A novel computational methodology for uncertainty quantification in prognostics using the most probable point concept

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

Volumn , Issue , 2013, Pages 350-362

  Sankararaman, Shankar ^a   Goebel, Kai ^a  

^a NASA AMES RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BATTERY MANAGEMENT SYSTEMS; COMPUTATIONAL METHODS; FORECASTING; SYSTEMS ENGINEERING;

COMPUTATIONAL METHODOLOGY; CONDITION-BASED MONITORING; ENGINEERING COMPONENTS; PROBABILISTIC TECHNIQUE; REMAINING USEFUL LIFE PREDICTIONS; SOURCES OF UNCERTAINTY; UNCERTAINTY PROPAGATION; UNCERTAINTY QUANTIFICATIONS;

PROBABILITY DISTRIBUTIONS;

EID: 84920507181     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Chen, M., & Rincon-Mora, G. A. (2006). Accurate electrical battery model capable of predicting runtime and I-V performance. IEEE Transactions on Energy Conversion, 21(2), 504-511.
2. Daigle,M., Saxena, A., & Goebel, K. (2012). An efficient deterministic approach to model-based prediction uncertainty estimation. In Annual Conference of the Prognostics and Health Management Society.
3. DeCastro, J. A. (2009). Exact nonlinear filtering and prediction in process model-based prognostics. In Annual Conference of the Prognostics and HealthManagement Society. San Diego, CA.
4. Farrar, C., & Lieven, N. (2007). Damage prognosis: the future of structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851), 623-632.
5. Farrar, C. R., Lieven, N. A., & Bement, M. T. (2005). An introduction to damage prognosis. Damage Prognosis: For Aerospace, Civil and Mechanical Systems, 1-12.
6. Gu, J., Barker, D., & Pecht, M. (2007). Uncertainty assessment of prognostics of electronics subject to random vibration. In Aaai fall symposium on artificial intelligence for prognostics (pp. 50-57).
7. Guan, X., Liu, Y., Jha, R., Saxena, A., Celaya, J., & Geobel, K. (2011). Comparison of two probabilistic fatigue damage assessment approaches using prognostic performance metrics. International Journal of Prognostics and Health Management, 1, 005.
8. Haldar, A., & Mahadevan, S. (2000). Probability, reliability, and statistical methods in engineering design. John Wiley & Sons, Inc.
9. Inman, D. J., Farrar, C. R., Junior, V. L., & Junior, V. S. (2005). Damage prognosis for aerospace, civil and mechanical systems. Wiley.
10. Liu, P.-L., & Der Kiureghian, A. (1986). Multivariate distribution models with prescribed marginals and covariances. Probabilistic Engineering Mechanics, 1(2), 105-112.
11. Morgenstern, D. (1956). Einfache beispiele zweidimensionaler verteilungen. Mitt. Math. Statist, 8(1), 234-235.
12. Nataf, A. (1962). Détermination des distributions de probabilit és dont les marges sont données. Comptes rendus de lacademie des sciences, 225, 42-43.
13. Orchard,M., Kacprzynski, G., Goebel, K., Saha, B., & Vachtsevanos, G. (2008). Advances in uncertainty representation and management for particle filtering applied to prognostics. In Prognostics and health management, international conference on (p. 1-6).
14. Saha, B., & Goebel, K. (2008). Uncertainty management for diagnostics and prognostics of batteries using bayesian techniques. In Aerospace Conference, 2008 IEEE (p. 1-8).
15. Saha, B., Quach, C. C., & Goebel, K. (2012). Optimizing battery life for electric uavs using a bayesian framework. In Aerospace Conference, 2012 IEEE.
16. Sankararaman, S., Daigle, M., Saxena, A., & Goebel, K. (2013). Analytical algorithms to quantify the uncertainty in remaining useful life prediction. In Aerospace Conference, 2013 IEEE (pp. 1-11).
17. Sankararaman, S., & Goebel, K. (2013a). Remaining useful life estimation in prognosis: An uncertainty propagation problem. In 2013 AIAA Infotech@Aerospace Conference.
18. Sankararaman, S., & Goebel, K. (2013b). 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.
19. Sankararaman, S., Ling, Y., Shantz, C., & Mahadevan, S. (2009). Uncertainty quantification in fatigue damage prognosis. In Annual Conference of the Prognostics and Health Management Society.
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. Sankararaman, S., & Mahadevan, S. (2011a). Likelihoodbased representation of epistemic uncertainty due to sparse point data and/or interval data. Reliability Engineering & System Safety, 96(7), 814-824.
22. Sankararaman, S., & Mahadevan, S. (2011b). Uncertainty quantification in structural damage diagnosis. Structural Control and Health Monitoring, 18(8), 807-824.
23. Sankararaman, S., & Mahadevan, S. (2013). Bayesian methodology for diagnosis uncertainty quantification and health monitoring. Structural Control and Health Monitoring, 20(1), 88-106.
24. Tang, L., Kacprzynski, G. J., Goebel, K., & Vachtsevanos, G. (2009). Methodologies for uncertainty management in prognostics. In Aerospace Conference, 2009 IEEE (pp. 1-12).
25. Usynin, A., & Hines, J. W. (2007). Uncertainty management in shock models applied to prognostic problems. In Artificial Intelligence For Prognostics: Papers From The AAAI Fall Symposium.