A Qualitative Fault Isolation Approach for Parametric and Discrete Faults Using Structural Model Decomposition
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
With increasing complexity of engineering systems, fault diagnostics plays a significant role in ensuring that they operate safely. Such systems most often exhibit mixed discrete and continuous, i.e., hybrid, behavior, and may encounter both parametric faults (unexpected changes in system parameters) as well as discrete faults (unexpected changes in component modes). Diagnosis becomes computationally very complex due to the large number of possible system modes, and possible mode changes that occur near the point of fault occurrence. This paper presents a qualitative fault isolation framework for integrated diagnosis of both parametric and discrete faults in hybrid systems, based on structural model decomposition. Fault isolation is performed by analyzing the qualitative information of the residual deviations, and considering observation delay. The great advantage of structural model decomposition for this problem is that it essentially defines several smaller independent diagnosis problems that become more efficient to solve, and makes the overall diagnosis problem more scalable. To demonstrate and test the validity of
our approach, we use a hydraulic multi-tank system as the case study in simulation. Results illustrate that the approach is both efficient and scalable.
How to Cite
##plugins.themes.bootstrap3.article.details##
fault diagnosis, Hybrid Systems, discrete faults, parametric faults, qualitative fault isolation
Bayoudh, M., Travé-Massuy`es, L., & Olive, X. (2008). Coupling continuous and discrete event system techniques for hybrid system diagnosability analysis. In 18th european conf. on artificial intel. (pp. 219–223).
Bayoudh, M., Travé-Massuy`es, L., & Olive, X. (2009). Diagnosis of a Class of Non Linear Hybrid Systems by On-line Instantiation of Parameterized Analytical Redundancy Relations. In 20th international workshop on principles of diagnosis (p. 283-289).
Benazera, E., & Travé-Massuy`es, L. (2009, October). Settheoretic estimation of hybrid system configurations. Trans. Sys. Man Cyber. Part B, 39, 1277–1291. doi: 10.1109/TSMCB.2009.2015280
Bregon, A., Daigle, M., & Roychoudhury, I. (2016, July). Qualitative fault isolation of hybrid systems: A structural model decomposition-based approach. In Third european conference of the phm society 2016.
Bregon, A., Daigle, M., Roychoudhury, I., Biswas, G., Koutsoukos, X., & Pulido, B. (2014, May). An event-based distributed diagnosis framework using structural model decomposition. Artificial Intelligence, 210, 1-35.
Bregon, A., Narasimhan, S., Roychoudhury, I., Daigle, M., & Pulido, B. (2013, October). An efficient model-based diagnosis engine for hybrid systems using structural model decomposition. In Proceedings of the annual conference of the prognostics and health management society, 2013.
Cocquempot, V., El Mezyani, T., & Staroswiecki, M. (2004, July). Fault detection and isolation for hybrid systems using structured parity residuals. In 5th asian control conference (Vol. 2, p. 1204-1212). doi: 10.1109/ASCC.2004.185027
Daigle, M. (2008). A qualitative event-based approach to fault diagnosis of hybrid systems (Unpublished doctoral dissertation). Vanderbilt University.
Daigle, M., Bregon, A., & Roychoudhury, I. (2015, September). A Structural Model Decomposition Framework for Hybrid Systems Diagnosis. In Proceedings of the 26nd international workshop on principles of diagnosis. Paris, France.
Daigle, M., Koutsoukos, X., & Biswas, G. (2009, July). A qualitative event-based approach to continuous systems diagnosis. IEEE Transactions on Control Systems Technology, 17(4), 780–793.
Daigle, M., Koutsoukos, X., & Biswas, G. (2010, October). An event-based approach to integrated parametric and discrete fault diagnosis in hybrid systems. Trans. of the Institute of Measurement and Control, 32(5), 487-510.
Daigle, M., Roychoudhury, I., Biswas, G., Koutsoukos, X., Patterson-Hine, A., & Poll, S. (2010, September). 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., & Bregon, A. (2014, September). Qualitative event-based fault isolation under uncertain observations. In Annual conference of the prognostics and health management society 2014 (p. 347-355).
Daigle, M., Roychoudhury, I., & Bregon, A. (2015). Qualitative event-based diagnosis applied to a spacecraft electrical power distribution system. Control Engineering Practice, 38, 75 - 91. doi: http://dx.doi.org/10.1016/j.conengprac.2015.01.007
Henzinger, T. A. (2000). The theory of hybrid automata. Springer.
Hofbaur, M., & Williams, B. (2004). Hybrid estimation of complex systems. IEEE Trans. on Sys., Man, and Cyber, Part B: Cyber., 34(5), 2178-2191. doi:
10.1109/TSMCB.2004.835009
Koutsoukos, X., Kurien, J., & Zhao, F. (2003). Estimation of distributed hybrid systems using particle filtering methods. In In hybrid systems: Computation and control (hscc 2003). springer verlag lecture notes on computer science (pp. 298–313). Springer.
Mosterman, P., & Biswas, G. (2000). A comprehensive methodology for building hybrid models of physical systems. Artificial Intel., 121(1-2), 171 - 209. doi: DOI: 10.1016/S0004-3702(00)00032-1
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.
Narasimhan, S., & Biswas, G. (2007, May). Model-Based Diagnosis of Hybrid Systems. IEEE Trans. Syst. Man. Cy. Part A, 37(3), 348-361.
Narasimhan, S., & Brownston, L. (2007, May). HyDE: A General Framework for Stochastic and Hybrid Modelbased Diagnosis. In Proc. of the 18th int. ws. on principles of diagnosis (p. 186-193).
Rienm¨uller, T., Bayoudh, M., Hofbaur, M., & Travé-Massuy`es, L. (2009). Hybrid Estimation through Synergic Mode-Set Focusing. In 7th ifac symposium on fault detection, supervision and safety of technical processes (p. 1480-1485). Barcelona, Spain.
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.
Trave-Massuyes, L., & Pons, R. (1997). Causal ordering for multiple mode systems. In Proceedings of the eleventh international workshop on qualitative reasoning (pp. 203–214).
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.