimate schemes. Int. J. Control, 27:1–19, 1978.

    Article  MathSciNet  Google Scholar 

  37. Ljung, L. and Söderström, T. Theory and practive of recursive identification. MIT Press, Cambridge, MA, 1983.

    Google Scholar 

  38. Maron, C. Methoden zur Identifikation und Lageregelung mechanischer Prozesse mit Reibung, volume Fortschr.-Ber. VDI Reihe 8, 246. VDI Verlag, Düsseldorf, 1996.

    Google Scholar 

  39. McCulloch, W. and Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys., 5:115–133, 1943.

    Article  MATH  MathSciNet  Google Scholar 

  40. Müller, N. Adaptive Motorregelung beim Ottomotor unter Verwendung von Brennraumdruck-Sensoren, volume Fortschr.-Ber. VDI Reihe 12, 545. VDI Verlag, Düsseldorf, 2003.

    Google Scholar 

  41. Murray-Smith, R. and Johansen, T. Multiple model approaches to modelling and control. Taylor & Francis, London, 1997.

    Google Scholar 

  42. Nelles, O. LOLIMOT — Lokale, lineare Modelle zur Identifikation nichtlinearer, dynamischer Systeme. Automatisierungstechnik — at, 45(4):163–174, 1997.

    Google Scholar 

  43. Nelles, O. Nonlinear system identification. Springer, Heidelberg, 2001.

    Book  MATH  Google Scholar 

  44. Nelles, O., Hecker, O., and Isermann, R. Automatic model selection in local linear model trees (LOLIMOT) for nonlinear system identification of a transport delay process. In Proc. 11th IFAC Symposium on System Identification (SYSID), Kitakyushu, Fukuoka, Japan, 1991.

    Google Scholar 

  45. Nelles, O. and Isermann, R. Identification of nonlinear dynamic systems — classical methods versus radial basis function networks. In Proc. American Control Conference (ACC), Seattle, USA, 1995.

    Google Scholar 

  46. Oppenheim, A., Schafer, R., and Buck, J. Discrete-time signal processing. Prentice Hall, Englewood Cliffs, 2nd edition, 1999.

    Google Scholar 

  47. Panuska, V. An adaptive recursive least square identification algorithm. In Proc. IEEE Symp. Adaptive Processes, Decision and Control, 1969.

    Google Scholar 

  48. Peter, K.-H. Parameteradaptive Regelalgorithmne auf der Basis zeitkontinuierlicher Prozessmodelle, volume Fortschr.-Ber. VDI Reihe 8, 348. VDI Verlag, Düsseldorf, 1993.

    Google Scholar 

  49. Peterka, V. A square root filter for real time multivariate regression. Kybernetika, 11:53–67, 1975.

    MATH  MathSciNet  Google Scholar 

  50. Pfeufer, T. Modellgestützte Fehlererkennung und Diagnose am Beispiel eines Fahrzeugaktors, volume Fortschr.-Ber. VDI Reihe 8, 749. VDI Verlag, Düsseldorf, 1999.

    Google Scholar 

  51. Preuß, H.-P. and Tresp, V. Neuro-Fuzzy. Automatisierungstechnische Praxis-at, 36(5):10–24, 1994.

    Google Scholar 

  52. Raab, U. Modellgestützte digitale Regelung und Überwachung von Kraftfahrzeugaktoren, volume Fortschr.-Ber. VDI Reihe 8, 313. VDI Verlag, Düsseldorf, 1993.

    Google Scholar 

  53. Rosenblatt, F. The perceptron: a probabilistic model for information storage & organization in the brain. Psychological Review, 65:386–408, 1958.

    Article  MathSciNet  Google Scholar 

  54. Schmitt, M. Untersuchungen zur Realisierung mehrdimensionaler lernfähiger Kennfeler in Großserien-Steuergeräten, volume Fortschr.-Ber. VDI Reihe 12, 246. VDI Verlag, Düsseldorf, 1995.

    Google Scholar 

  55. Strejc, V. Least square parameter estimation. Automatica, 16:535–550, 1980.

    Article  MATH  MathSciNet  Google Scholar 

  56. Töpfer, S. Hierarchische neuronale Modelle für die Identifikation nichtlinearer Systeme, volume Fortschr.-Ber. VDI Reihe 10, 705. VDI Verlag, Düsseldorf, 2002.

    Google Scholar 

  57. Töpfer, S., Wolfram, A., and Isermann, R. Semi-physical modelling of nonlinear processes by means of local model approaches. In Proc. 15th IFAC World Congress, Barcelona, Spain, July 2002.

    Google Scholar 

  58. Widrow, B. and Hoff, M. Adaptive switching circuits. IRE WESCON Conv. Rec., pages 96–104, 1960.

    Google Scholar 

  59. Wolfram, A. and Moseler, O. Design and application of digital FIR differentiators using modulating functions. In Proc. 12th IFAC Symposium on System Identification (SYSID), Santa Barbara, CA, USA, June 2001.

    Google Scholar 

  60. Wolfram, A. and Vogt, M. Zeitdiskrete Filteralgorithmen zur Erzeugung zeitlicher Ableitungen. Automatisierungstechnik — at, 7:346–353, 2002.

    Google Scholar 

  61. Young, P. The use of linear regression and related procedures for the identification of dynamic processes. In Proc. 7th IEEE Symposium on Adaptive Processes, Los Angeles, CA, USA, 1968.

    Google Scholar 

  62. Young, P. Recursive estimation and time-series analysis. Springer, Berlin, 1984.

    Book  MATH  Google Scholar 

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  1. Institut für Automatisierungstechnik Fachgebiet Regelungstechnik und Prozessautomatisierung, TU Darmstadt, Landgraf-Georg-Str. 4, 64283, Darmstadt, Germany

    Professor Dr. Rolf Isermann

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Isermann, R. (2006). Fault detection with process-identification methods. In: Fault-Diagnosis Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-30368-5_9

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  • DOI: https://doi.org/10.1007/3-540-30368-5_9

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