A Novel Computational Methodology for Uncertainty Quantification in Prognostics Using The Most Probable Point Concept
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Abstract
This paper develops a novel computational approach to quantify the uncertainty in prognostics in the context of condition- based monitoring. Prognostics consists of two major steps; first, it is necessary to estimate the state of health at any time instant, and then, it is required to predict the remaining useful life of the engineering component/system of interest. While the topic of estimation has been addressed through different types of Bayesian tracking techniques, this paper primarily focuses on the second aspect of future prediction and remain- ing useful life computation, which is influenced by several sources of uncertainty. Therefore, it is important to identify these sources of uncertainty, and quantify their combined ef- fect on the remaining useful life prediction. The computation of uncertainty in remaining useful life can be treated as an uncertainty propagation problem which can be solved using probabilistic techniques. This paper investigates the use of the Most Probable Point approach (which was originally developed to estimate the failure probability of structural systems) for calculating the probability distribution of the remaining useful life prediction. The proposed methodology is illustrated using a battery which is used to power an un- manned aerial vehicle.
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uncertainty management, Uncertainty Quantification, Remaining useful Life, model-based prognosis
Daigle, M., Saxena, A., & Goebel, K. (2012). An efficient de- terministic approach to model-based prediction uncertainty estimation. In Annual Conference of the Prognostics and Health Management Society.
DeCastro, J. A. (2009). Exact nonlinear filtering and pre- diction in process model-based prognostics. In Annual Conference of the Prognostics and Health Management Society. San Diego, CA..
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.
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.
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).
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.
Haldar, A., & Mahadevan, S. (2000). Probability, reliability, and statistical methods in engineering design. John Wiley & Sons, Inc.
Inman, D. J., Farrar, C. R., Junior, V. L., & Junior, V. S. (2005). Damage prognosis for aerospace, civil and mechanical systems. Wiley.
Liu, P.-L., & Der Kiureghian, A. (1986). Multivariate distribution models with prescribed marginals and co- variances. Probabilistic Engineering Mechanics, 1(2), 105–112.
Morgenstern, D. (1956). Einfache beispiele zweidimension- aler verteilungen. Mitt. Math. Statist, 8(1), 234–235.
Nataf, A. (1962). De ́termination des distributions de proba- bilite ́s dont les marges sont donne ́es. Comptes rendus de lacademie des sciences, 225, 42–43.
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).
Saha, B., & Goebel, K. (2008). Uncertainty management for diagnostics and prognostics of batteries using bayesian techniques. In Aerospace Conference, 2008 IEEE (p. 1 -8).
Saha, B., Quach, C. C., & Goebel, K. (2012). Optimizing battery life for electric uavs using a bayesian framework. In Aerospace Conference, 2012 IEEE.
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). Remaining useful life estimation in prognosis: An uncertainty propagation problem. In 2013 AIAA Infotech@Aerospace Conference.
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.
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.
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).
Sankararaman, S., & Mahadevan, S. (2011a). Likelihood- based 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. (2011b). Uncertainty quantification in structural damage diagnosis. Structural Control and Health Monitoring, 18(8), 807–824.
Sankararaman, S., & Mahadevan, S. (2013). Bayesian methodology for diagnosis uncertainty quantification and health monitoring. Structural Control and Health Monitoring, 20(1), 88–106.
Tang, L., Kacprzynski, G. J., Goebel, K., & Vachtsevanos, G. (2009). Methodologies for uncertainty management in prognostics. In Aerospace Conference, 2009 IEEE (pp. 1–12).
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.
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