Uncertainty in prognostics: Computational methods and practical challenges

This paper discusses the topic of uncertainty quantification in prognostics and explains the importance of accurately estimating such uncertainty in order to aid risk-informed operational decision-making. Since prognostics deals with predicting the future behavior of engineering systems, it is impossible to accurately predict the future response, and therefore, it is necessary to compute the uncertainty associated with such prediction. This paper discusses the various sources of uncertainty that influence prognostics and explains that the problem of quantifying their combined effect on prognostics can be posed as an uncertainty propagation problem. Different types of uncertainty quantification methods - sampling methods and analytical methods - are reviewed and their applicability to prognostics is investigated. The practical challenges involved in applying these methods for online health monitoring purposes are discussed. Finally, the various concepts presented in this paper are illustrated using a numerical example.