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From structural analysis to observer-based residual generation for fault detection

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Języki publikacji
EN
Abstrakty
EN
This paper combines methods for the structural analysis of bipartite graphs with observer-based residual generation. The analysis of bipartite structure graphs leads to over-determined subsets of equations within a system model, which make it possible to compute residuals for fault detection. In observer-based diagnosis, by contrast, an observability analysis finds observable subsystems, for which residuals can be generated by state observers. This paper reveals a fundamental relationship between these two graph-theoretic approaches to diagnosability analysis and shows that for linear systems the structurally over-determined set of model equations equals the output connected part of the system. Moreover, a condition is proved which allows us to verify structural observability of a system by means of the corresponding bipartite graph. An important consequence of this result is a comprehensive approach to fault detection systems, which starts with finding the over-determined part of a given system by means of a bipartite structure graph and continues with designing an observer-based residual generator for the fault-detectable subsystem found in the first step.
Rocznik
Strony
233--245
Opis fizyczny
Bibliogr. 24 poz., rys., wykr.
Twórcy
autor
  • Robert Bosch GmbH, Robert-Bosch-Campus 1, D-71272 Renningen, Germany
autor
  • Institute of Automation and Computer Control, Ruhr University Bochum, Universitätsstraße 150, D-44801 Bochum, Germany
  • Robert Bosch GmbH, Robert-Bosch-Campus 1, D-71272 Renningen, Germany
Bibliografia
  • [1] Armengol, J., Bregón, A., Escobet, T., Gelso, E., Krysander, M., Nyberg, M., Olive, X., Pulido, B. and Travé-Massuyès, L. (2009). Minimal structurally overdetermined sets for residual generation: A comparison of alternative approaches, 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Barcelona, Spain, pp. 1480–1485.
  • [2] Blanke, M., Kinnaert, M., Lunze, J. and Staroswiecki, M. (2016). Diagnosis and Fault-Tolerant Control, 3rd Edn., Springer, Berlin.
  • [3] Boukhobza, T., Hamelin, F. and Sauter, D. (2006). Observability of structured linear systems in descriptor form: A graph-theoretic approach, Automatica 42(4): 629–635.
  • [4] Boukhobza, T., Hamelin, F. and Martinez-Martinez, S. (2007). State and input observability for structured linear systems: A graph-theoretic approach, Automatica 43(7): 1204–1210.
  • [5] Bregon, A., Alonso-Gonzalez, C.J. and Pulido, B. (2014). Integration of simulation and state observers for online fault detection of nonlinear continuous systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems 44(12): 1553–1568.
  • [6] Commault, C. and Dion, J.M. (2007). Sensor location for diagnosis in linear systems: A structural analysis, IEEE Transactions on Automatic Control 52(2): 155–169.
  • [7] De Persis, C. and Isidori, A. (2001). A geometric approach to nonlinear fault detection and isolation, IEEE Transactions on Automatic Control 46(6): 853–865.
  • [8] Ding, S.X. (2013). Model-Based Fault Diagnosis Techniques, 2nd Edn., Springer, Berlin.
  • [9] Dulmage, A.L. and Mendelsohn, N.S. (1958). Coverings of bipartite graphs, Canadian Journal of Mathematics 10: 517–534.
  • [10] Frisk, E., Bregon, A., Aslund, J., Krysander, M., Pulido, B. and Biswas, G. (2012). Diagnosability analysis considering causal interpretations for differential constraints, IEEE Transactions on Systems, Man and Cybernetics A: Systems and Humans 42(5): 1216–1229.
  • [11] Isermann, R. (2006). Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, Springer, Berlin.
  • [12] Krysander, M. (2006). Design and Analysis of Diagnosis Systems Using Structural Methods, PhD thesis, Linköpings University, Linköpings.
  • [13] Krysander, M. and Frisk, E. (2008). Sensor placement for fault diagnosis, IEEE Transactions on Systems, Man and Cybernetics A: Systems and Humans 38(6): 1398–1410.
  • [14] Krysander, M., Aslund, J. and Nyberg, M. (2008). An efficient algorithm for finding minimal overconstrained subsystems for model-based diagnosis, IEEE Transactions on Systems, Man and Cybernetics A: Systems and Humans 38(1): 197–206.
  • [15] Lin, C.-T. (1974). Structural controllability, IEEE Transactions on Automatic Control 19(3): 201–208.
  • [16] Lunze, J. (2013). Regelungstechnik 2, 7th Edn., Springer, Berlin.
  • [17] Lunze, J. (2017). Two methods to find analytical redundancy relations for fault diagnosis, Automatisierungstechnik 65(4): 219–232.
  • [18] Massoumnia, M.-A., Verghese, G.C. and Willsky, A. (1989). Failure detection and identification, IEEE Transactions on Automatic Control 34(3): 316–321.
  • [19] Murota, K. (1987). Systems Analysis by Graphs and Matroids, Springer, Berlin.
  • [20] Pothen, A. and Fan, C.-J. (1990). Computing the block triangular form of a sparse matrix, ACM Transactions on Mathematical Software 16(4): 303–324.
  • [21] Pröll, S., Jarmolowitz, F. and Lunze, J. (2015). Structural diagnosability analysis of switched systems, 9th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Paris, France, pp. 156–163.
  • [22] Reinschke, K.J. (1988). Multivariable Control, Akademie-Verlag, Berlin.
  • [23] Shields, R. and Pearson, J. (1976). Structural controllability of multi-input linear systems, IEEE Transactions on Automatic Control 21(2): 203–212.
  • [24] Svärd, C. and Nyberg, M. (2010). Residual generators for fault diagnosis using computation sequences with mixed causality applied to automotive systems, IEEE Transactions on Systems, Man and Cybernetics A: Systems and Humans 40(6): 1310–1328.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b68f9d0e-9183-49bd-a6ad-05f718d04ec2
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