Efficient Generation of Minimal Dynamic Bayesian Networks for Hybrid Systems Fault Diagnosis using Hybrid Possible Conflicts
Hybrid systems diagnosis requires different sets of equations for each operation mode in order to estimate the continuous system behaviour. In this work we rely upon Hybrid Possible Conflicts (HPCs), which are an extension of Possible Conflicts (PCs) for hybrid systems, that introduce the information about potential system modes as control specifications that activate/deactivate different sets of equations. We also introduce the concept of Hybrid Minimal Evaluation Models (H-MEMs) to represent the set of globally consistent causal assignments in an HPC for any potential mode.
H-MEMs can be explored for a specific operation mode, and its computational model automatically generated. In this work, the selected computational models are minimal Dynamic Bayesian Networks (DBNs). Since DBNs can be directly generated from PCs, and can be used for fault detection and isolation, we propose to efficiently generate Mini- mal DBNs models on-line using the H-MEM structure. By introducing fault parameters in the DBN model, we can also perform fault identification, providing an unifying framework for fault diagnosis, under single fault assumption. We test the approach in a simulation four-tank system.
How to Cite
fault diagnosis, Hybrid Systems, Model-based diagnosis, Dynamic Bayesian Network, Possible Conflicts
Advances in AI (pp. 223–232). Berlin: Springer-Verlag. Available from http://dl.acm.org/citation.cfm?- id=2075561.2075588
Armengol, J., Brego ́n, A., Escobet, T., Gelso, E., Krysander, M., Nyberg, M., et al. (2009). Minimal Structurally Overdetermined sets for residual generation: A comparison of alternative approaches. In Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS09 (pp. 1480–1485).
Bayoudh, M., Trave ́-Massuye`s, L., & Olive ́, X. (2009). Di- agnosis of a Class of Non Linear Hybrid Systems by On-line Instantiation of Parameterized Analytical Redundancy Relations. In Proc. of the XX Intl. Workshop on Principles of Diagnosis, DX’09 (p. 283-289). Stock- holm, Sweden.
Bregon, A., Alonso, C., Biswas, G., Pulido, B., & Moya, N. (2012). Fault Diagnosis in Hybrid Systems using Possible Conflicts. In Proc. of the 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Safeprocess’12. Mexico City, Mexico.
Bregon, A., Biswas, G., & Pulido, B. (2012). A Decomposition Method for Nonlinear Parameter Estimation in TRANSCEND. IEEE Trans. Syst. Man. Cy. Part A, 42(3), 751-763.
Bregon, A., Pulido, B., Biswas, G., & Koutsoukos, X. (2009). Generating Possible Conflicts from Bond Graphs Using Temporal Causal Graphs. In Proceedings of the 23rd European Conference on Modelling and Simulation, ECMS’09 (p. 675-682). Madrid, Spain.
Cocquempot, V., El Mezyani, T., & Staroswiecki, M. (2004, Jul.). Fault detection and isolation for hybrid systems using structured parity residuals. In Proceedings of the 5th Asian Control Conference.
Hofbaur, M., & Williams, B. (2004, Oct.). Hybrid estimation of complex systems. IEEE T. Syst. Man. Cy. Part B, 34(5), 2178 -2191.
Karnopp, D., Margolis, D., & Rosenberg, R. (2006). System Dynamics: Modeling and Simulation of Mechatronic Systems. New York, NY, USA: John Wiley & Sons, Inc.
Koller, D., & Lerner, U. (2001). Sequential Monte Carlo Methods in Practice. In N. d. F. A. Doucet & N. Gordon (Eds.), (chap. Sampling in factored dynamic systems). Springer.
Koutsoukos, X., Kurien, J., & Zhao, F. (2003). Estimation of Distributed Hybrid Systems Using Particle Filtering Methods. In Proc. of the Intl. Workshop on Hybrid Systems: Computation and Control, HSCC’03 (p. 298- 313). Springer.
Moya, N., Bregon, A., Alonso-Gonza ́lez, C., & Pulido, B. (2013). A Common Framework for Fault Diagnosis of Parametric and Discrete Faults using Possible Con- flicts. In Proc. of the 15th Intl. Conf. on Advances in AI (pp. 239–249). Madrid, Spain: Springer-Verlag, Berlin.
Moya, N., Bregon, A., Alonso-Gonza ́lez, C., Pulido, B., & Biswas, G. (2012, Jul-Aug). Automatic Generation of Dynamic Bayesian Networks for Hybrid Systems Fault Diagnosis. In Proc. of the XXIII Intl. Workshop on Principles of Diagnosis, DX’12. Great Malvern, UK.
Murphy, K. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. Unpublished doctoral dissertation, University of California, Berkeley.
Narasimhan, S., & Biswas, G. (2007, May). Model-Based Diagnosis of Hybrid Systems. IEEE T. Syst. Man Cy. Part A, 37(3), 348-361.
Narasimhan, S., & Brownston, L. (2007). HyDE: A General Framework for Stochastic and Hybrid Model-based Diagnosis. In Proc. the XVIII Intl. Workshop on Principles of Diagnosis, DX’07 (p. 186-193). Nashville, USA.
Podgursky, B., G. B., & Koutsoukos, X. (2010, Oct). Efficient Tracking of Behavior in Complex Hybrid Systems via Hybrid Bond Graphs. In Proc. of the Prognostics and Health Management Conf., PHM’10. Portland, OR, USA.
Pulido, B., & Alonso, C. (2001). Dealing with cyclical configurations in (MORDRED). In Proc. of the 9th Intl. Conf. on Advances in AI, CAPIA’01 (p. 983-992). Gi- jon, Spain.
Pulido, B., & Alonso-Gonza ́lez, C. (2004, Oct.). Possi- ble Conflicts: a compilation technique for consistency- based diagnosis. IEEE Trans. Sys. Man Cy. Part B, 34(5), 2192-2206.
Pulido, B., Bregon, A., & Alonso-Gonza ́lez, C. (2010). analyzing the influence of differential constraints in Possible Conflicts and ARR Computation. In P. Meseguer, L. Mandow, & R.
Gasca (Eds.), Current Topics in Artificial Intelligence (Vol. 5988, p. 11-21). Springer Berlin.
Rienmu ̈ller, T., Bayoudh, M., Hofbaur, M., & Trave ́- Massuye`s, L. (2009). Hybrid Estimation through Synergic Mode-Set Focusing. In Proc. of 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Safeprocess’09 (p. 1480-1485). Barcelona, Spain.
Roychoudhury, I., Biswas, G., & Koutsoukos, X. (2008, Sept.). Comprehensive Diagnosis of Continuous systems Using Dynamic Bayes Nets. In Proc. of XIX Intl. Workshop on Principles of Diagnosis, DX’08. Blue Mountains, Australia.
Roychoudhury, I., Daigle, M., Biswas, G., & Koutsoukos, X. (2011, June). Efficient simulation of hybrid systems: A hybrid bond graph approach. SIMULATION: Transactions of the Society for Modeling and Simulation International(6), 467-498.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.