PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2011 | 21 | 1 | 109-125
Tytuł artykułu

Algebraic approach for model decomposition: Application to fault detection and isolation in discrete-event systems

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a constrained decomposition methodology with output injection to obtain decoupled partial models. Measured process outputs and decoupled partial model outputs are used to generate structured residuals for Fault Detection and Isolation (FDI). An algebraic framework is chosen to describe the decomposition method. The constraints of the decomposition ensure that the resulting partial model is decoupled from a given subset of inputs. Set theoretical notions are used to describe the decomposition methodology in the general case. The methodology is then detailed for discrete-event model decomposition using pair algebra concepts, and an extension of the output injection technique is used to relax the conservatism of the decomposition.
Rocznik
Tom
21
Numer
1
Strony
109-125
Opis fizyczny
Daty
wydano
2011
otrzymano
2009-12-02
poprawiono
2010-07-19
Twórcy
  • Automatic Control Laboratory, LAMIH, University of Valenciennes (UVHC), Bgt. Malvache, Le Mont Houy, 59313 Valenciennes cedex 9, France
  • Automatic Control Laboratory, LAGIS, Polytech'Lille, Lille 1 University, 59655 Villeneuve d'Ascq, France
  • Automatic Control Laboratory, LAGIS, Polytech'Lille, Lille 1 University, 59655 Villeneuve d'Ascq, France
  • Institute for Marine Technology Problems, Far Eastern Branch of the Russian Academy of Sciences, 5a Sukhanova St, Vladivostok, 690600, Russia
  • Institute for Marine Technology Problems, Far Eastern Branch of the Russian Academy of Sciences, 5a Sukhanova St, Vladivostok, 690600, Russia
Bibliografia
  • Bavishi, S. and Chong, E. (1994). Automated fault diagnosis using a discrete event systems framework, IEEE Symposium on Intelligent Control, Columbus, OH, USA, pp. 213-218.
  • Benveniste, A., Fabre, E., Haar, S. and Jard, C. (2003). Diagnosis of asynchronous discrete event systems: A net unfolding approach, IEEE Transactions of Automatic Control 48(5): 714-727.
  • Berdjag, D., Christophe, C. and Cocquempot, V. (2006a). An algebraic method for nonlinear system decomposition, 6th IFAC Symposium on Fault Detection Supervision ans Safety for Technical Processes, SAFEPROCESS'2006, Beijing, China, pp. 42-53.
  • Berdjag, D., Christophe, C. and Cocquempot, V. (2006b). Nonlinear model decomposition for fault detection and isolation system design, 45th IEEE Conference on Decision and Control, San Diego, CA, USA, pp. 3321-3326.
  • Berdjag, D., Christophe, C., Cocquempot, V. and Jiang, B. (2006c). Nonlinear model decomposition for robust fault detection and isolation using algebraic tools, International Journal of Innovative Computing, Information and Control 2(6): 1337-1353.
  • Blanke, M., Kinnaert, M., Lunze, J. and Staroswiecki, M. (2003). Diagnosis and Fault-Tolerant Control, Springer, Berlin.
  • Boel, R. and Jiroveanu, G. (2004). Distributed contextual diagnosis for very large systems, International Workshop on Discrete Event Systems, Reims, France, pp. 343-348.
  • Boubour, R., Jard, C., Aghasaryan, A., Fabre, E. and Benveniste, A. (1997). A Petri net approach to fault detection and diagnosis in distributed systems (Parts 1 and 2), IEEE 36th International Conference on Decision and Control, San Diego, CA, USA, pp. 720-731.
  • Chow, E. and Willsky, A. (1984). Analytical redundancy and the design of robust failure detection systems, IEEE Transactions on Automatic Control 29(7): 603-614.
  • Cox, D., Little, J. and O'Shea, D. (1991). Ideals, Varieties, and Algorithms, Springer-Verlag, New York, NY.
  • Diop, S. (1991). Elimination in control theory, Mathematics of Control, Signals, and Systems 4: 17-32.
  • Fliess, M. and Join, C. (2003). An algebraic approach to fault diagnosis for linear systems, Proceedings of the International Conference on Computational Engineering in System Applications, CESA, Lille, France, pp. 1-9.
  • Fliess, M., Join, C. and Sira-Ramírez, H. (2004). Robust residual generation for linear fault diagnosis: An algebraic setting with examples, International Journal of Control 77(20): 1223-1242.
  • Gertler, J. (1991). Analytical redundancy methods in fault detection and isolation-Survey and synthesis, Proceedings of the 1st IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS'91, Baden Baden, Germany, Vol. 1, pp. 9-21.
  • Gertler, J.J. (1998). Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker, New York, NY.
  • Giua, A. (1997). Petri net state estimators based on event observation, Proceedings of the 36th IEEE International Conference on Decision and Control, San Diego, CA, USA, pp. 4086-4091.
  • Hadjicostis, C. and Verghese, G. (1999). Monitoring discrete event systems using Petri net embeddings, in S. Donatelli and J. Kleijn (Eds.), Application and Theory of Petri Nets, Lecture Notes in Computer Science, Vol. 1639, Springer Verlag, Berlin/Heidelberg, pp. 188-207, DOI: 10.1007/3540-48745-X_12.
  • Hammouri, H., Kinnaert, M. and El Yaagoubi, E. (2001). A geometric approach to fault detection and isolation for bilinear systems, IEEE Transactions on Automatic Control 46(9): 1451-1455.
  • Hamscher, W., Console, L. and Kleer, J.D. (1992). Readings in Model-Based Diagnosis, Morgan Kaufmann Publishers, San Mateo, CA.
  • Hartmanis, J. and Stearns, R. (1966). The Algebraic Structure Theory of Sequential Machines, Prentice-Hall, New York, NY.
  • Hillston, J. (1996). A Compositional Approach to Performance Modelling, Cambridge University Press, Cambridge.
  • Isermann, R. (1984). Process fault-detection based on modelling and estimation methods-A survey, Automatica 20(4): 387-404.
  • Isermann, R. (2005). Model-based filt detection and analysis - Status and application, Annual Reviews in Control 29(1): 71-85.
  • Isermann, R. and Freyermuth, B. (1991). Process fault diagnosis based on process model knowledge, Part 1: Principles for fault diagnosis with parameter estimation, Transactions of the American Society of Mechanical Engineers 113(4): 620-626.
  • Isidori, A. (1995). Nonlinear Control Systems, 3rd Edn., Springer-Verlag, Berlin.
  • Jiang, B., Staroswiecki, M. and Cocquempot, V. (2004). Fault diagnosis based on adaptive observer for a class of nonlinear systems with unknown parameters, International Journal of Control 77(4): 415-426.
  • Jiang, B., Staroswiecki, M. and Cocquempot, V. (2006). Fault accommodation for nonlinear dynamic systems, IEEE Transactions on Automatic Control 51(9): 1578-1583.
  • Kinnaert, M. (1999). Robust fault detection based on observers for bilinear systems, Automatica 35(11): 1829-1842.
  • Lafortune, S., Teneketzis, D., Sengupta, R., Sampath, M. and Sinnamohideen, K. (2001). Failure diagnosis of dynamic systems: An approach based on discrete event systems, Proceedings of the American Control Conference, Arlington, VA, USA, pp. 2058-2071.
  • Lefebvre, D. (1999). Failure detection and isolation for manufacturing systems, Revue internationale d'ingenierie des systemes de production mecanique 2: V.33-V.44.
  • Leuschen, M., Walker, I. and Cavallaro, J. (2005). Fault residual generation via nonlinear analytical redundancy, IEEE Transactions on Control Systems Technology 13(3): 452-458.
  • Lin, F. (1994). Diagnosability of discrete event systems and its applications, Discrete Event Dynamic Systems 4(2): 197-212, DOI: 10.1007/BF01441211.
  • Lootsma, T. (2001). Observer-based Fault Detection and Isolation for Nonlinear Systems, Ph.D. thesis, Aalborg University, Aalborg.
  • Maquin, D., Cocquempot, V., Cassar, J., Staroswiecki, M. and Ragot, J. (1997). Generation of analytical redundancy relations for FDI purposes, IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED'97, Carry-le Rouet, France, pp. 270-276.
  • Maquin, D., Luong, M. and Ragot, J. (1997). Fault detection and isolation and sensor network design, Journal européen des systèmes automatisés 31(2): 393-406.
  • Patton, R. (1994). Robust model-based fault diagnosis: The state of the art, Proceedings of the 2nd IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes, SAFEPROCESS'94, Espoo, Finland, Vol. 1, pp. 1-24.
  • Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K. and Teneketzis, D. (1995). Diagnosability of discreteevent systems, IEEE Transactions on Automatic Control 40(9): 1555-1575.
  • Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K. and Teneketzis, D. (1996). Failure diagnosis using discrete-event models, IEEE Transactions on Control Systems Technology 4(2): 105-124.
  • Shumsky, A. (1991). Fault isolation in nonlinear dynamic systems by functional diagnosis, Automation and Remote Control 12: 148-155.
  • Shumsky, A. (2007). Redundancy relations for fault diagnosis in nonlinear uncertain systems, International Journal of Applied Mathematics and Computer Science 17(4): 477-489, DOI: 10.2478/v10006-007-0040-1.
  • Shumsky, A. and Zhirabok, A. (2006). Nonlinear diagnostic filter design: Algebraic and geometric points of view, International Journal of Applied Mathematics and Computer Science 16(1): 115-127.
  • Staroswiecki, M. and Comtet-Varga, G. (2001). Analytical redundancy relations for fault detection and isolation in algebraic dynamic systems, Automatica 37(5): 687-699.
  • Vereshchagin, N. and Shen, A. (2002). Basic Set Theory, Student Mathematical Library, Vol 17, American Mathematical Society, Providence, RI.
  • Zad, H., Kwong, R. and Wonham, W. (2003). Fault diagnosis in discrete-event systems: Framework and model reduction, IEEE Transactions on Automatic Control 48(7): 1199-1212.
  • Zad, S.H. (1999). Fault Diagnosis in Discrete-event and Hybrid Systems, Ph.D. thesis, University of Toronto, Toronto.
  • Zhirabok, A. (2006). Nonlinear dynamic systems: Their canonical decomposition based on invariant functions, Automation and Remote Control 67(4): 517-528.
  • Zhirabok, A. and Shumsky, A. (1993). A new mathematical techniques for nonlinear systems research, Proceedings of the 12th IFAC World Congress, Sydney, Australia, pp. 485-488.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv21i1p109bwm
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.