Double fault distinguishability in linear systems

• Autorzy: Jan Maciej Kościelny , Zofia M. Łabęda-Grudziak
• Języki publikacji: EN

Abstrakty

EN

This paper develops a new approach to double fault isolation in linear systems with the aid of directional residuals. The method of residual generation for computational as well as internal forms is applied. Isolation of double faults is based on the investigation of the coplanarity of the residual vector with the planes defined by the individual pairs of directional fault vectors. Additionally, the method of designing secondary residuals, which are structured and directional, is proposed. These transformations allow achieving various isolation properties. It is shown that double fault distinguishability can be improved by decomposing the observed residual vector along the response directions. The described methods are illustrated with a simple computational example.

Słowa kluczowe

EN

faults isolation; double fault; fault distinguishability; directional and structured residuals; secondary residuals; linear systems

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2013
• Tom: 23
• Numer: 2
• Strony: 395-406

Daty

wydano

2013

otrzymano

2011-11-27

poprawiono

2012-10-17

poprawiono

2013-01-21

Twórcy

autor

Jan Maciej Kościelny

• Institute of Automatic Control and Robotics, Warsaw University of Technology, ul. św. Andrzeja Boboli 8, 02-525 Warsaw, Poland

autor

Zofia M. Łabęda-Grudziak

• Institute of Automatic Control and Robotics, Warsaw University of Technology, ul. św. Andrzeja Boboli 8, 02-525 Warsaw, Poland

Bibliografia

• Adam-Medina, M., Theilliol, D. and Sauter, D. (2003). Simultaneous fault diagnosis and robust model selection in multiple linear models framework, Proceedings of the 5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS, Washington, DC, USA, pp. 513-518.
• Chen, J. and Patton, R.J. (1999). Robust Model Based Fault Diagnosis for Dynamic Systems, Kluwer Academic Publishers, Boston, MA.
• Chen, R.H. and Speyer, J.L. (1999). Optimal stochastic multiple faults detection filter, Proceedings of the 38th IEEE Conference on Decision and Control, Phoenix, AL, USA, Vol. 5, pp. 4965-4970.
• Clark, R.N. (1989). State estimation schemes for instrument fault detection, in R.J. Patton, P.M. Frank and R.N. Clark (Eds.), Fault Diagnosis in Dynamic Systems: Theory and Application, Prentice Hall, London.
• Daigle, M., Koutsoukos, X. and Biswas, G. (2006). Multiple fault diagnosis in complex physical systems, 17th International Workshop on Principles of Diagnosis, Penaranda de Duero, Spain, pp. 69-76.
• de Kleer, J. and Kurien, J. (2003). Fundamentals of model-based diagnosis, Proceedings of the 5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS 2003, Washington, DC, USA, pp. 25-36.
• de Kleer, J. and Williams, B.C. (1987). Diagnosing multiple faults, Artificial Intelligence 32(1): 97-130.
• De-Persis, C. and Isidori, A. (2001). A geometric approach to nonlinear fault detection and isolation, IEEE Transactions on Automatic Control 46(6): 853-866.
• Ding, S.X. (2008). Model-based Fault Diagnosis Techniques, Springer, Berlin/Heidelberg.
• Frank, P.M. (1987). Fault diagnosis in dynamic systems via state estimations methods: A survey, in S.G. Tzafestas, M. Singh and G. Schmidt (Eds.), System Fault Diagnostics, Reliability and Related Knowledge-based Approaches, Vol. 2, D. Reidel Publishing Company, Dordrecht/Boston, MA/Lancaster/Tokyo.
• Frank, P.M. (1991). Enhancement of robustness in observer-based fault detection, Proceedings of the IFAC/IMACS Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS, Baden-Baden, Germany, pp. 275-288.
• Geltler, J. and Singer, D. (1990). A new structural framework for parity equation based failure detection and isolation, Automatica 26(2): 381-388.
• Gertler, J. (1998). Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker, Inc., New York, NY/Basel/Hong Kong.
• Górny, B. (2001). Consistency-Based Reasoning in ModelBased Diagnosis, Ph.D. thesis, AGH University of Science and Technology, Cracow.
• Hamscher, W., Console, L. and de Kleer, J. (1992). Readings in Model-Based Diagnosis, Morgan Kaufmann Publishers, San Mateo, CA.
• Hashtrudi, S. and Massoumnia, M. (1999). Generic solvability of the failure detection and identification problem, Automatica 35(5): 887-893.
• Hwee, T.N. (1991). Model-based, multiple-fault diagnosis of dynamic, continuous physical devices, IEEE Expert 6(6): 38-43.
• Isermann, R. (2006). Fault Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance, Springer-Verlag, New York, NY.
• Khémiri, K., Ben Hmida, F., Ragot, J. and Gossa, M. (2011). Novel optimal recursive filter for state and fault estimation of linear stochastic systems with unknown disturbances, International Journal of Applied Mathematics and Computer Science 21(4): 629-637, DOI: 10.2478/v10006-011-0049-3.
• Korbicz, J., Kościelny, J.M., Kowalczuk, Z. and Cholewa, W. (Eds.) (2004). Fault Diagnosis. Models, Artificial Intelligence, Applications, Springer, Berlin.
• Kościelny, J.M. (1995). Fault isolation in industrial processes by dynamic table of states method, Automatica 31(5): 747-753.
• Kościelny, J.M. (2001). Diagnostics of Automated Industrial Processes, Akademicka Oficyna Wydawnicza Exit, Warsaw, (in Polish). Double fault
• Kościelny, J.M. and Łabęda, Z.M. (2007). distinguishability in linear systems, 8th Conference on Diagnostics of Processes and Systems, Słubice, Poland, pp. 45-52, (in Polish).
• Kościelny, J.M., Bartyś, M. and Syfert, M. (2012). Method of multiple fault isolation in large scale systems, IEEE Transactions on Control Systems Technology 20(5): 1302-1310.
• Ligęza, A. and Kościelny, J.M. (2008). A new approach to multiple fault diagnosis: A combination of diagnostic matrices, graphs, algebraic and rule-based models. The case of two-layer models, International Journal of Applied Mathematics and Computer Science 18(4): 465-476, DOI: 10.2478/v10006-008-0041-8.
• Manders, E.J., Narasimhan, S., Biswas, G. and Mosterman, P. (2000). A combined qualitative/quantitative approach for fault isolation in continuous dynamic systems, 4th Symposium on Fault Detection, Supervision and Safety for Technical Processes, Budapest, Hungary, pp. 1074-1079.
• Mattone, R. and de Luca, A. (2006). Relaxed fault detection and isolation: An application to a nonlinear case study, Automatica 42(1): 109-116.
• Mosterman, P.J. and Biswas, G. (1999). Diagnosis of continuous valued systems in transient operating regions, IEEE Transactions on Systems, Man and Cybernetics, Part A 29(6): 554-565.
• Patton, R.J., Frank, P.M. and Clark, R.N. (2000). Issues of Fault Diagnosis for Dynamic Systems, Springer, Berlin.
• Sorsa, T. and Koivo, H.N. (1993). Application of artificial neural networks in process fault diagnosis, Automatica 29(4): 843-849.
• Staroswiecki, M., Cassar, J.P. and Declerck, P. (2000). A structural framework for the design of FDI system in large scale industrial plants, in R.J. Patton, P.M. Frank and R.N. Clark (Eds.), Issues of Fault Diagnosis for Dynamic Systems, Springer-Verlag, Berlin.
• Verde, C., Gentil, S. and Rosas, O. (2001). Fuzzy directional residuals evaluation for multileaks in pipelines, European Control Conference, Porto, Portugal, pp. 504-509.
• Watanabe, K. and Hirota, S. (1991). Incipient diagnosis of multiple faults in chemical process via hierarchical artificial neural networks: Industrial electronics, control and instrumentation, IECON International Conference, Kobe, Japan, Vol. 2, pp. 1500-1505.
• Watanabe, K. and Hou, L. (1992). An optimal neural network for diagnosing multiple faults in chemical processes. industrial electronics, control and instrumentation, Proceedings of the International Conference on Power Electronics and Motion Control, San Diego, CA, USA, Vol. 2, pp. 1068-1073.

Identyfikatory

DOI

10.2478/amcs-2013-0030