  Non-destructive evaluation (NDE) through inspection and monitoring is an
integral part of asset integrity management. The relationship between the
condition of interest and the quantity measured by NDE is described with
probabilistic models such as PoD or ROC curves. These models are used to assess
the quality of the information provided by NDE systems, which is affected by
factors such as the experience of the inspector, environmental conditions, ease
of access, or imprecision in the measuring device. In this paper, we show how
the different probabilistic models of NDE are connected within a unifying
framework. Using this framework, we derive insights into how these models
should be learned, calibrated, and applied. We investigate how the choice of
the model can affect the maintenance decisions taken on the basis of NDE
results. In addition, we analyze the impact of experimental design on the
performance of a given NDE system in a decision-making context.
