Advanced methods for determining prediction uncertainty in model-based prognostics with application to planetary rovers

PHM 2013 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013

Volumn , Issue , 2013, Pages 262-274

  Daigle, Matthew ^a   Sankararaman, Shankar ^a  

^a NASA AMES RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; PLANETARY LANDERS; SYSTEMS ENGINEERING;

END OF DISCHARGES; FIRST ORDER RELIABILITY; INFORMED DECISION; PLANETARY ROVERS; PREDICTION PROBLEM; PREDICTION UNCERTAINTY; PROGNOSTIC APPROACH; UNSCENTED TRANSFORM;

MONTE CARLO METHODS;

EID: 84904004994     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. 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.
2. Balaban, E., Narasimhan, S., Daigle, M., Celaya, J., Roychoudhury, I., Saha, B.,. Goebel, K. (2011, September). A mobile robot testbed for prognostics-enabled autonomous decision making. In Annual conference of the prognostics and health management society (p. 15-30). Montreal, Canada.
3. Ceraolo, M. (2000, November). New dynamical models of lead-acid batteries. IEEE Transactions on Power Systems, 15(4), 1184-1190.
4. 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.
5. Daigle, M., Bregon, A., & Roychoudhury, I. (2012, September). A distributed approach to system-level prognostics. In Annual conference of the prognostics and health management society (p. 71-82).
6. 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.
7. Daigle, M., & Goebel, K. (2013, May). Model-based prognostics with concurrent damage progression processes. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(4), 535-546.
8. 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.
9. Daigle, M., Saxena, A., & Goebel, K. (2012, September). An efficient deterministic approach to model-based prediction uncertainty estimation. In Annual conference of the prognostics and health management society (p. 326-335).
10. Haldar, A., & Mahadevan, S. (2000). Probability, reliability, and statistical methods in engineering design. John Wiley & Sons, Inc.
11. 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).
12. 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).
13. Julier, S. J., & Uhlmann, J. K. (2004, Mar). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401-422.
14. Ling, Y., Shantz, C., Mahadevan, S., & Sankararaman, S. (2011). Stochastic prediction of fatigue loading using real-time monitoring data. International Journal of Fatigue, 33(7), 868-879.
15. 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.
16. Orchard, M., & Vachtsevanos, G. (2009, June). A particle filtering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control(3-4), 221-246.
17. 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.
18. Sankararaman, S., Daigle, M., Saxena, A., & Goebel, K. (2013, March). Analytical algorithms to quantify the uncertainty in remaining useful life prediction. In Proceedings of the 2013 IEEE Aerospace Conference.
19. Sankararaman, S., & Goebel, K. (2013, April). Uncertainty quantification in remaining useful life of aerospace components using state space models and inverse form. In Proceedings of the 15th Non-Deterministic Approaches Conference.
20. 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).
21. 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).