Model adaptation for prognostics in a particle filtering framework

International Journal of Prognostics and Health Management

Volumn 2, Issue 1, 2011, Pages

  Saha, Bhaskar ^a   Goebel, Kai ^b  

^a MISSION CRITICAL TECHNOLOGIES INC   (United States)

^b NASA AMES RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84875870469     PISSN: None     EISSN: 21532648     Source Type: Journal    
DOI: None     Document Type: Article

References (13)

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5. 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.
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7. Jazwinski, A. H. (1970). Stochastic Processes and Filtering Theory, Academic Press, N. Y.
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9. Ristic, B., Arulampalam, S. & Gordon, N. (2004). Beyond the Kalman Filter, Artech House.
10. 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.
11. 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.
12. Saltelli, A., Tarantola, S., Campolongo, F. & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models, John Wiley and Sons.
13. 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.