Uncertainty in Prognostics and Health Management: An Overview
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Abstract
This paper presents an overview of various aspects of uncertainty quantification in prognostics and health management. Since prognostics deals with predicting the future behavior of engineering systems and it is almost practically impossible to precisely predict future events, it is necessary to account for the different sources of uncertainty that affect prognostics, and develop a systematic framework for uncertainty quantification and management in this context. Researchers have developed computational methods for prognostics, both in the context of testing-based health management and conditionbased health management. However, one important issue is that, the interpretation of uncertainty for these two different types of situations is completely different. While both the frequentist (based on the presence of true variability) and Bayesian (based on subjective assessment) approaches are applicable in the context of testing-based health management, only the Bayesian approach is applicable in the context of condition-based health management. This paper explains that the computation of the remaining useful life ismoremeaningful in the context of condition-based monitoring and needs to be approached as an uncertainty propagation problem. Numerical examples are presented to illustrate the various concepts discussed in the paper.
How to Cite
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CBM, filtering, uncertainty, sampling, testing-based prognostics
Bucher, C. G. (1988). Adaptive samplingan iterative fast monte carlo procedure. Structural Safety, 5(2), 119–126.
Caflisch, R. E. (1998). Monte carlo and quasi-monte carlo methods. Acta numerica, 1998, 1–49.
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).
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).
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.
Daigle, M., & Goebel, K. (2010). Model-based prognostics under limited sensing. In Aerospace Conference, 2010 IEEE (pp. 1–12).
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).
Der Kiureghian, A., Lin, H.-Z., & Hwang, S.-J. (1987). Second-order reliability approximations. Journal of Engineering Mechanics, 113(8), 1208–1225.
Dolinski, K. (1983). First-order second-moment approximation in reliability of structural systems: critical review and alternative approach. Structural Safety, 1(3), 211–231.
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).
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.
Glynn, P. W., & Iglehart, D. L. (1989). Importance sampling for stochastic simulations. Management Science, 35(11), 1367–1392.
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).
Haldar, A., & Mahadevan, S. (2000). Probability, reliability, and statistical methods in engineering design. John Wiley & Sons, Incorporated.
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..
Hohenbichler, M., & Rackwitz, R. (1983). First-order concepts in system reliability. Structural safety, 1(3), 177–188.
Kiureghian,A. D. (1989).Measures of structural safety under imperfect states of knowledge. Journal of Structural Engineering, 115(5), 1119–1140.
Liao, H., Zhao, W., & Guo, H. (2006). Predicting remaining useful life of an individual unit using proportional haz-ardsmodel and logistic regressionmodel. In Reliability and Maintainability Symposium, 2006. RAMS’06. Annual (pp. 127–132).
Loh,W.-L. (1996). On latin hypercube sampling. The annals of statistics, 24(5), 2058–2080.
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
Saha, B., & Goebel, K. (2008). Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques. In Aerospace Conference, 2008 IEEE (pp.1–8).
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).
Sankararaman, S., & Goebel, K. (2013a). A novel computational methodology for uncertainty quantification in prognostics using the most probable point concept. In Annual conference of the prognostics and health management society.
Sankararaman, S., & Goebel, K. (2013b). Remaining useful life estimation in prognosis: An uncertainty propagation problem. In 2013 aiaa infotech@ aerospace conference.
Sankararaman, S., & Goebel, K. (2013c). Why is the remaining useful life prediction uncertain? In Annual conference of the prognostics and health management society.
Sankararaman, S., & Goebel, K. (2014). Uncertainty in prognostics: Computational methods and practical challenges. In Aerospace Conference, 2014 IEEE (pp. 1–9).
Sankararaman, S., Ling, Y., & Mahadevan, S. (2011). Uncertainty quantification and model validation of fatigue crack growth prediction. Engineering Fracture Mechanics, 78(7), 1487–1504.
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), 15 pages.
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.
Sankararaman, S., & Mahadevan, S. (2013a). Distribution type uncertainty due to sparse and imprecise data. Mechanical Systems and Signal Processing, 37(1), 182–198.
Sankararaman, S., & Mahadevan, S. (2013b). Separating the contributions of variability and parameter uncertainty in probability distributions. Reliability Engineering & System Safety, 112, 187–199.
Tang, L., Kacprzynski, G., Goebel, K., & Vachtsevanos, G. (2009,march). Methodologies for uncertaintymanagement in prognostics. In Aerospace conference, 2009 IEEE (p. 1 -12).
Van Zandt, J. R. (2001). A more robust unscented transform. In International symposium on optical science and technology (pp. 371–380).
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