A Fuzzy Model Based Sensor Fault Detection Scheme for Nonlinear Dynamic Systems

퍼지모델을 이용한 비선형시스템의 센서고장 검출식별

  • Published : 2007.02.01

Abstract

A sensor fault detection scheme(SFDS) for a class of nonlinear systems that can be represented by Takagi-Sugeno fuzzy model is proposed. Basically, the SFDS may be considered as a multiple observer scheme(MOS) in which the bank of state observers and the detection & isolation logic are included. However, the proposed scheme has two great differences from the conventional MOSs. First, the proposed scheme includes fuzzy fault detection observers(FFDO) that are constructed based on the T-S fuzzy model that provides very good approximation to nonlinear dynamic systems. Secondly, unlike the conventional MOS, the FFDOS are driven not parallelly but sequentially according to the predetermined sequence to avoid the massive computational burden, which is known to be the biggest obstacle to the practical application of the multiple observer based FDI schemes. During the operating time, each FFDO generates the residuals carrying the information of a specified fault, and the corresponding fault detection logic unit performs the logical operations to detect and isolate the fault of interest. The proposed scheme is applied to an inverted pendulum control system for sensor fault detection/isolation. Simulation study shows the practical feasibility of the proposed scheme.

Keywords

References

  1. R Patton, P. M. Frank and R N. Clark, Fault diagnosis In dynamic systems, Theory and Application, Prentice-Hall, 1989
  2. A.D. Pouliezos and G.S. Stavrakakis, Real time fault monitoring of industrial processes, Kluwer academic publishers, 1994
  3. S. Simani, C. Fantuzzi and R.J, Patton, Model-based fault diagnosis In dynamic systems using identification techniques, Springer, 2003
  4. P. M. Frank,' Fault diagnosis in dynamic systems using analytical and knowledge based redundancy: A survey and some new results' ,Automatica,Vol.26, 1990, pp.459-474 https://doi.org/10.1016/0005-1098(90)90018-D
  5. P. M. Frank,'On-line fault detection in uncertain nonlinear systems using diagnostic observers: A survey', International Journal of System Science, 1994, Vo!.25, No.12, pp.2129-2154 https://doi.org/10.1080/00207729408949341
  6. R Seliger and P. M. Frank,' Robust residual evaluation by threshold selection and a performance index for nonlinear observer based fault diagnosis', Proc. International Conf. on Fault Diagnosis, Toulouse, April, 1993
  7. D. Koenig and S. Mammar,'Design of a class of reduced order unknown input nonlinear observer for fault diagnosis', Proc. of ACC, Arlington, VA, June25-27,2oo1
  8. Wen Chen and Mehrdad Saif,' Robust fault detection in uncertain nonlinear systems via a second order sliding mode observer', Proc. of the 40th IEEE conf. on Decision and Control, Sydney, Australia, Dec. 2000
  9. D. Schroder, Intelligent observer and control design for nonlinear systems, springer, 2000
  10. K. Tanaka, T. Ikeda and H.O. Wang,' Fuzzy regulators and fuzzy observers: relaxed stability conditions and LMI-based designs', IEEE Transaction on Fuzzy Systems, Vo!.6, No.2, May, 1998, pp.250-265 https://doi.org/10.1109/91.669023
  11. M.C.M. Teixeira & S.H. Zak,' Stabilizing controller design for uncertain nonlinear systems using fuzzy models', IEEE Trans. Fuzzy Systems, Vol.7, No.2, April 1999, pp.133-142 https://doi.org/10.1109/91.755395
  12. P. Bergstein, R. Palm and D. Driankov, 'Fuzzy Observers', Proc. of 2001 IEEE International Fuzzy System Conference, pp.7oo-703
  13. E. Kim,' A fuzzy disturbance observer and its application to control', IEEE Transaction on Fuzzy Systems, Vol.10, No.1, February, 2002, pp.77-84 https://doi.org/10.1109/91.983280
  14. C.J. Lopez-Toribio, R]. Patton,' Takagi-Sugeno fuzzy fault tolerant control for a non-linear system', proc. of the 38th Conference on Decision and Control. Phoenix, Arizona USA, December 1999, pp. 4368-4373
  15. Y. Zheng, H. Fang, and H.G. Wang, 'Takagi-Sugeno fuzzy-model-based fault detection for networked control systems with markov delays', IEEE transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, Vol.36, NO.4, August, 2006, pp.924-929 https://doi.org/10.1109/TSMCB.2005.861879
  16. A. Ichtev, l Hellendoorn, and R Babuska,' Fault detection and isolation using multiple Takagi-Sugeno fuzzy models', Proceedings of the 2001 IEEE international fuzzy system conference, pp.l498-1502
  17. J.R Lee et. al., 'Fault Diagnosis of nonlinear system based on fuzzy dynamic model', Proceedings of the 1999 IEEE international fuzzy system conference, Seoul, Korea, August 1999, pp.245-250
  18. L.F.Mendonca, lM.C.Sousa and lM.G. Sa da Costa,' Fault detection and isolation of industrial process using optimized fuzzy models', Proceedings of the 2005 IEEE international conference on fuzzy system, May 2005, pp.851 -856
  19. 이기상, 이상문, ' 함수관측자를 이용한 장치고장검출기법', 전기학회논문지, 제 55D권, 제3호, 2006, pp.91-97
  20. L.H. Tsoukalas, RE. Uhrig, Fuzzy and neural approaches in engineering, John Wiley & sons, Inc, 1997