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 conditionbased 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 ismoremeaningful in the context of condition-based monitoring and needs to be approached as an uncertainty propagation problem. Numerical examples are presented to illustrate the various concepts discussed in the paper.
How to Cite
CBM, filtering, uncertainty, sampling, testing-based prognostics
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