A unifying review of NDE models towards optimal decision support

TL;DR

This paper reviews probabilistic models in non-destructive evaluation (NDE), unifies them into a comprehensive framework, and discusses their implications for decision-making and experimental design in asset integrity management.

Contribution

It introduces a unifying framework for probabilistic NDE models, enhancing understanding of model calibration, application, and impact on maintenance decisions.

Findings

Unified probabilistic models for NDE are interconnected within a comprehensive framework.

Model choice significantly influences maintenance decision outcomes.

Experimental design impacts NDE system performance in decision-making contexts.

Abstract

Non-destructive evaluation (NDE) inspections are 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, and the precision of the measurement device. In this paper, we review existing probabilistic models of NDE andshow how they are connected within a unifying framework. This frameworkprovides insights into how these models should be learned, calibrated, and applied. We investigate and highlighthow 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.