Uncertainty Representation, Quantification, and Management in Prognostics

This chapter analyzes the significance, interpretation, quantification, and management of uncertainty in prognostics, with an emphasis on predicting the remaining useful life (RUL) of engineering systems and components. Prognostics deals with predicting the future behavior of engineering systems and is affected by various sources of uncertainty. The chapter explains the paramount importance of uncertainty interpretation, quantification, and management in prognostics, focusing both on testing‐based life prediction and condition‐based prognostics. It discusses the suitability of classical and subjective approaches to uncertainty. The chapter also explains computational methods for uncertainty quantification and management. The most intuitive method for uncertainty propagation is to make use of Monte Carlo simulation (MCS). Having calculated the uncertainty in the prediction, it is necessary to facilitate uncertainty management from a decision‐making point of view in order to facilitate risk mitigation activities. The chapter presents a case study on the power system of an unmanned aerial vehicle.