{"title":"Quantile POD for nondestructive evaluation with hit--miss data","authors":"Yew-Meng Koh, W. Meeker","doi":"10.1080/09349847.2017.1374493","DOIUrl":null,"url":null,"abstract":"ABSTRACT Probability of detection (POD) is commonly used to measure a nondestructive evaluation (NDE) inspection procedure’s performance. Due to inherent variability in the inspection procedure caused by variability in factors such as crack morphology and operators, it is important, for some purposes, to model POD as a random function. Traditionally, inspection variabilities are pooled and an estimate of the mean POD (averaged over all sources of variability) is reported. In some applications it is important to know how poor typical inspections might be, and this question is answered by estimating a quantile of the POD distribution. This article shows how to fit and compare different models to repeated-measures hit--miss data with multiple inspections with different operators for each crack and shows how to estimate the mean POD as well as quantiles of the POD distribution for binary (hit--miss) NDE data. We also show how to compute credible intervals (quantifying uncertainty due to limited data) for these quantities using a Bayesian estimation approach. We use NDE for the detection of fatigue cracks as the motivating example, but the concepts apply more generally to other NDE applications areas.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"44 1","pages":"111 - 89"},"PeriodicalIF":1.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research in Nondestructive Evaluation","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1080/09349847.2017.1374493","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 2
Abstract
ABSTRACT Probability of detection (POD) is commonly used to measure a nondestructive evaluation (NDE) inspection procedure’s performance. Due to inherent variability in the inspection procedure caused by variability in factors such as crack morphology and operators, it is important, for some purposes, to model POD as a random function. Traditionally, inspection variabilities are pooled and an estimate of the mean POD (averaged over all sources of variability) is reported. In some applications it is important to know how poor typical inspections might be, and this question is answered by estimating a quantile of the POD distribution. This article shows how to fit and compare different models to repeated-measures hit--miss data with multiple inspections with different operators for each crack and shows how to estimate the mean POD as well as quantiles of the POD distribution for binary (hit--miss) NDE data. We also show how to compute credible intervals (quantifying uncertainty due to limited data) for these quantities using a Bayesian estimation approach. We use NDE for the detection of fatigue cracks as the motivating example, but the concepts apply more generally to other NDE applications areas.
期刊介绍:
Research in Nondestructive Evaluation® is the archival research journal of the American Society for Nondestructive Testing, Inc. RNDE® contains the results of original research in all areas of nondestructive evaluation (NDE). The journal covers experimental and theoretical investigations dealing with the scientific and engineering bases of NDE, its measurement and methodology, and a wide range of applications to materials and structures that relate to the entire life cycle, from manufacture to use and retirement.
Illustrative topics include advances in the underlying science of acoustic, thermal, electrical, magnetic, optical and ionizing radiation techniques and their applications to NDE problems. These problems include the nondestructive characterization of a wide variety of material properties and their degradation in service, nonintrusive sensors for monitoring manufacturing and materials processes, new techniques and combinations of techniques for detecting and characterizing hidden discontinuities and distributed damage in materials, standardization concepts and quantitative approaches for advanced NDE techniques, and long-term continuous monitoring of structures and assemblies. Of particular interest is research which elucidates how to evaluate the effects of imperfect material condition, as quantified by nondestructive measurement, on the functional performance.