Investigating the Effect of Damage Progression Model Choice on Prognostics Performance

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

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

Matthew Daigle Indranil Roychoudhury Sriram Narasimhan Sankalita Saha Bhaskar Saha Kai Goebel

Abstract

The success of model-based approaches to systems health management depends largely on the quality of the underlying models. In model-based prognostics, it is especially the quality of the damage progression models, i.e., the models describing how damage evolves as the system operates, that determines the accuracy and precision of remaining useful life predictions. Several common forms of these models are generally assumed in the literature but are often not supported by physical evidence or physics-based analysis. In this paper, using a centrifugal pump as a case study, we develop different damage progression models. In simulation, we investigate how model changes influence prognostics performance. Results demonstrate that, in some cases, simple damage progression models are sufficient. But, in general, the results show a clear need for damage progression models that are accurate over long time horizons under varied loading conditions.

How to Cite

Daigle, M. ., Roychoudhury , I. ., Narasimhan, . S. ., Saha, S., Saha, B. ., & Goebel, K. (2011). Investigating the Effect of Damage Progression Model Choice on Prognostics Performance. Annual Conference of the PHM Society, 3(1). https://doi.org/10.36001/phmconf.2011.v3i1.2071
Abstract 8 | PDF Downloads 0

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

Keywords

model-based prognostics, centrifugal pump, model abstraction, damage progression model

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

Biswas, G., & Mahadevan, S. (2007, March). A Hierarchical Model-based approach to Systems Health Management. In Proceedings of the 2007 IEEE Aerospace Conference.

Daigle, M., & Goebel, K. (2010, 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.

Doucet, A., Godsill, S., & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering.
Statistics and Computing, 10, 197–208.

Frantz, F. (1995). A taxonomy of model abstraction techniques. In Proceedings of the 27th conference on Winter Simulation (pp. 1413–1420).
Hutchings, I. M. (1992). Tribology: friction and wear of engineering materials. CRC Press.

Kallesøe, C. (2005). Fault detection and isolation in centrifugal pumps. Unpublished doctoral dissertation, Aalborg University.

Lee, K., & Fishwick, P. A. (1996). Dynamic model abstraction. In Proceedings of the 28th conference on Winter Simulation (pp. 764–771).

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.

Lyshevski, S. E. (1999). Electromechanical Systems, Electric Machines, and Applied Mechatronics. CRC.

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., Quach, P., & Goebel, K. (2011, September). Exploring the model design space for battery health management. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2011.

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.

Tu, F., Ghoshal, S., Luo, J., Biswas, G., Mahadevan, S., Jaw, L., et al. (2007, March). PHM integration with maintenance and inventory management systems. In Proceedings of the 2007 IEEE Aerospace Conference.

Wolfram, A., Fussel, D., Brune, T., & Isermann, R. (2001). Component-based multi-model approach for fault detection and diagnosis of a centrifugal pump. In Proceedings of the 2001 American Control Conference (Vol. 6, pp. 4443–4448).

Zeigler, B., Praehofer, H., & Kim, T. (2000). Theory of modeling and simulation (2nd ed.). Academic Press.
Section
Technical Papers

Most read articles by the same author(s)

1 2 3 4 > >>