Model Adaptation for Prognostics in a Particle Filtering Framework



Bhaskar Saha Kai Goebel


One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.*

Abstract 66 | PDF Downloads 61



model-based prognostics, particle filters, model adaptation, sensitivity analysis

Bellman, R.E. (1957). Dynamic Programming, Princeton University Press, Princeton, NJ.
Cacuci, D. G. (2003). Sensitivity and Uncertainty Analysis: Theory, Volume I, Chapman & Hall.
Daum, F. E. (2005). Nonlinear Filters: Beyond the Kalman Filter, IEEE A&E Systems Magazine, vol. 20, no. 8, pp. 57-69.
Daum, F. E. & Huang, J. (2003). Curse of Dimensionality and Particle Filters, in Proceedings of IEEE Conference on Aerospace, Big Sky, MT.
Gordon, N. J., Salmond, D. J. & Smith, A. F. M. (1993). Novel Approach to Nonlinear/Non-Gaussian Bayesian State Estimation, Radar and Signal Processing, IEE Proceedings F, vol. 140, no. 2, pp. 107-113.
Helton, J. C., Johnson, J. D., Salaberry, C. J. & Storlie, C. B. (2006). Survey of sampling based methods for uncertainty and sensitivity analysis, Reliability Engineering and System Safety, vol. 91, pp. 1175–1209.
Jazwinski, A. H. (1970). Stochastic Processes and Filtering Theory, Academic Press, N. Y.
Orchard, M., Tobar, F. & Vachtsevanos, G.. (2009). Outer Feedback Correction Loops in Particle Filtering-based Prognostic Algorithms: Statistical Performance Comparison, Studies in Informatics
and Control, vol. 18, issue 4, pp. 295-304.
Ristic, B., Arulampalam, S. & Gordon, N. (2004). Beyond the Kalman Filter, Artech House.
Saha, B. & Goebel, K. (2009). 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, San Diego, CA.
Saha, B., Goebel, K., Poll, S. & Christophersen, J. (2009). Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework, IEEE Transactions on Instrumentation and Measurement, vol.58, no.2, pp. 291-296.
Saltelli, A., Tarantola, S., Campolongo, F. & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models, John Wiley and Sons.
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S. & Schwabacher, M. (2008). Metrics for Evaluating Performance of Prognostic Techniques, in Proceedings of Intl. Conf. on Prognostics and Health Management, Denver, CO.
Technical Papers