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Bridging the gap between HRA research and HRA practice: A Bayesian network version of SPAR-H

机译：缩小HRA研究与HRA实践之间的差距：SPAR-H的贝叶斯网络版本

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摘要

The shortcomings of Human Reliability Analysis (HRA) have been a topic of discussion for over two decades. Repeated attempts to address these limitations have resulted in over 50 HRA methods, and the HRA research community continues to develop new methods. However, there remains a gap between the methods developed by HRA researchers and those actually used by HRA practitioners. Bayesian Networks (BNs) have become an increasingly popular part of the risk and reliability analysis framework over the past decade. BNs provide a framework for addressing many of the shortcomings of HRA from a researcher perspective and from a practitioner perspective. Several research groups have developed advanced HRA methods based on BNs, but none of these methods has been adopted by HRA practitioners in the U.S. nuclear power industry or at the U.S. Nuclear Regulatory Commission. In this paper we bridge the gap between HRA research and HRA practice by building a BN version of the widely used SPAR-H method. We demonstrate how the SPAR-H BN can be used by HRA practitioners, and we also demonstrate how it can be modified to incorporate data and information from research to advance HRA practice. The SPAR-H BN can be used as a starting point for translating HRA research efforts and advances in scientific understanding into real, timely benefits for HRA practitioners.
机译：二十多年来，人类可靠性分析（HRA）的缺点一直是人们讨论的话题。反复尝试解决这些局限性已导致了50多种HRA方法，并且HRA研究界继续开发新方法。但是，HRA研究人员开发的方法与HRA从业人员实际使用的方法之间仍然存在差距。在过去的十年中，贝叶斯网络（BN）已成为风险和可靠性分析框架中越来越受欢迎的一部分。 BN为从研究人员和从业人员的角度解决HRA的许多缺点提供了一个框架。几个研究小组已经开发出了基于BN的高级HRA方法，但美国核电行业或美国核监管委员会的HRA实践人员均未采用这些方法。在本文中，我们通过构建广泛使用的SPAR-H方法的BN版本来弥合HRA研究与HRA实践之间的差距。我们演示了HPAR专业人员如何使用SPAR-H BN，并且还演示了如何对其进行修改以合并研究中的数据和信息，从而促进HRA实践。 SPAR-H BN可以用作将HRA研究工作和对科学认识的进步转化为HRA从业人员真正，及时的收益的起点。

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