Why is the Remaining Useful Life Prediction Uncertain?

Shankar Sankararaman and Kai Goebel
Submission Type: 
Full Paper
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phmc_13_048.pdf278.18 KBOctober 11, 2013 - 12:28am

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.

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Submission Keywords: 
Uncertainty Quantification
Remaining useful Life
model-based prognosis
uncertainty propagation
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Uncertainty Quantification and Management in PHM
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