This work studies potential ways of integration of two techniques for fault detection, isolation, and identification in dynamic systems: Lydia-NG suite of diagnosis algorithms and Consistency-based Diagnosis with Possible Conflicts. By integrating both techniques, Lydia- NG will benefit from a more efficient fault detection and isolation task, and Possible Conflicts will benefit from the identification capabilities of Lydia-NG. In this paper, we define a common framework that integrates both techniques, and then we apply the proposed integrated approach to a three-tank system, and draw some conclusions about potential ways of integration.
How to Cite
Diagnosis and fault isolation methods, Model-based diagnosis
Armengol, J., Bregon, 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 (p. 1480- 1485). Barcelona, Spain.
Bayoudh, M., Trav ́e-Massuy`es, L., & Oliv ́e, X. (2008). Coupling Continuous and Discrete Event System Techniques for Hybrid System Diagnosability Analysis. In Proc. of the 18th European Conf. on Artificial Intelligence, ECAI08 (pp. 219–223). Amsterdam, The Netherlands.
Bregon, A., Alonso, C., Biswas, G., Pulido, B., & Moya, N. (2012). Fault Diagnosis in Hybrid Systems using Possible Conficts. In Proc. of Safeprocess’12. Mexico City, Mexico.
Bregon, A., Biswas, G., & Pulido, B. (2012). A Decomposition Method for Nonlinear Parameter Estima- tion in TRANSCEND. IEEE Trans. Syst. Man. Cy. Part A, 42(3), 751-763.
Cordier, M., Dague, P., L ́evy, F., Montmain, J., Staroswiecki, M., & Trav ́e-Massuy`es, L. (2004). Conflicts versus Analytical Redundancy Relations: a comparative analysis of the Model-
based Diagnosis approach from the Artificial Intelligence and Automatic Control perspectives. IEEE Trans. on Systems, Man, and Cybernetics. Part B: Cybernetics, 34(5), 2163-2177.
Daigle, M., Bregon, A., & Roychoudhury, I. (2012, aug). Qualitative Event-based Diagnosis with Possible Conflicts Applied to Spacecraft Power Distribution Systems. In Proceedings of the 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (p. 265-270).
Daigle, M., Roychoudhury, I., Biswas, G., Koutsoukos, X., Patterson-Hine, A., , et al. (2010, Septem- ber). A Comprehensive Diagnosis Methodology for Complex Hybrid Systems: A Case Study on Spacecraft Power Distribution Systems. IEEE Transactions of Systems, Man, and Cybernetics, Part A,4(5), 917–931.
Daigle, M., Roychoudhury, I., Biswas, G., Koutsoukos,X., Patterson-Hine, A., & Poll, S. (2010). A comprehensive diagnosis methodology for complex hy- brid systems: A case study on spacecraft power distribution systems. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 40(5), 917–931.
Dressler, O. (1996). On-line diagnosis and monitoring of dynamic systems based on qualitative models and dependency-recording diagnosis engines. In Proceedings of the Twelfth European Conference on Artificial Intelligence, ECAI-96 (p. 461-465).
Feldman, A., Castro, H. V. de, Gemund, A. van, & Provan, G. (2013). Model-Based Diagnostic decision-support system for satellites. In Aerospace Conference, 2013 IEEE (pp. 1–14).
Feldman, A., Provan, G., & Gemund, A. van. (2010). Approximate Model-Based Diagnosis Using Greedy Stochastic Search. Journal of Artificial Intelligence Research, 38, 371–413.
Gertler, J. (1998). Fault Detection and Diagnosis in Engineering Systems. Marcel Dekker, Inc.
Hofbaur, M., & Williams, B. (2004, Oct.). Hybrid estimation of complex systems. IEEE T. Syst. Man. Cy. Part B, 34(5), 2178 -2191.
Isermann, R. (2006). Fault-Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance. Springer.
Keogh, E., & Ratanamahatana, C. (2005). Exact indexing of dynamic time warping. Knowledge and Information Systems, 7(3), 358-386.
Kleer, J. de, & Williams, B. C. (1987). Diagnosing multiple faults. Artificial Intelligente, 32, 97-130.
Pulido, B., & Alonso, C. (2001). Dealing with cyclical
configurations in MORDRED. In IX Conferencia Nacional de la Asociacion Espan ̃ola de Inteligen- cia Artificial, (CAEPIA-01) (p. 983-992). Gijon, Spain.
Pulido, B., Alonso, C., & Acebes, F. (2001). Lessons learned from diagnosing dynamic systems using possible conflicts and quantitative models. In Engineering of Intelligent Systems. XIV Conf. IEA/AIE-2001 (Vol. 2070, p. 135-144). Budapest, Hungary.
Pulido, B., & Alonso-Gonz ́alez, C. (2004, Octubre). Possible Conflicts: a compilation technique for consistency-based diagnosis. IEEE Trans. on Systems, Man, and Cybernetics. Part B: Cybernetics, 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.
Pulido, B., Zamarren ̃o, J., Merino, A., & Bregon, A. (2012, July). Using structural decomposition methods to design gray-box models for fault diagnosis of complex systems: a beet sugar factory case study. In A. Bregon & A. Saxena (Eds.), Procs. of the First European Conference of the Prognostics and Health Management Society (p. 225-238). Dresden, Germany.
Reiter, R. (1987). A Theory of Diagnosis from First Principles. Artificial Intelligence, 32, 57-95.
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