A Model-Based Prognostics Approach Applied to Pneumatic Valves
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Published
Jun 1, 2011
Matthew J. Daigle
Kai Goebel
Abstract
Within the area of systems health management, the task of prognostics centers on predicting when components will fail. 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. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general modelbased prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.
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Keywords
Pneumatic Valves, model-based prognostics, particle filters
References
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Clapp, T. C., & Godsill, S. J. (1999). Fixed-lag smoothing using sequential importance sampling. Bayesian Statistics IV, 743–752.
Daigle, M., & Goebel, K. (2009, September). Model-based prognostics with fixed-lag particle filters. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2009.
Daigle, M., & Goebel, K. (2010a, October). Improving Computational Efficiency of Prediction in Model-based Prognostics Using the Unscented Transform. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2010.
Daigle, M., & Goebel, K. (2010b, March). Model-based prognostics under limited sensing. In Proceedings of the 2010 IEEE Aerospace Conference.
Daigle, M., & Goebel, K. (2011, March). Multiple damage progression paths in model-based prognostics. In Proceedings of the 2011 IEEE Aerospace Conference.
Daigle, M., Roychoudhury, I., Narasimhan, S., Saha, S., Saha, B., & Goebel, K. (2011, September). Investigating the Effect of Damage Progression Model Choice on Prognostics Performance. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2011.
Doucet, A., Godsill, S., & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10, 197–208.
Gomes, J. P. P., Ferreira, B. C., Cabral, D., Glav˜ao, R. K. H., & Yoneyama, T. (2010, October). Health monitoring of a pneumatic valve using a PIT based technique. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2010.
Hutchings, I. M. (1992). Tribology: friction and wear of engineering materials. CRC Press.
Julier, S., & Uhlmann, J. (1997). A new extension of the Kalman filter to nonlinear systems. In Proceedings of the 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls (pp. 182–193).
Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5(1), 1–25.
Liu, J., & West, M. (2001). Combined parameter and state estimation in simulation-based filtering. Sequential Monte Carlo methods in Practice, 197–223.
Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008, September). Model-Based Prognostic Techniques Applied to a Suspension System. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 38(5), 1156 -1168.
Orchard, M. (2007). A Particle Filtering-based Framework for On-line Fault Diagnosis and Failure Prognosis. Unpublished doctoral dissertation, Georgia Institute of Technology.
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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.
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Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for Offline Evaluation of Prognostic Performance. International Journal of Prognostics and Health Management (IJPHM), 1.
Schwabacher, M. (2005). A survey of data-driven prognostics. In Proceedings of the AIAA Infotech@Aerospace Conference.
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.
Bolander, N., Qiu, H., Eklund, N., Hindle, E., & Rosenfeld, T. (2010, October). Physics-based remaining useful life prediction for aircraft engine bearing prognosis. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2010.
Byington, C. S., Watson, M., Edwards, D., & Stoelting, P. (2004, March). A model-based approach to prognostics and health management for flight control actuators. In Proceedings of the 2004 IEEE Aerospace Conference (Vol. 6, pp. 3551–3562).
Cappe, O., Godsill, S. J.,& Moulines, E. (2007). An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE, 95(5), 899.
Chelidze, D. (2002). Multimode damage tracking and failure prognosis in electromechanical system. In Proceedings of the SPIE Conference (Vol. 4733, pp. 1–12).
Clapp, T. C., & Godsill, S. J. (1999). Fixed-lag smoothing using sequential importance sampling. Bayesian Statistics IV, 743–752.
Daigle, M., & Goebel, K. (2009, September). Model-based prognostics with fixed-lag particle filters. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2009.
Daigle, M., & Goebel, K. (2010a, October). Improving Computational Efficiency of Prediction in Model-based Prognostics Using the Unscented Transform. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2010.
Daigle, M., & Goebel, K. (2010b, March). Model-based prognostics under limited sensing. In Proceedings of the 2010 IEEE Aerospace Conference.
Daigle, M., & Goebel, K. (2011, March). Multiple damage progression paths in model-based prognostics. In Proceedings of the 2011 IEEE Aerospace Conference.
Daigle, M., Roychoudhury, I., Narasimhan, S., Saha, S., Saha, B., & Goebel, K. (2011, September). Investigating the Effect of Damage Progression Model Choice on Prognostics Performance. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2011.
Doucet, A., Godsill, S., & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10, 197–208.
Gomes, J. P. P., Ferreira, B. C., Cabral, D., Glav˜ao, R. K. H., & Yoneyama, T. (2010, October). Health monitoring of a pneumatic valve using a PIT based technique. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2010.
Hutchings, I. M. (1992). Tribology: friction and wear of engineering materials. CRC Press.
Julier, S., & Uhlmann, J. (1997). A new extension of the Kalman filter to nonlinear systems. In Proceedings of the 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls (pp. 182–193).
Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5(1), 1–25.
Liu, J., & West, M. (2001). Combined parameter and state estimation in simulation-based filtering. Sequential Monte Carlo methods in Practice, 197–223.
Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008, September). Model-Based Prognostic Techniques Applied to a Suspension System. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 38(5), 1156 -1168.
Orchard, M. (2007). A Particle Filtering-based Framework for On-line Fault Diagnosis and Failure Prognosis. Unpublished doctoral dissertation, Georgia Institute of Technology.
Orchard, M., Kacprzynski, G., Goebel, K., Saha, B., & Vachtsevanos, G. (2008, October). Advances in uncertainty representation and management for particle filtering applied to prognostics. In Proceedings of International Conference on Prognostics and Health Management.
Perry, R., & Green, D. (2007). Perry’s chemical engineers’ handbook. McGraw-Hill Professional.
Roemer, M., Byington, C., Kacprzynski, G., & Vachtsevanos, G. (2005). 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.
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.
Saha, B., & Goebel, K. (2011). Model Adaptation for Prognostics in a Particle Filtering Framework. International Journal of Prognostics and Health Management.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for Offline Evaluation of Prognostic Performance. International Journal of Prognostics and Health Management (IJPHM), 1.
Schwabacher, M. (2005). A survey of data-driven prognostics. In Proceedings of the AIAA Infotech@Aerospace Conference.
Section
Technical Papers