Decision-theoretic inspection planning using imperfect and incomplete data

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2021-11-10 DOI:10.1017/dce.2021.18
D. Di Francesco, M. Chryssanthopoulos, M. Faber, U. Bharadwaj
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引用次数: 8

Abstract

Abstract Attempts to formalize inspection and monitoring strategies in industry have struggled to combine evidence from multiple sources (including subject matter expertise) in a mathematically coherent way. The perceived requirement for large amounts of data are often cited as the reason that quantitative risk-based inspection is incompatible with the sparse and imperfect information that is typically available to structural integrity engineers. Current industrial guidance is also limited in its methods of distinguishing quality of inspections, as this is typically based on simplified (qualitative) heuristics. In this paper, Bayesian multi-level (partial pooling) models are proposed as a flexible and transparent method of combining imperfect and incomplete information, to support decision-making regarding the integrity management of in-service structures. This work builds on the established theoretical framework for computing the expected value of information, by allowing for partial pooling between inspection measurements (or groups of measurements). This method is demonstrated for a simulated example of a structure with active corrosion in multiple locations, which acknowledges that the data will be associated with some precision, bias, and reliability. Quantifying the extent to which an inspection of one location can reduce uncertainty in damage models at remote locations has been shown to influence many aspects of the expected value of an inspection. These results are considered in the context of the current challenges in risk based structural integrity management.
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基于不完善和不完整数据的决策理论检验计划
摘要试图将工业中的检查和监测策略形式化,很难以数学上连贯的方式将来自多个来源(包括主题专业知识)的证据结合起来。对大量数据的感知需求经常被认为是基于风险的定量检查与结构完整性工程师通常可以获得的稀疏和不完善的信息不兼容的原因。目前的行业指南在区分检查质量的方法上也受到限制,因为这通常基于简化的(定性)启发式方法。在本文中,贝叶斯多级(部分池化)模型被提出为一种灵活透明的方法,将不完美和不完整的信息相结合,以支持在役结构完整性管理的决策。这项工作建立在已建立的计算信息期望值的理论框架之上,允许在检查测量(或测量组)之间进行部分汇集。该方法在多个位置具有主动腐蚀的结构的模拟示例中得到了验证,这承认数据将与一些精度、偏差和可靠性相关。量化一个位置的检查可以在多大程度上降低远程位置损伤模型的不确定性,这已被证明会影响检查预期值的许多方面。这些结果是在当前基于风险的结构完整性管理面临挑战的背景下考虑的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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