Uncertainty representation and interpretation in model-based prognostics algorithms based on Kalman Filter estimation

Proceedings of the Annual Conference of the Prognostics and Health Management Society 2012, PHM 2012

Volumn , Issue , 2012, Pages 427-436

  Celaya, José R ^a   Saxena, Abhinav ^a   Goebel, Kai ^a  

^a NASA AMES RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DECISION MAKING; KALMAN FILTERS; RANDOM PROCESSES; STOCHASTIC SYSTEMS; SYSTEMS ENGINEERING; UNCERTAINTY ANALYSIS;

ELECTRONICS COMPONENTS; KALMAN FILTER ESTIMATION; MODEL-BASED OPC; REMAINING USEFUL LIFE PREDICTIONS; REMAINING USEFUL LIVES; UNCERTAINTY REPRESENTATION;

PROBABILITY DENSITY FUNCTION;

EID: 84920507324     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Bain, L. J., & Engelhardt, M. (1992). Introduction to probability and mathematical statistics. Duxbury.
2. Bedford, T., & Cooke, R. M. (2001). Probabilistic risk analysis: Foundations and methods. Cambridge University Press.
3. Celaya, J., Kulkarni, C., Biswas, G., & Goebel, K. (2011, September 25-29). A model-based prognostics methodology for electrolytic capacitors based on electrical overstress accelerated aging. In Proceedings of annual conference of the phm society. Montreal, Canada.
4. Celaya, J., Kulkarni, C., Saha, S., Biswas, G., & Goebel, K. (2012, January). Accelerated aging in electrolytic capacitors for prognostics. In 2012 proceedings-annual reliability and maintainability symposium (rams) (p. 1-6). doi: 10.1109/RAMS.2012.6175486
5. Celaya, J., Saxena, A., & Goebel, K. (2012, June 19-21). A discussion on uncertainty representation and interpretation in model-based prognostics algorithms based on kalman filter estimation applied to prognostics of electronics components. In Infotech@aerospace 2012 online conference proceedings. Garden Grove, California.
6. Celaya, J., Saxena, A., Kulkarni, C., Saha, S., & Goebel, K. (2012, January). Prognostics approach for power mosfet under thermal-stress aging. In 2012 proceedings-annual reliability and maintainability symposium (rams) (p. 1-6). doi: 10.1109/RAMS.2012.6175487
7. Celaya, J. R., Saxena, A., Saha, S., & Goebel, K. (2011, September 25-29). Prognostics of power mosfets under thermal stress accelerated aging using data-driven and model-based methodologies. In Proceedings of annual conference of the phm society. Montreal, Canada.
8. Daigle, M. J., & Goebel, K. (2011). A model-based prognostics approach applied to pneumatic valves. International Journal of Prognostics and Health Management, 2 (2)(008).
9. DeCastro, J. A. (2009). Exact nonlinear filtering and prediction in process model-based prognostics. In Annual conference of the prognostics and health management society. San Diego, CA.
10. deNeufville, R. (2004). Uncertainty management for engineering systems planning and design. In Engineering systems symposium mit. Cambridge, MA.
11. Engel, S. J. (2009). PHM engineering perspectives, challenges and 'crossing the valley of death'. In Annual conference of the prognostics and health managament society. San Diego, CA.
12. Gross, D., & Harris, C. M. (1998). Fundamentals of queueing theory (3rd ed.). John Wiley & Sons Inc.
13. Gu, J., Barker, D., & Pecht, M. (2007). Uncertainty assessment of prognostics of electronics subject to random vibration. In Artificial intelligence for prognostics: Papers from the aaai fall symposium.
14. Hastings, D., & McManus, H. (2004). A framework for understanding uncertainty and its mitigation and exploitation in complex systems. In Engineering systems symposium mit (p. 19). Cambridge MA.
15. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering, 82 (Series D), 35-45.
16. Ng, K.-C., & Abramson, B. (1990). Uncertainty management in expert systems. IEEE Expert Systems, 20.
17. Oppenheim, A. V., & Schafer, R. W. (1989). Discrete-time signal processing. Prentice Hall.
18. Orchard, M., Kacprzynski, G., Goebel, K., Saha, B., & Vachtsevanos, G. (2008, oct.). Advances in uncertainty representation and management for particle filtering applied to prognostics. In Prognostics and health management, 2008. phm 2008. international conference on (p. 1-6). doi: 10.1109/PHM.2008.4711433
19. Saha, B., & Goebel, K. (2008, march). Uncertainty management for diagnostics and prognostics of batteries using bayesian techniques. In Aerospace conference, 2008 ieee (p. 1-8). doi: 10.1109/AERO.2008.4526631
20. Saha, B., & Goebel, K. (2009). Modeling li-ion battery capacity depletion in a particle filtering framework. In Proceedings of annual conference of the phm society.
21. Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009, feb.). Prognostics methods for battery health monitoring using a bayesian framework. IEEE Transactions on Instrumentation and Measurement, 58(2), 291-296. doi: 10.1109/TIM.2008.2005965
22. 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(1).
23. Saxena, A., Celaya, J. R., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health Management, 1 (1)(001).
24. Tang, L., Kacprzynski, G., Goebel, K., & Vachtsevanos, G. (2009, march). Methodologies for uncertainty management in prognostics. In Aerospace conference, 2009 ieee (p. 1-12). doi: 10.1109/AERO.2009.4839668
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.
26. Wang, H.-f. (2011, January). Decision of prognostics and health management under uncertainty. International Journal of Computer Applications, 13(4), 1-5. (Published by Foundation of Computer Science)