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Neural Network-based Approaches to Fault Diagnosis

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Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 266))

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Abstract

Apart from the unquestionable effectiveness of the approaches presented in the preceding chapter, there are examples for which fault directions are very similar to that of an unknown input.

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References

  1. J. Chen, R.J. Patton, Robust Model Based Fault Diagnosis for Dynamic Systems (Kluwer Academic Publishers, London, 1999)

    Google Scholar 

  2. K. Patan, M. Witczak, J. Korbicz, Towards robustness in neural network based fault diagnosis. Int. J. Appl. Math. Comput. Sci. 18(4), 443–454 (2008)

    MATH  Google Scholar 

  3. M. Witczak, Toward the training of feed-forward neural networks with the \(\text{ D }\)-optimum input sequence. IEEE Trans. Neural Netw. 17(2), 357–373 (2006)

    Article  Google Scholar 

  4. M. Witczak, P. Prȩtki, Designing neural-network-based fault detection systems with \(\text{ D }\)-optimum experimental conditions. Comput. Assist. Mech. Eng. Sci. 12(2), 279–291 (2005)

    MATH  Google Scholar 

  5. M. Witczak, Modelling and Estimation Strategies for Fault Diagnosis of Non-linear Systems (Springer, Berlin, 2007)

    Google Scholar 

  6. M. Milanese, J. Norton, H. Piet-Lahanier, E. Walter, Bounding Approaches to System Identification (Plenum Press, New York, 1996)

    Google Scholar 

  7. E. Walter, L. Pronzato, Identification of Parametric Models from Experimental Data (Springer, London, 1996)

    Google Scholar 

  8. M. Witczak, J. Korbicz, M. Mrugalski, R.J. Patton, A \(\text{ GMDH }\) neural network-based approach to robust fault diagnosis: application to the \(\text{ DAMADICS }\) benchmark problem. Control Eng. Pract. 14(6), 671–683 (2006)

    Article  Google Scholar 

  9. V. Puig, M. Witczak, F. Nejari, J. Quevedo, J. Korbicz, A GMDH neural network-based approach to passive robust fault detection using a constraint satisfaction backward test. Eng. Appl. Artif. Intell. 20(7), 886–897 (2007)

    Article  Google Scholar 

  10. A.C. Atkinson, A.N. Donev, Optimum Experimental Designs (Oxford University Press, New York, 1992)

    Google Scholar 

  11. M. Witczak, R. Rybski, J. Kaczmarek, Impedance measurement with the D-optimum experimental conditions. IEEE Trans. Instrum. Meas. 58(8), 2535–2543 (2009)

    Article  Google Scholar 

  12. K. Fukumizu, Statistical active learning in multilayer perceptrons. IEEE Trans. Neural Netw. 11(1), 17–26 (2000)

    Article  Google Scholar 

  13. D. Uciński, Optimal Measurement Methods for Distributed Parameter System Identification. Systems and Control Series (CRC Press, Boca Raton, 2005)

    Google Scholar 

  14. G.C. Goodwin, R.L. Payne, Dynamic Systems Identification. Experiment Design and Data Analysis (Acadamic Press, New York, 1977)

    Google Scholar 

  15. V.V. Fedorov, P. Hackl, Model-oriented Design of Experiments (Springer, New York, 1997)

    Google Scholar 

  16. G. Chryssolouris, M. Lee, A. Ramsey, Confidence interval prediction for neural network models. IEEE Trans. Neural Netw. 7(1), 229–232 (1996)

    Article  Google Scholar 

  17. K. Fukumizu, A regularity condition of the information matrix of a multi-layer perceptron network. Neural Netw. 9(5), 871–879 (1996)

    Article  Google Scholar 

  18. K. Fukumizu, Active learning in multilayer perceptrons, in Advances in Neural Information Processing Systems, ed. by D.S. Touretzky (MIT Press, Cambridge, 1996), pp. 295–301

    Google Scholar 

  19. M.M. Gupta, L. Jin, N. Homma, Static and Dynamic Neural Networks. From Fundamentals to Advanced Theory (Wiley, New Jersey, 2003)

    Google Scholar 

  20. D. Erdogmus, O. Fontenela-Romero, J.C. Principe, Linear-least-square initialization of multilayer perceptrons through backpropagation of the desired response. IEEE Trans. Neural Netw. 16(2), 325–337 (2005)

    Article  Google Scholar 

  21. E.P.J. Boer, E.M.T. Hendrix, Global optimization problems in optimal design of experiments in regression models. J. Global Optim. 18(2), 385–398 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  22. J. Korbicz, J. Kościelny, Z. Kowalczuk, W. Cholewa (eds.), Fault Diagnosis. Models, Artificial Intelligence, Applications (Springer, Berlin, 2004)

    Google Scholar 

  23. L. Pronzato, E. Walter, Eliminating suboptimal local minimizers in nonlinear parameter estimation. Technometrics 43(4), 434–442 (2001)

    Article  MathSciNet  Google Scholar 

  24. H.J. Sussmann, Uniqueness of the weights for minimal feedworward nets with a given input-output map. Neural Netw. 5, 589–593 (1992)

    Article  Google Scholar 

  25. V.V. Fedorov, Theory of Optimal Experiments (Academic Press, New York, 1972)

    Google Scholar 

  26. I. Ford, D.M. Titterington, C.P. Kitsos, Recent advances in nonlinear experimental design. Technometrics 31(1), 49–60 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  27. DAMADICS. Website of the research training network on \(\text{ Development }\) and application of methods for actuator diagnosis in industrial control systems http://diag.mchtr.pw.edu.pl/damadics, 2004

  28. M. Metenidis, M. Witczak, J. Korbicz, A novel genetic programming approach to nonlinear system modelling: application to the \(\text{ DAMADICS }\) benchmark problem. Eng. Appl. Artif. Intell. 17(4), 363–370 (2004)

    Article  Google Scholar 

  29. Papers of the special sessions. \(\text{ DAMADICS } \text{ I, } \text{ II, } \text{ III }\). In Proc. 5th IFAC Symp. Fault Detection Supervision and Safety of Technical Processes, SAFEPROCESS 2003, Washington DC, USA, 2003

    Google Scholar 

  30. M. Witczak, J. Korbicz, Observers and genetic programming in the identification and fault diagnosis of non-linear dynamic systems, in Fault Diagnosis. Models, Artificial Intelligence, Applications, ed. by J. Korbicz, J.M. Kościelny , Z. Kowalczuk, W. Cholewa (Springer, Berlin, 2004), pp. 457–509

    Google Scholar 

  31. 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)

    Google Scholar 

  32. J.E. Mueller, F. Lemke, Self-Organising Data Mining (Libri, Hamburg, 2000)

    Google Scholar 

  33. D.T. Pham, L. Xing, Neural Networks for Identification. Prediction and Control (Springer, London, 1995)

    Google Scholar 

  34. 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)

    Google Scholar 

  35. A.G. Ivakhnenko, J.A. Mueller, Self-organizing of nets of active neurons. Syst. Anal. Model. Simul. 20, 93–106 (1995)

    Google Scholar 

  36. T. Soderstrom, P. Stoica, System Identification (Prentice-Hall International, Hemel Hempstead, 1989)

    Google Scholar 

  37. G. Papadopoulos, P.J. Edwards, A.F. Murray, Confidence estimation methods for neural networks: a practical comparison. IEEE Trans. Neural Netw. 12(6), 1279–1287 (2001)

    Google Scholar 

  38. G.A.F. Seber, C.J. Wild, Nonlinear Regression (John Wiley and Sons, New York, 1989)

    Google Scholar 

  39. S.H. Mo, J.P. Norton, Fast and robust algorithm to compute exact polytope parameter bounds. Math. Comput. Simul. 32, 481–493 (1990)

    Article  MathSciNet  Google Scholar 

  40. P.M. Frank, G. Schreier, E.A. Garcia, Nonlinear observers for fault detection and isolation, in New Directions in Nonlinear Observer Design, ed. by H. Nijmeijer, T. Fossen (Springer, Berlin, 1999)

    Google Scholar 

  41. T. Clement, S. Gentil, Reformulation of parameter identification with unknown-but-bounded errors. Math. Comput. Simul. 30(3), 257–270 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  42. R. Fletcher, Practical Methods of Optimization (John Wiley and Sons, Chichester, 1981)

    Google Scholar 

  43. T. Clement, S. Gentil, Recursive membership set estimation for output-error models. Math. Comput. Simul. 32(5–6), 505–513 (1990)

    Article  MathSciNet  Google Scholar 

  44. K. Patan, J. Korbicz, Artificial neural networks in fault diagnosis, in Fault Diagnosis. Models, Artificial Intelligence, Applications, ed. J. Korbicz, J.M. Kościelny, Z. Kowalczuk , W. Cholewa (Springer, Berlin, 2004), pp. 333–379

    Google Scholar 

  45. L. Jaulin, M. Kieffer, O. Didrit, E. Walter, Applied Interval Analysis, with Examples in Parameter and State Estimation. Robust Control and Robotics (Springer, London, 2001)

    Google Scholar 

  46. M. Bartyś, R. Patton, M. Syfert, S. Heras, J. Quevedo, Introduction to the \(\text{ DAMADICS }\) actuator \(\text{ FDI }\) study. Control Eng. Pract. 14(6), 577–596 (2006)

    Article  Google Scholar 

  47. M. Mrugalski, M. Witczak, Parameter estimation of dynamic \(\text{ GMDH }\) neural networks with the bounded-error technique. J. Appl. Comput. Sci. 10(1), 77–90 (2002)

    Google Scholar 

  48. M. Mrugalski, M. Witczak, State-space GMDH neural networks for actuator robust fault diagnosis. Adv. Electr. Comput. Eng. 12(3), 65–72 (2012)

    Article  Google Scholar 

  49. M. Mrugalski, An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection. Int. J. Appl. Math. Comput. Sci. 23(1), 157–169 (2013)

    MathSciNet  Google Scholar 

  50. M. Mrugalski, M. Witczak, J. Korbicz, Confidence estimation of the multi-layer perceptron and its application in fault detection systems. Eng. Appl. Artif. Intell. 21(8), 895–906 (2008)

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Marcin Witczak

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  1. Marcin Witczak
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Correspondence to Marcin Witczak .

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Witczak, M. (2014). Neural Network-based Approaches to Fault Diagnosis. In: Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems. Lecture Notes in Electrical Engineering, vol 266. Springer, Cham. https://doi.org/10.1007/978-3-319-03014-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-03014-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03013-5

  • Online ISBN: 978-3-319-03014-2

  • eBook Packages: EngineeringEngineering (R0)

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