Remaining Useful Life Prediction through Failure Probability Computation for Condition-based Prognostics

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Shankar Sankararaman

Abstract

The key goal in prognostics is to predict the remaining use- ful life (RUL) of engineering systems in order to guide dif- ferent types of decision-making activities such as path plan- ning, fault mitigation, etc. The remaining useful life of an engineering component/system is defined as the first future time-instant in which a set of safety threshold conditions are violated. The violation of these conditions may render the system inoperable or even lead to catastrophic failure. This paper develops a computational methodology to analyze the aforementioned set of safety threshold conditions, calculate the probability of failure, and in turn, proposes a new hy- pothesis to mathematically connect such probability to the re- maining useful life prediction. A significant advantage of the proposed methodology is that it is possible to learn important properties of the remaining useful life, without simulating the system until the occurrence of failure; this feature renders the proposed approach unique in comparison with existing direct- RUL-prediction approaches. The methodology also provides a systematic way of treating the different sources of uncer- tainty that may arise from imprecisely known future operating conditions, inaccurate state-of-health state estimates, use of imperfect models, etc. The proposed approach is developed using a model-based framework prognostics using principles of probability, and illustrated using a numerical example.

How to Cite

Sankararaman, S. (2015). Remaining Useful Life Prediction through Failure Probability Computation for Condition-based Prognostics. Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2566
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Keywords

failure prediction

References
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, 7, 1–49.
Chen, M., & Rincon-Mora, G. A. (2006). Accurate electrical battery model capable of predicting runtime and I-V performance. IEEE Transactions on Energy Con- version, 21(2), 504 - 511.
Daigle, M., & Goebel, K. (2011). A model-based prognostics approach applied to pneumatic valves. International Journal of Prognostics and Health Management, 2(2), 16 pages.
Daigle, M., & Goebel, K. (2013). Model-based prog- nostics with concurrent damage progression pro- cesses. Systems, Man, and Cybernetics: Sys- tems, IEEE Transactions on, 43(3), 535-546. doi: 10.1109/TSMCA.2012.2207109
Daigle, M., Saxena, A., & Goebel, K. (2012). An efficient deterministic approach to model-based prediction un- certainty estimation. In Annual conference of the prog- nostics 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.
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. Philosophi- cal 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.
Haldar, A., & Mahadevan, S. (2000). Probability, reliability, and statistical methods in engineering design. John Wiley & Sons, Incorporated.
Harter, J. (1999). AFGROW users guide and technical manual. AIR FORCE RESEARCH LAB WRIGHT- PATTERSON AFB OH AIR VEHICLES DIRECTORATE.
Kulkarni, C. S., Celaya, J. R., Goebel, K., & Biswas, G. (2013). Physics based electrolytic capacitor degradation models for prognostic studies under thermal over- stress. International Journal of Prognostics and Health Management, 4(5).
Liu, Y., & Mahadevan, S. (2009). Probabilistic fatigue life prediction using an equivalent initial flaw size distribution. International Journal of Fatigue, 31(3), 476–487.
Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008). Model-based prognostic techniques applied to a suspension system. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 38(5), 1156–1168.
Newman, J. (1992). Fastran ii: a fatigue crack growth structural analysis program. National Aeronautics and Space Administration, Langley Research Center.
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, 2008. phm 2008. international conference on (pp. 1– 6).
Pugno, N., Ciavarella, M., Cornetti, P., & Carpinteri, A. (2006). A generalized paris’ law for fatigue crack growth. Journal of the Mechanics and Physics of Solids, 54(7), 1333–1349.
Robert, C., & Casella, G. (2004). Monte carlo statistical methods. Springer.
Sankararaman, S. (2015). Significance, interpretation, and quantification of uncertainty in prognostics and re- maining useful life prediction. Mechanical Systems and Signal Processing, 52, 228–247.
Sankararaman, S., Daigle, M., & Goebel, K. (2014, June). Uncertainty quantification in remaining useful life pre- diction using first-order reliability methods. Relia- bility, IEEE Transactions on, 63(2), 603-619. doi: 10.1109/TR.2014.2313801
Sankararaman, S., & Goebel, K. (2013). Why is the remaining useful life prediction uncertain. In Annual conference of the prognostics and health management soci- ety.
Saxena, A., Sankararaman, S., & Goebel, K. (2014). Performance evaluation for fleet-based and unit-based prognostic methods. In Second European conference of the Prognostics and Health Management society.
Sun, J., Zuo, H., Wang, W., & Pecht, M. G. (2012). Ap- plication of a state space modeling technique to system prognostics based on a health index for condition-based maintenance. Mechanical Systems and Signal Process- ing, 28, 585–596.
Swanson, D. C. (2001). A general prognostic tracking algo- rithm for predictive maintenance. In Aerospace confer- ence, 2001, ieee proceedings. (Vol. 6, pp. 2971–2977).
Vaidya, P., & Rausand, M. (2011). Remaining useful life, technical health, and life extension. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 225(2), 219–231.
Zio, E., & Peloni, G. (2011). Particle filtering prognostic esti- mation of the remaining useful life of nonlinear compo- nents. Reliability Engineering & System Safety, 96(3), 403–409.
Section
Technical Papers