ENGINEERING SCIENCES. Information measuring and control systems
Zhirabok A., Pavlov S.
ALEXEI N. ZHIRABOK, Ph.D. Professor of Department of Automation and Control, School of Engineering, Far Eastern Federal University. Vladivostok. 8 Sukhanova St., Vladivostok, Russia, 690950, e-mail: zhirabok@mail.ru
SERGEI V. PAVLOV, Postgraduate Student, Department of Automation and Control, School of Engineering, Far Eastern Federal University. Vladivostok. 8 Sukhanova St., Vladivostok, Russia, 690950, e-mail: egoist@vladivostok.com
The non-parametric method of fault isolation in linear systems
The paper deals with the issue of fault isolation in technical systems described by linear dynamic models. To isolate faults, the non-parametric method is used, the special feature of which is that the parameters of the system being investigated may be unknown.
Key words: linear systems, diagnostics, fault isolation, non-parametric method.
REFERENCE
1. Bittencourt А., Saarinen K., Sander-Tavallaey S. A Data-driven method for monitoring systems that operate repetitively – applications to wear monitoring in an industrial robot joint. Proc. 8th IFAC Symp. Safeprocess’ 2012, p. 198-203.
2. Blanke M., Kinnaert M., Lunze J., Staroswiecki M. Diagnosis and Fault-Tolerant Control. Berlin, Heidelberg, Springer-Verlag, 2006.
3. Caccavale F., Villiani L. (eds). Fault Diagnosis and Tolerance for Mechatronic Systems, Recent Advances. Berlin, Heidelberg, Springer-Verlag, 2002.
4. Ding S., Wang Y., Yin S., Zhang P., Yang Y., Ding E. Date-driven design of fault-tolerant control systems. Proc. 8th IFAC Symp. Safeprocess’2012, p. 1323-1328.
5. Filaretov F., Zhirabok A., Tkachev D. Non-parametric method for fault diagnosis in electrical circuits. Proc. of 23d Int. DAAAM Symposium. 2012, p. 5-8.
6. Frank P. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results. Automatica. 1990(26):459-474.
7. Gertler J. Residual generation in model-based fault diagnosis. Control Theory and Advanced Technology. 1993(9):259-285.
8. Lou X., Willsky A., Verghese G. Optimally robust redundancy relations for failure detection in uncertain systems. Automatica. 1996(22):333-344.
9. Patton R. Robust model-based fault diagnosis: the state of the art. Proc. IFAC Symp. Safeprocess 1994, Finland, Espoo, 1994, p. 1-24.
10. Patton R., Frank P., Clark R. Issues of fault diagnosis for dynamic systems. London, Springer Verlag, 2000.
11. Russell E., Chiang L., Chiang L. Fault Detection and Diagnosis in Industrial Systems. , Berlin Heidelberg, Springer-Verlag, 2001.
12. Shumsky A. Data driven method for fault detection and isolation in nonlinear uncertain systems. CD. Proc. IFAC Conf. on Control Applications in Marine Systems, 2007.
13. Shumsky A. Functional diagnosis of nonlinear time-delay dynamic systems. Automation and Remote Control. 2009(70):172-184.
14. Shumsky A., Zhirabok A. Nonlinear diagnostic filter design: algebraic and geometric points of view. Int. Journal of Applied Mathematics and Computer Science. 2006(16):115-127.
15. Simani S., Fantuzzi C., Patton R. Model-based Fault Diagnosis in Dynamic Systems Using Identification. Berlin Heidelberg, Springer-Verlag, 2002.
16. Witczak M., Korbicz J., Jrozefowicz R. Design of unknown input observers for non-linear stochastic systems and their application to robust fault diagnosi. Control and Cybernetics. 2013(42):1-30.