Uncertainty in Prognostics and Systems Health Management



Published Nov 3, 2020
Shankar Sankararaman Kai Goebel


This paper presents an overview of various aspects of uncertainty quantification and management in prognostics and systems health management. Prognostics deals with predicting possible future failures in different types of engineering systems. It is almost practically impossible to precisely predict future events; therefore, 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 condition-based health management. This paper explains 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 illustrates that the computation of the remaining useful life is more meaningful in the context of condition-based monitoring and needs to be approached
as an uncertainty propagation problem. Further, uncertainty management issues are discussed and possible solutions are explored. Numerical examples are presented to illustrate the various concepts discussed in the paper.

Abstract 265 | PDF Downloads 288



prognostics, sensitivity analysis, uncertainty, remaining useful life prediction

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