Review of Literature for Model Assisted Probability of Detection

Abstract

This report documents a literature survey of so-called "Model Assisted Probability of Detection" (MAPOD) approaches that may be useful in determining the probability of detection (POD), a metric for quantifying the performance of nondestructive evaluation (NDE) methods. The objective of this report is to summarize MAPOD concepts that have been proposed to date in order to assess specific approaches that may be appropriate for application to improve estimates of POD for field NDE of nuclear power plant components. The limitations of laboratory-based studies to replicate actual field conditions are well-recognized and not limited to the nuclear power industry. Probability of detection estimates based on laboratory studies generally provide ideal environments for performing examinations as compared field settings, which typically will result in non-conservative estimates of POD for field applications. As a consequence of this effort, the authors conclude that use of MAPOD concepts to improve estimates of field NDE performance may require access to certain field data, or alternatively, may require significant laboratory studies to assess the influence of human and environmental shaping factors. If the necessary field data cannot be made available, it is proposed that laboratory efforts will have greater chance of success by focusing on a specific examination technique and component application. The authors also find that MAPOD concepts may have a more immediate contribution to the nuclear power industry through their use in extending personnel and procedure qualifications beyond established limits. Finally, another basis was identified that may be used to adjust POD curves generated in previous reliability studies and performance demonstrations. Many of the previous studies calculated POD as the average of performance data which may be inappropriate for many applications, as the calculation may be nonconservative. A more conservative approach to estimating POD would include basing the calculation on a lower statistical quantile of the data set. v

Feasibility of Model-Assisted Probability of Detection Principles for Structural Health Monitoring Systems Based on Guided Waves for Fiber-Reinforced Composites

“…Testing and monitoring methods must be qualified in accordance with the applicable test regulations to demonstrate a sufficiently high level of reliability in damage detection and assessment. This reliability assessment is based on the POD: a monotonous function of a parameter of interest which characterises the flaw [21], [22]. This parameter is usually the size of the flaw and therefore the POD is often given as a function of it [23].…”

“…Investigations of the POD must therefore also be carried out based on simulations, also known as Model Assisted POD (MAPOD). In the literature, MAPOD approaches are basically divided into two categories [2], [22], [41], [27] Current research is initially concerned with the development and understanding of the POD concept in the context of SHM, since there is no standard procedure for determining the POD of an SHM system [27]. The present work follows a completely model-based approach.…”

A simplified method for holistic value of information computation for informed structural integrity management under uncertainty

“…Given a combination of actions (CA) and a combination of signals (CS), an updated failure probability The probability ( ) can be obtained based on the probability of detection (PoD) functions of adopted inspection methods (Dong and Frangopol 2016, Madsen, Torhaug, and Cramer 1991, Meyer et al 2014. By a PoD function, the detectable crack size ( ) of an inspection method is modelled as a random variable with a distribution.…”

Meta-Analysis and Forest Plots for Sustainability of Heavy Load Carrier Equipment Used in the Industrial Mining Environment

“…This paper carried out a survival analysis of the motor used in missiles under field conditions using the Maximum Likelihood Estimation (MLE) method and the Bayesian method [9][10][11]. R M. Mayer et al [12] reported on grouping a small amount of failure data in order to obtain a larger pool of statistical data to be used in statistical interpretation [12,13]. G Wang et al [14] proposed a method that determined which small-sample failure data of diesel engines could be used for a reliability estimation.…”