An Efficient Model-based Diagnosis Engine for Hybrid Systems using Structural Model Decomposition
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, and embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) architecture offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. How- ever, HyDE faces some problems regarding performance in terms of time and space complexity. This paper focuses on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic benchmark problem, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.
How to Cite
##plugins.themes.bootstrap3.article.details##
fault diagnosis, Model-based diagnosis
Bayoudh, M., Trave-Massuyes, L., & Olive, X. (2009). On-line analytic redundancy relations instantiation guided by component discrete-dynamics for a class of non-linear hybrid systems. In Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on (p. 6970- 6975).
Benazera, E., & Trav ́e-Massuy`es, L. (2009, October). Set-theoretic estimation of hybrid system configurations. Trans. Sys. Man Cyber. Part B, 39, 1277– 1291.
Bregon, A., Alonso, C., Biswas, G., Pulido, B., & Moya, N. (2012). Fault Diagnosis in Hybrid Systems using Possible Conficts. In Proc. of Safeprocess’2012. Mexico City, Mexico.
Bregon, A., Biswas, G., & Pulido, B. (2012, May). A Decomposition Method for Nonlinear Parameter Estimation in TRANSCEND. IEEE Trans. on Systems, Man, and Cybernetics, Part A: Systems and Humans, 42(3), 751-763.
Cocquempot, V., El Mezyani, T., & Staroswiecki, M. (2004, july). Fault detection and isolation for hybrid systems using structured parity residuals. In Control Conference, 2004. 5th Asian (Vol. 2, p. 1204 - 1212 Vol.2).
Daigle, M., Bregon, A., & Roychoudhury, I. (2011a, September). Distributed Damage Estimation for Prognostics Based on Structural Model Decomposition. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2011 (p. 198-208).
Daigle, M., Bregon, A., & Roychoudhury, I. (2011b, Oct). Qualitative Event-based Diagnosis with Pos- sible Conflicts: Case Study on the Third Interna- tional Diagnostic Competition. In Proceedings of the 22nd International Workshop on Principles of
Diagnosis (p. 285-292). Murnau, Germany. Daigle, M., Bregon, A., & Roychoudhury, I. (2012, September). A Distributed Approach to System- Level Prognostics. In Annual Conference of the Prognostics and Health Management Society 2012(p. 71-82).
Daigle, M., & Roychoudhury, I. (2010, October). Qualitative Event-based Diagnosis: Case Study on the Second International Diagnostic Competition. In Proceedings of the 21st International Workshop on Principles of Diagnosis (pp. 371–378).
Hofbaur, M., & Williams, B. (2004, oct.). Hybrid estimation of complex systems. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 34(5), 2178 -2191.
̊
Krysander, M., Aslund, J., & Nyberg, M. (2008).An Efficient Algorithm for Finding Minimal Over- constrained Sub-systems for Model-based Diagnosis. IEEE Trans. on Systems, Man, and Cybernetics, Part A, 38(1).
Mosterman, P. J., & Biswas, G. (1999). Diagnosis of continuous valued systems in transient operating regions. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 29(6), 554-565.
Moya, N., Pulido, B., Alonso-Gonza ́lez, C., Bregon, A., & Rubio, D. (2012). Automatic generation of Dy- namic Bayesian Networks for hybrid systems fault diagnosis. In Proceeding of Intl. Workshop on principles of Diagnosis, DX, 2012. Great Malvern, U.K..
Narasimhan, S., & Biswas, G. (2007, may). Model-Based Diagnosis of Hybrid Systems. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 37(3), 348 -361.
Narasimhan, S., & Brownston, L. (2007, May 29-31). HyDE - A General Framework for Stochastic and Hybrid Model-based Diagnosis. In Proceedings of the 18th International Workshop on Principles of Diagnosis, DX07 (p. 186-193). Nashville, TN, USA.
Poll, S., de Kleer, J., Abreau, R., Daigle, M., Feldman, A., Garcia, D., et al. (2011, October). Third International Diagnostics Competition – DXC’11. In Proc. of the 22nd International Workshop on Principles of Diagnosis (pp. 267–278).
Poll, S., et al. (2007, May). Evaluation, Selection, and Application of Model-Based Diagnosis Tools and Approaches. In AIAA Infotech@Aerospace 2007 Conference and Exhibit.
Pulido, B., & Alonso-Gonza ́lez, C. (2004). Possible Conflicts: a compilation technique for consistency- based diagnosis. IEEE Trans. on Systems, Man, and Cybernetics, Part B, Special Issue on Diagnosis of Complex Systems, 34(5), 2192-2206.
Rienmuller, T., Hofbaur, M., Trave-Massuyes, L., & Bayoudh, M. (2013, March). Mode set focused hybrid estimation. International Journal of Ap- plied Mathematics and Computer Science , 23 (1),131-144.
Roychoudhury, I., Daigle, M., Bregon, A., & Pulido, B.(2013, March). A Structural Model Decomposition Framework for Systems Health Management. In Proceedings of the 2013 IEEE Aerospace Conference.
Sampath, M., Sengputa, R., Lafortune, S., Sinnamo- hideen, K., & Teneketsis, D. (1995). Diagnosability of discrete-event systems. IEEE Transactions on Automatic Control.
Staroswiecki, M., & Declerck, P. (1989, July). Analytical redundancy in nonlinear interconnected systems by means of structural analysis. In IFAC Symp. on Advanced Information Processing in Automatic Control.
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.