Model-based Prognostics with Fixed-lag Particle Filters

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

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

Published Mar 26, 2021
Matthew Daigle Kai Goebel

Abstract

Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. In most applications, uncertainties from a number of sources cause the predictions to be inaccurate and imprecise even with accurate models. Therefore, algorithms are employed that help in managing these uncertainties. Particle filters have become a popular choice to solve this problem due to their wide applicability and ease of implementation. We present a general model-based prognostics methodology using particle filters. In order to provide more accurate and precise estimates, and, therefore, more accurate and precise predictions, we investigate the use of fixed-lag filters. We develop a detailed physics-based model of a pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach. The experiments demonstrate the advantages that fixed-lag filters may provide in the context of prognostics, as measured by prognostics performance metrics.

How to Cite

Daigle, M. ., & Goebel , K. . (2021). Model-based Prognostics with Fixed-lag Particle Filters. Annual Conference of the PHM Society, 1(1). Retrieved from https://papers.phmsociety.org/index.php/phmconf/article/view/1598
Abstract 7 | PDF Downloads 2

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

Keywords

filtering, model based prognostics, particle filtering, applications: space

References
(Abbas et al., 2007) M. Abbas, A. A. Ferri, M. E. Orchard, and G. J. Vachtsevanos. An intelligent diagnostic/prognostic framework for automotive electrical systems. In 2007 IEEE Intelligent Vehicles Symposium, pages 352–357, 2007.
(Arulampalam et al., 2002) M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2):174–188, 2002.
(Byington et al., 2004) C. S. Byington, M. Watson, D. Edwards, and P. Stoelting. A model-based approach to prognostics and health management for flight control actuators. In Proceedings of the 2004 IEEE Aerospace Conference, volume 6, pages 3551–3562, March 2004.
(Cappe et al., 2007) O. Cappe, S. J. Godsill, and E. Moulines. An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE, 95(5):899, 2007.
(Clapp and Godsill, 1999) T. C. Clapp and S. J. God- sill. Fixed-lag smoothing using sequential importance sampling. Bayesian Statistics IV, pages 743– 752, 1999.
(Doucet et al., 2000) A. Doucet, S. Godsill, and C. Andrieu. On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10:197–208, 2000.
(Hutchings, 1992) I. M. Hutchings. Tribology: friction and wear of engineering materials. Hodder & Stoughton Publishers, 1992.
(Kitagawa,1996) G.Kitagawa. MonteCarlofilterand smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5(1):1–25, 1996.
(Li et al., 2007) P. Li, R. Goodall, P. Weston, C. Seng Ling, C. Goodman, and C. Roberts. Estimation of railway vehicle suspension parameters for condition monitoring. Control Engineering Practice, 15(1):43–55, 2007.
(Liu and West, 2001) J. Liu and M. West. Combined parameter and state estimation in simulation-based filtering. Sequential Monte Carlo methods in Practice, pages 197–223, 2001.
(Orchard et al., 2008) M. Orchard, G. Kacprzynski, K. Goebel, B. Saha, and G. Vachtsevanos. Advances in uncertainty representation and management for particle filtering applied to prognostics. In Proceedings of International Conference on Prognostics and Health Management, Oct 2008.
(Perry and Green, 2007) R.H. Perry and D.W. Green. Perry’s chemical engineers’ handbook. McGraw- Hill Professional, 2007.
(Roemer et al., 2005) M. Roemer, C. Byington, G. Kacprzynski, and G. Vachtsevanos. An overview of selected prognostic technologies with reference to an integrated PHM architecture. In Proceedings of the First International Forum on Integrated System Health Engineering and Management in Aerospace, 2005.
(Saha and Goebel, 2009) B. Saha and K. Goebel. 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, September 2009.
(Saxena et al., 2008) A. Saxena, J. Celaya, E. Bal- aban, K. Goebel, B. Saha, S. Saha, and M. Schwabacher. Metrics for evaluating performance of prognostic techniques. In International Conference on Prognostics and Health Management 2008, Oct 2008.
(Saxena et al., 2009) A. Saxena, J. Celaya, B. Saha, S. Saha, and K. Goebel. On applying the prognostic performance metrics. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2009, September 2009.
(Schwabacher, 2005) M.A. Schwabacher. A survey of data-driven prognostics. In Proceedings of the AIAA Infotech@ Aerospace Conference, 2005.
Section
Technical Papers

Most read articles by the same author(s)

1 2 3 4 5 6 > >>