Applying Bayesian Networks to TRL Assessments – Innovation in Systems Engineering

IF 1 4区 工程技术 Q4 INSTRUMENTS & INSTRUMENTATION
Insight Pub Date : 2024-12-21 DOI:10.1002/inst.12516
Marc F. Austin, Virginia Ahalt, Erin Doolittle, Cheyne Homberger, George A. Polacek, Donal M. York
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引用次数: 0

Abstract

Currently, technology readiness assessments (TRAs) are used in determining the maturity of the critical technology elements (CTEs) of a system as it moves forward in the system development life cycle. The TRA method uses technology readiness levels (TRLs) as the decision metric. TRL values are assessed and determined by subject matter experts (SMEs). Since expert evaluators often differ in their judgment when scoring a system element against the TRL scale criteria, this paper argues for the use of a Bayesian network model to provide a mathematical method to consistently combine and validate the judgment of these SMEs and increase the confidence in the determination of the readiness of system components and their technologies.

将贝叶斯网络应用于TRL评估——系统工程中的创新
目前,技术准备评估(TRAs)用于确定系统在系统开发生命周期中前进时关键技术元素(cte)的成熟度。TRA方法使用技术准备等级(trl)作为决策度量。TRL值由主题专家(sme)评估和确定。由于专家评估者在根据TRL量表标准对系统元素进行评分时,他们的判断往往不同,因此本文主张使用贝叶斯网络模型来提供一种数学方法,以一致地组合和验证这些中小企业的判断,并增加对确定系统组件及其技术的准备程度的信心。
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
Insight
Insight 工程技术-材料科学:表征与测试
CiteScore
1.50
自引率
9.10%
发文量
0
审稿时长
2.8 months
期刊介绍: Official Journal of The British Institute of Non-Destructive Testing - includes original research and devlopment papers, technical and scientific reviews and case studies in the fields of NDT and CM.
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