Why is the remaining useful life prediction uncertain?

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

Volumn , Issue , 2013, Pages 337-349

  Sankararaman, Shankar ^a   Goebel, Kai ^a  

^a NASA AMES RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION MAKING; FORECASTING; SYSTEMS ENGINEERING;

CONDITION-BASED MONITORING; ENGINEERING APPLICATIONS; FREQUENTIST; REMAINING USEFUL LIFE PREDICTIONS; REMAINING USEFUL LIVES; RUL PREDICTIONS; SOURCES OF UNCERTAINTY; UNCERTAINTY PROPAGATION;

UNCERTAINTY ANALYSIS;

EID: 84920532088     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (38)

1. Bichon, B., Eldred, M., Swiler, L., Mahadevan, S., & McFarland, J. (2008). Efficient global reliability analysis for nonlinear implicit performance functions. AIAA journal, 46(10), 2459-2468.
2. Bucher, C. G. (1988). Adaptive samplingan iterative fast monte carlo procedure. Structural Safety, 5(2), 119-126.
3. Caflisch, R. E. (1998). Monte carlo and quasi-monte carlo methods. Acta numerica, 1998, 1-49.
4. Calvetti, D., & Somersalo, E. (2007). Introduction to bayesian scientific computing: ten lectures on subjective computing (Vol. 2). Springer New York.
5. Celaya, J., Saxena, A., & Goebel, K. (2012). Uncertainty representation and interpretation in model-based prognostics algorithms based on kalman filter estimation. In Proceedings of the Annual Conference of the PHM Society (pp. 23-27).
6. Celaya, J., Saxena, A., Kulkarni, C., Saha, S., & Goebel, K. (2012). Prognostics approach for power MOSFET under thermal-stress aging. In Reliability and Maintainability Symposium (RAMS), 2012 Proceedings-Annual (pp. 1-6).
7. Coppe, A., Haftka, R. T., Kim, N. H., & Yuan, F.-G. (2010). Uncertainty reduction of damage growth properties using structural health monitoring. Journal of Aircraft, 47(6), 2030-2038.
8. Daigle, M., & Goebel, K. (2010). Model-based prognostics under limited sensing. In Aerospace Conference, 2010 IEEE (pp. 1-12).
9. Daigle,M., & Goebel, K. (2011). A model-based prognostics approach applied to pneumatic valves. International Journal of Prognostics and Health Management, 2(2).
10. Daigle, M., & Goebel, K. (2013). Model-based prognostics with concurrent damage progression processes. Systems, Man, and Cybernetics: Systems, IEEE Transactions on, 43(3), 535-546. doi: 10.1109/TSMCA.2012.2207109
11. 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 (pp. 326-335).
12. de Finetti, B. (1977). Theory of probability, Volumes I and II. Bull. Amer. Math. Soc. 83 (1977), 94-97, 0002-9904.
13. deNeufville, R. (2004). Uncertainty management for engineering systems planning and design. In Engineering systems symposium mit. Cambridge, MA.
14. Der Kiureghian, A., Lin, H.-Z., & Hwang, S.-J. (1987). Second-order reliability approximations. Journal of Engineering Mechanics, 113(8), 1208-1225.
15. Dolinski, K. (1983). First-order second-moment approximation in reliability of structural systems: critical review and alternative approach. Structural Safety, 1(3), 211-231.
16. Engel, S. J., Gilmartin, B. J., Bongort, K., & Hess, A. (2000). Prognostics, the real issues involved with predicting life remaining. In Aerospace Conference Proceedings, 2000 IEEE (Vol. 6, pp. 457-469).
17. Farrar, C. R., & Lieven, N. A. (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.
18. Glynn, P. W., & Iglehart, D. L. (1989). Importance sampling for stochastic simulations. Management Science, 35(11), 1367-1392.
19. 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).
20. Haldar, A., & Mahadevan, S. (2000). Probability, reliability, and statistical methods in engineering design. John Wiley & Sons, Incorporated.
21. Hastings, D. and 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.
22. Hohenbichler, M., & Rackwitz, R. (1983). First-order concepts in system reliability. Structural safety, 1(3), 177-188.
23. Liao, H., Zhao, W., & Guo, H. (2006). Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model. In Reliability and Maintainability Symposium, 2006. RAMS'06. Annual (pp. 127-132).
24. Loh, W.-L. (1996). On latin hypercube sampling. The annals of statistics, 24(5), 2058-2080.
25. Ng, K.-C., & Abramson, B. (1990). Uncertainty management in expert systems. IEEE Expert Systems, 20.
26. 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
27. Popper, K. (1959). The propensity interpretation of probability. The British journal for the philosophy of science, 10(37), 25-42.
28. Saha, B., & Goebel, K. (2008). Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques. In Aerospace Conference, 2008 IEEE (pp. 1-8).
29. Sankararaman, S. (2012). Uncertainty quantification and integration in engineering systems (Ph.D. Dissertation). Vanderbilt University.
30. 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).
31. Sankararaman, S., & Goebel, K. (2013, Apr). 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.
32. Sankararaman, S., Ling, Y., & Mahadevan, S. (2011). Uncertainty quantification and model validation of fatigue crack growth prediction. Engineering Fracture Mechanics, 78(7), 1487-1504.
33. 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).
34. Sankararaman, S., & Mahadevan, S. (2011). Likelihoodbased representation of epistemic uncertainty due to sparse point data and/or interval data. Reliability Engineering & System Safety, 96(7), 814-824.
35. Szabó, L. (2007). Objective probability-like things with and without objective indeterminism. Studies In History and Philosophy of Science Part B: Studies In History and Philosophy of Modern Physics, 38(3), 626-634.
36. 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
37. Van Zandt, J. R. (2001). A more robust unscented transform. In International symposium on optical science and technology (pp. 371-380).
38. VonMises, R. (1981). Probability, statistics and truth. Dover Publications.