Multiple Faults Isolation for Hybrid Systems with Unknown Fault Pattern
This work is concerned with multiple faults isolation for hybrid systems based on Global Analytical Redundancy Relationships (GARRs) approach. GARRs are derived from the Hybrid Bond Graph (HBG) model of a hybrid system with a specified causality assignment procedure. In this article, multiple faults are considered in a complex hybrid system and these faults can develop during a mode when the faults are not detectable. Once a fault is detected, a fault candidates set is generated from mode dependent-fault signature matrix (MD-FSM) tables and a set of fault pattern hypothesis is created from the fault candidates set for further refinement. Fault isolation is carried out using a multiple nonlinear least square optimization (MNLSO) algorithm. The developed technique can deal with multiple faults with unknown pattern. The fault could be of incipient or abrupt nature. The simulation results show the effectiveness of the proposed method.
How to Cite
K. Medjaher, A. K. Samantaray, B. Ould Bouamama &
M. Staroswiecki (2006), Supervision of an Industrial Steam Generator: Part II. Online Implementation, Control Engineering Practice, vol.14, no.1, pp. 85-
X. Koutsoukos, F. Zhao, H. Haussecker, J. Reich & P. Cheung (2001), Fault Modeling for Monitoring and Diagnosis of Sensor-Rich Hybrid Systems, in Proc. IEEE Conf. Decision Control, pp. 793-801.
V. Cocquempot, T. E. Mezyani & M. Staroswiecki (2004), Fault Detection and Isolation for Hybrid Systems Using Structured Parity Residuals, in Proc. 5th Asian Control Conf., vol. 2, pp. 1204 -1212.
C. B. Low, D. Wang, S. Arogeti & J. B. Zhang, Quantitative Hybrid Bond Graph-based Fault Detection and Isolation, IEEE Trans. Autom. Sci. Eng., vol.7, no.3, pp. 558- 569, 2010.
S. Arogeti, D. Wang & C. B. Low (2010), Mode Identification of Hybrid Systems in Presence of Fault, IEEE Trans. Ind. Electron., vol. 57, no. 4, pp.14521467.
M. Yu, M. Luo, S. Arogeti, D.Wang & X. Z. Zhang (2010). Simultaneous Fault and Mode Switching Identification for Hybrid Systems based on Particle Swarm Optimization, Expert systems with applications. vol.37, no.4, pp: 3000-3012.
M. Yu, D.Wang, M. Luo & L. Huang. Prognosis of Hybrid Systems with Multiple Iincipient Faults: Augmented Global Analytical Redundancy Relations Approach. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans. to be published.
A. K. Samantaray & B. Ould Bouamama (2008), Model-based Process Supervision: A Bond Graph Approach, Springer, London.
C. B. Low, D.Wang, S. Arogeti & J. B. Zhang (2008), Causality Assignment and Modeling Approximation for Quantitative Hybrid Bond Graph Fault Diagnosis, The 17th IFAC World Congress, Seoul, Korea, pp. 10522-10527.
D.C. Karnopp, D.L. Margolis & R.C. Rosenberg (2006), System Dynamics, Modeling and Simulation of Mechatronics Systems, New Jersey: John Wiley & Sons Inc.
S. Narasimhan and G. Biswas (2007), Model-based Diagnosis of Hybrid Systems, IEEE Trans. on Systems, Man, and Cybernetics, Part A: Systems and Humans, vol. 37, no. 3, pp. 348-361.
M. Daigle, X. Koutsoukos, and G. Biswas (2007), A Qualitative Approach to Multiple Fault Isolation in Continuous Systems, AAAI National Conference (AAAI-2007), pp. 293-298.
P.J. Mosterman and G. Biswas (1998), A Theory of Discontinuities in Physical System Models, Journal of the Franklin Institute: Engineering and Applied Mathematics, vol. 335B, no. 3, pp. 401-439.
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