An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

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

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

Published Sep 23, 2012
Matthew Daigle Abhinav Saxena Kai Goebel

Abstract

Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithms must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transform. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed.

How to Cite

Daigle, M. ., Saxena, A. ., & Goebel, K. (2012). An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation. Annual Conference of the PHM Society, 4(1). https://doi.org/10.36001/phmconf.2012.v4i1.2111
Abstract 608 | PDF Downloads 211

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

Keywords

model-based prognostics, unscented transform, uncertainty

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.
Barsali, S., & Ceraolo, M. (2002, March). Dynamical models of lead-acid batteries: Implementation issues. IEEE Transactions on Energy Conversion, 17(1), 16–23.
Ceraolo, M. (2000, November). New dynamical models of lead-acid batteries. IEEE Transactions on Power Systems, 15(4), 1184–1190.
Chen, M., & Rincon-Mora, G. A. (2006, June). Accurate electrical battery model capable of predicting runtime and I-V performance. IEEE Transactions on Energy Conversion, 21(2), 504 - 511.
Daigle, M., & Goebel, K. (2010, October). Improving computational efficiency of prediction in model-based prognostics using the unscented transform. In Proc. of the annual conference of the prognostics and health management society 2010.
Daigle, M., & Goebel, K. (2011, August). A model-based prognostics approach applied to pneumatic valves. International Journal of Prognostics and Health Management, 2(2).
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.
Edwards, D., Orchard, M. E., Tang, L., Goebel, K., & Vachtsevanos, G. (2010, September). Impact in input uncertainty on failure prognostic algorithms: Extending the remaining useful life of nonlinear systems. In Annual conference of the prognostics and health management society.
Julier, S. J. (1998, April). A skewed approach to filtering. In Proc. aerosense: 12th int. symp. aerospace/defense sensing, simulation and controls (Vol. 3373, p. 54-65).
Julier, S. J., & Uhlmann, J. K. (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).
Julier, S. J., & Uhlmann, J. K. (2004, March). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401–422.
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.
Roychoudhury, I., & Daigle, M. (2011, October). An integrated model-based diagnostic and prognostic framework. In Proceedings of the 22nd international workshop on principles of diagnosis (p. 44-51).
Saha, B., Quach, C. C., & Goebel, K. (2012, March). Optimizing battery life for electric UAVs using a Bayesian framework. In Proceedings of the 2012 ieee aerospace conference.
Sankararaman, S., Ling, Y., Shantz, C., & Mahadevan, S. (2011). Uncertainty quantification in fatigue crack growth prognosis. International Journal of Prognostics and Health Management, 2(1).
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, 1(1).
Section
Technical Research Papers

Most read articles by the same author(s)

<< < 1 2 3 4 5 > >>