Why is the Remaining Useful Life Prediction Uncertain

This paper discusses the significance and interpretation of uncertainty in the remaining useful life (RUL) prediction of components used in several types of engineering applications, and answers certain fundamental questions such as “Why is the RUL prediction uncertain?”, “How to interpret the uncertainty in the RUL prediction?”, and “How to compute the uncertainty in the RUL prediction?”. Prognostics and RUL prediction are affected by various sources of uncertainty. In order to make meaningful prognostics-based decision-making, it is important to analyze how these sources of uncertainty affect the remaining useful life prediction, and thereby, compute the overall uncertainty in the remaining useful life prediction. The classical (frequentist) and Bayesian (subjective) interpretations of uncertainty and their implications on prognostics are explained, and it is argued that the Bayesian interpretation of uncertainty is more suitable for remaining useful life prediction in the context of condition-based monitoring. Finally, it is demonstrated that the calculation of uncertainty in remaining useful life can be posed as an uncertainty propagation problem, and the practical challenges involved in computing the uncertainty in the remaining useful life prediction are discussed.

[1]  Matthew Daigle,et al.  A Model-Based Prognostics Approach Applied to Pneumatic Valves , 2011 .

[2]  K. Popper The Propensity Interpretation of Probability , 1959 .

[3]  László E. Szabó Objective probability-like things with and without objective indeterminism , 2007 .

[4]  Matthew Daigle,et al.  An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation , 2012 .

[5]  Matthew Daigle,et al.  Model-based prognostics under limited sensing , 2010, 2010 IEEE Aerospace Conference.

[6]  Jie Gu,et al.  Uncertainty Assessment of Prognostics of Electronics Subject to Random Vibration , 2007, AAAI Fall Symposium: Artificial Intelligence for Prognostics.

[7]  Wei-Liem Loh On Latin hypercube sampling , 1996 .

[8]  K. Doliński,et al.  First-order second-moment approximation in reliability of structural systems: Critical review and alternative approach , 1982 .

[9]  J. Celaya,et al.  Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation , 2012 .

[10]  Donald L. Iglehart,et al.  Importance sampling for stochastic simulations , 1989 .

[11]  Kai Goebel,et al.  Model-Based Prognostics With Concurrent Damage Progression Processes , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[12]  S Sankararaman,et al.  Likelihood-based representation of imprecision due to sparse point data and/or interval data , 2011 .

[13]  Shankar Sankararaman,et al.  Uncertainty quantification and integration in engineering systems , 2012 .

[14]  Keung-Chi Ng,et al.  Uncertainty management in expert systems , 1990, IEEE Expert.

[15]  Huairui Guo,et al.  Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model , 2006, RAMS '06. Annual Reliability and Maintainability Symposium, 2006..

[16]  Stephen J. Engel,et al.  Prognostics, the real issues involved with predicting life remaining , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[17]  James R. Van Zandt A more robust unscented transform , 2001 .

[18]  R. Caflisch Monte Carlo and quasi-Monte Carlo methods , 1998, Acta Numerica.

[19]  A. Wald,et al.  Probability, statistics and truth , 1939 .

[20]  Raphael T. Haftka,et al.  Uncertainty Reduction of Damage Growth Properties Using Structural Health Monitoring , 2010 .

[21]  K. Goebel,et al.  Analytical algorithms to quantify the uncertainty in remaining useful life prediction , 2013, 2013 IEEE Aerospace Conference.

[22]  P. E. James T. P. Yao,et al.  Probability, Reliability and Statistical Methods in Engineering Design , 2001 .

[23]  K. Goebel,et al.  Prognostics approach for power MOSFET under thermal-stress aging , 2012, 2012 Proceedings Annual Reliability and Maintainability Symposium.

[24]  Charles R Farrar,et al.  Damage prognosis: the future of structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[25]  G. Kacprzynski,et al.  Advances in uncertainty representation and management for particle filtering applied to prognostics , 2008, 2008 International Conference on Prognostics and Health Management.

[26]  Sankaran Mahadevan,et al.  Uncertainty Quantification in Fatigue Crack Growth Prognosis , 2011 .

[27]  C. Bucher Adaptive sampling — an iterative fast Monte Carlo procedure , 1988 .

[28]  Kai Goebel,et al.  Uncertainty Quantification in Remaining Useful Life of Aerospace Components using State Space Models and Inverse FORM , 2013 .

[29]  Hugh McManus,et al.  A framework for understanding uncertainty and its mitigation and exploitation in complex systems , 2006, IEEE Engineering Management Review.

[30]  M. Eldred,et al.  Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions , 2008 .

[31]  A. Kiureghian,et al.  Second-Order Reliability Approximations , 1987 .

[32]  R. Rackwitz,et al.  First-order concepts in system reliability , 1982 .

[33]  Sankaran Mahadevan,et al.  Uncertainty quantification and model validation of fatigue crack growth prediction , 2011 .

[34]  George Vachtsevanos,et al.  Methodologies for uncertainty management in prognostics , 2009, 2009 IEEE Aerospace conference.