Overcoming Non-Discernibility through Mode-Sequence Analytic Redundancy Relations in Hybrid Diagnosis and Estimation

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

PDF
Published Oct 11, 2010
DOI https://doi.org/10.36001/phmconf.2010.v2i1.1938
Michael Hofbaur
Institute of Automation and Control Engineering, UMIT, Eduard Walln¨over Zentrum 1, A- 6060 Hall in Tyrol, Austria
Louise Travé-Massuy`es
CNRS ; LAAS ; 7 avenue du colonel Roche, F-31077 Toulouse, Fra
Theresa Rienm¨uller
Institute of Automation and Control Engineering, UMIT, Eduard Walln¨over Zentrum 1, A- 6060 Hall in Tyrol, Austria
Mehdi Bayoudh
Université de Toulouse ; UPS, INSA, INP, ISAE ; LAAS ; F-31077 Toulouse, France

Abstract

Estimating the mode of operation of a hybrid system means to account for its interleaved continuous and discrete dynamics from a partial set of observations. The quality of the estimation obviously depends on the level of diagnosability of the system, which has been shown to depend on the discrete and the continuous observations as a whole. The case of two modes showing very similar, if not the same, continuous dynamic input/output behavior is critical when the discrete events do not allow discrimination. In this case, these modes, so called non-discernible modes, are accepted to be not discriminable. This paper uses the concept of Mode-Sequence Analytic Redundancy Relation which is shown to achieve discriminability of such non-discernible modes in many cases. A focusing test based on modesequence ARRs is then proposed to improve hybrid state estimation.

How to Cite

Hofbaur, M., Travé-Massuy`es, L., Rienm¨uller, T., & Bayoudh, M. (2010). Overcoming Non-Discernibility through Mode-Sequence Analytic Redundancy Relations in Hybrid Diagnosis and Estimation. Annual Conference of the PHM Society, 2(2). https://doi.org/10.36001/phmconf.2010.v2i1.1938
Abstract 104 | PDF Downloads 100

##plugins.themes.bootstrap3.article.details##

Keywords

PHM

References
( Ackerson and Fu, 1970 ) G.A. Ackerson and K.S. Fu. On state estimation in switching environments. IEEE Transactions on Automatic Control, 15:1017, 1970.
( Bayoudh et al., 2008a ) M. Bayoudh, L. Travé-Massuy`es, and X. Olive. Coupling continuous and discrete event system techniques for hybrid system diagnosability analysis. In Proceedings of the European Conference on Artificial Intelligence (ECAI 08), Patras, Greece, pages 219–223, 2008.
( Bayoudh et al., 2008b ) M. Bayoudh, L. Travé-Massuy`es, and X. Olive. Hybrid systems diagnosis by coupling continuous and discrete event techniques. In Proceedings of the IFAC World Congress, Seoul, Korea, pages 7265–7270, 2008.
( Benazera and Travé-Massuy`es, 2009 ) E. Benazera and L. Travé-Massuy`es. Set-Theoretic Estimation of Hybrid System Configurations. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society, 39/5:1277–1291, Oct. 2009.
(Benazera et al., 2002 ) E. Benazera, L. Travé-Massuy`es, and P. Dague. State tracking of uncertain hybrid concurrent systems. In Proceedings of the 13th International Workshop on Principles of Diagnosis (DX02), pages 106–114, May 2002.
(Blom and Bar-Shalom, 1988 ) H.A.P. Blom and Y. Bar-Shalom. The interacting multiple model algorithm for systems with markovian switching coefficients. IEEE Transactions on Automatic Control, 33:780–783, 1988.
(Cocquempot et al., 2004 ) V. Cocquempot, T. El Mezyani, and M. Staroswiecki. Fault detection and isolation for hybrid systems using structured parity residuals. In IEEE/IFAC-ASCC : Asian Control Conference, 2004.
(de Freitas, 2002 ) N. de Freitas. Rao-blackwellised particle filtering for fault diagnosis. In Proceedings of the IEEE Aerospace Conference 2002, volume 4, pages 1767–1772, 2002.
(Domlan et al., 2007 ) Elom Ayih Domlan, José Ragot, and Didier Maquin. Switching systems: Active mode recognition, identification of the switching law. Journal of Control Science and Engineering, 11 pages, 2007.
(Gertler, 1991 ) J. Gertler. Analytical redundancy methods in failure detection and isolation. In Preprints of the IFAC SAFEPROCESS Symposium, pages 9–21, 1991.
(Hofbaur and Williams, 2004 ) M. W. Hofbaur and B. C. Williams. Hybrid estimation of complex systems. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 34(5):2178–2191, October 2004.
(Li and Bar-Shalom, 1996 ) X.R. Li and Y. BarShalom. Multiple-model estimation with variable structure. IEEE Transactions on Automatic Control, 41:478–493, 1996.
( Ljung and Glad, 1994 ) L. Ljung and T. Glad. On global identifiability for model parametrizations. IEEE Transactions on Automatic Control, 30:265276, 1994.
( Narasimhan and Biswas, 2002 ) S. Narasimhan and G. Biswas. An approach to model-based diagnosis of hybrid systems. In C.J. Tomlin and M.R.
Greenstreet, editors, Hybrid Systems: Computation and Control, HSCC 2002, volume 2289 of Lecture Notes in Computer Science, pages 308–322. Springer Verlag, 2002.
( Patton and Chen, 1997 ) R. Patton and J. Chen. Observer-based fault detection and isolation: robustness and applications. Control Engineering Practice, 5(5):671–682, May 1997.
( Rienm¨uller et al., 2009 ) Th. Rienm¨uller, M. Mayoudh, M. Hofbaur, and L. Travé-Massuy`es. Hybrid estimation through synergic mode-set focusing. In Preprints of the IFAC SAFEPROCESS Symposium, 2009.
( Sampath et al., 1995 ) M. Sampath, R. Sengputa, S. Lafortune, K. Sinnamohideen, and D. Teneketzis. Diagnosability of discrete event systems. IEEE Transactions on Automatic Control, 40:1555–1575, Sept. 1995.
( Verma et al., 2004 ) V. Verma, G. Gordon, R. Simmons, and S. Thrun. Real-time fault diagnosis. IEEE Robotics and Automation Magazine, 11(2):56–66, 2004.
( Vidal et al., 2003 ) R. Vidal, A. Chiuso, S. Soatto, and S. Sastry. Observability of linear hybrid systems. In Hybrid Systems: Computation and Control, HSCC 2003, volume 2623 of Lecture Notes in Computer Science, pages 526–539. Springer Verlag, 2003.
Issue
Vol. 2 No. 2 (2010): Proceedings of the 21st International Workshop on the Principles of Diagnosis, held jointly with the Annual Conference of the PHM Society 2010
Section
Technical Research Papers

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.