Uncertainty in Prognostics and Health Management: An Overview

Shankar Sankararaman and Kai Goebel
Submission Type: 
Full Paper
Supporting Agencies (optional): 
NASA Ames Research Center
phmce_14_005.pdf249.06 KBJune 5, 2014 - 12:16am

This paper presents an overview of various aspects of uncertainty quantification in prognostics and health management. Since prognostics deals with predicting the future behavior of engineering systems and it is almost practically impossible to precisely predict future events, 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. However, one important issue is 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 explains that the computation of the remaining useful life is more meaningful in the context of condition-based monitoring; this computation needs to be approached as an uncertainty propagation problem, and the different types of statistical uncertainty propagation methods are discussed in this regard.

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Submission Keywords: 
testing-based prognostics
Submission Topic Areas: 
Uncertainty Quantification and Management in PHM
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