Model-based Prognostics of Hybrid Systems

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

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

Published Oct 18, 2015
Matthew Daigle Indranil Roychoudhury Anibal Bregon

Abstract

Model-based prognostics has become a popular approach to solving the prognostics problem. However, almost all work has focused on prognostics of systems with continuous dynamics. In this paper, we extend the model-based prognostics framework to hybrid systems models that combine both continuous and discrete dynamics. In general, most systems are hybrid in nature, including those that combine physical processes with software. We generalize the model-based prognostics formulation to hybrid systems, and describe the challenges involved. We present a general approach for modeling hybrid systems and overview methods for solving estimation and prediction in hybrid systems. As a case study, we consider the problem of conflict (i.e., loss of separation) pre- diction in the National Airspace System, in which the aircraft models are hybrid dynamical systems.

How to Cite

Daigle, M. ., Roychoudhury, . I. ., & Bregon, . A. . (2015). Model-based Prognostics of Hybrid Systems. Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2586
Abstract 2341 | PDF Downloads 174

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

Keywords

prediction, prognosis, Hybrid Systems, NAS

References
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–188.

Bayoudh, M., Trave-Massuyes, L., & Olive, X. (2008, July). Hybrid systems diagnosis by coupling continuous and discrete event techniques. In Proceedings of the 17th International Federation of Automatic Control World Congress (pp. 7265–7270).

Benazera, E., & Trave ́-Massuye`s, L. (2009, October). Set- theoretic estimation of hybrid system configurations. Trans. Sys. Man Cyber. Part B, 39, 1277–1291.

Bilmoria, K. D., Banavar, S., Chatterji, G. B., Sheth, K. S., & Grabbe, S. (2000, June). Facet: Future atm concepts evaluation tool. In 3rd USA/EuropeATM R&D Seminar.

Blom, H. A., & Bloem, E. A. (2004). Particle filtering for stochastic hybrid systems. In 43rd IEEE Conference on Decision and Control (Vol. 3, pp. 3221–3226).

Bregon, A., Alonso, C., Biswas, G., Pulido, B., & Moya, N. (2011, October). Hybrid systems fault diagnosis with possible conflicts. In Proceedings of the 22nd International Workshop on Principles of Diagnosis (p. 195- 202). Murnau, Germany.

Camci, F. (2009). System maintenance scheduling with prognostics information using genetic algorithm. IEEE Transactions on Reliability, 58(3), 539–552.

Chanthery, E., & Ribot, P. (2013). An integrated framework for diagnosis and prognosis of hybrid systems. In 3rd Workshop on Hybrid Autonomous Systems (pp. 14–25).

Chatterji, G., Sridhar, B., & Bilimoria, K. (1996, July). En- route flight trajectory prediction for conflict avoidance and traffic management. In AIAA Guidance, Navigation, and Control and Conference.

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., Bregon, A., & Roychoudhury, I. (2012, Septem- ber). A distributed approach to system-level prognostics. In Annual Conference of the Prognostics and Health Management Society 2012 (p. 71-82).

Daigle, M., Bregon, A., & Roychoudhury, I. (2015, Septem- ber). A structural model decomposition framework for hybrid systems diagnosis. In 26th International Workshop on Principles of Diagnosis.

Daigle, M., & Goebel, K. (2013, May). Model-based prognostics with concurrent damage progression processes. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(4), 535-546.

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., Saha, B., & Goebel, K. (2012, March). A comparison of filter-based approaches for model- based prognostics. In Proceedings of the 2012 IEEE Aerospace Conference.

Daigle, M., & Sankararaman, S. (2013, October). Advanced methods for determining prediction uncertainty in model-based prognostics with application to planetary rovers. In Annual Conference of the Prognostics and Health Management Society 2013 (p. 262-274).

Daigle, M., Saxena, A., & Goebel, K. (2012, September). An efficient deterministic approach to model-based prediction uncertainty estimation. In Annual Conference of
the Prognostics and Health Management Society 2012(p. 326-335).

Erzberger, H., Paielli, R. A., Isaacson, D. R., & Eshow, M. M.(1997). Conflict detection and resolution in the presence of prediction error. In 1st USA/Europe Air Traffic Management R&D Seminar.
Gaudel, Q., Chanthery, E., & Ribot, P. (2014, September). Health monitoring of hybrid systems using hybrid particle petri nets. In Annual Conference of the Prognostics and Health Management Society 2014.

Henzinger, T. A. (2000). The theory of hybrid automata. Springer.

Hofbaur, M., & Williams, B. (2004, October). Hybrid estimation of complex systems. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 34(5), 2178-2191.

Julier, S. J., & Uhlmann, J. K. (1997). A new extension of the Kalman filter to nonlinear systems. In Proceedings of the 11th Intl. Symposium on Aerospace/Defense Sensing, Simulation and Controls (pp. 182–193).

Julier, S. J., & Uhlmann, J. K. (2004, Mar). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401-422.

Koutsoukos, X., Kurien, J., & Zhao, F. (2003). Estimation of distributed hybrid systems using particle filtering methods. In Hybrid Systems: Computation and Control (HSCC 2003). Springer Verlag Lecture Notes on Computer Science (pp. 298–313). Springer.

McIlraith, S. (2000). Diagnosing hybrid systems: a Bayesian model selection approach. In Proceedings of the 11th International Workshop on Principles of Diagnosis (pp. 140–146).

Mosterman, P., & Biswas, G. (2000). A comprehensive methodology for building hybrid models of physical systems. Artificial Intelligence, 121(1-2), 171 - 209.

Mosterman, P. J., & Biswas, G. (1998). A theory of dis- continuities in physical system models. Journal of the Franklin Institute, 335(3), 401–439.

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 model- based diagnosis. In Proc. of the 18th Int. WS. on Principles of Diagnosis (p. 186-193).
Orchard, M., & Vachtsevanos, G. (2009, June). A particle filtering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control(3-4), 221-246.

Rienmu ̈ller, T., Bayoudh, M., Hofbaur, M., & Trave ́- Massuye`s, 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.

Saha, B., & Goebel, K. (2009, September). Modeling Li-ion battery capacity depletion in a particle filtering framework. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2009.

Sankararaman, S., Daigle, M., & Goebel, K. (2014, June). Uncertainty quantification in remaining useful life pre- diction using first-order reliability methods. IEEE Transactions on Reliability, 63(2), 603-619.

Sankararaman, S., Daigle, M., Saxena, A., & Goebel, K. (2013, March). Analytical algorithms to quantify the uncertainty in remaining useful life prediction. In 2013 IEEE Aerospace Conference.

Slattery, R., & Zhao, Y. (1997). Trajectory synthesis for air traffic automation. Journal of Guidance, Control, and Dynamics, 20(2), 232–238.

Tian, Z., Jin, T., Wu, B., & Ding, F. (2011). Condition based maintenance optimization for wind power generation systems under continuous monitoring. Renewable Energy, 36(5), 1502–1509.

Tomlin, C., Pappas, G. J., & Sastry, S. (1998). Conflict resolution for air traffic management: A study in multiagent hybrid systems. Automatic Control, IEEE Transactions on, 43(4), 509–521.

Yu, M., Wang, D., Luo, M., & Huang, L. (2011). Prognosis of hybrid systems with multiple incipient faults: augmented global analytical redundancy relations approach. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 41(3), 540– 551.

Zabi, S., Ribot, P., & Chanthery, E. (2013, October). Health monitoring and prognosis of hybrid systems. In Annual Conference of the Prognostics and Health Management Society.
Section
Technical Research Papers