Human error is one of the largest contributing factors to unsafe operation and accidents in high‐speed train operation. As a well‐known second‐generation human reliability analysis (HRA) technique, the cognitive reliability and error analysis method (CREAM) has been introduced to address HRA problems in various fields. Nevertheless, current CREAM models are insufficient to deal with the HRA problem that need to consider the interdependencies between the Common Performance Conditions (CPCs) and determine the weights of these CPCs, simultaneously. Hence, the purpose of this paper is to develop a hybrid HRA model by integrating CREAM, the interval type‐2 fuzzy sets, and analytic network process (ANP) to overcome this drawback. Firstly, the interval type‐2 fuzzy sets are utilized to express the highly uncertain information of CPCs. Secondly, the ANP is incorporated into the CREAM to depict the interdependencies between the CPCs and determine their weights. Furthermore, human error probability (HEP) can be calculated based on the obtained weights. Finally, an illustrative example of the HRA problem in high‐speed train operation is proposed to demonstrate the application and validity of the proposed HRA model. The results indicate that experts prefer to express their preferences by fuzzy sets rather than crisp values, and the interdependences between the CPCs can be better depicted in the proposed model.

中文翻译：

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类型2模糊环境下基于分析网络过程的扩展CREAM模型用于高速列车运行中的人员可靠性分析

人为错误是造成高速列车运行中不安全运行和事故的最大因素之一。作为一种众所周知的第二代人类可靠性分析（HRA）技术，已引入认知可靠性和错误分析方法（CREAM）来解决各个领域的HRA问题。但是，当前的CREAM模型不足以解决HRA问题，而HRA问题需要考虑通用绩效条件（CPC）之间的相互依赖性并同时确定这些CPC的权重。因此，本文的目的是通过集成CREAM，区间2型模糊集和分析网络过程（ANP）来开发混合HRA模型，以克服这一缺点。首先，使用区间2型模糊集来表示CPC的高度不确定的信息。其次，ANP已合并到CREAM中，以描述CPC之间的相互依赖性并确定其权重。此外，可以基于获得的权重来计算人为错误概率（HEP）。最后，提出了高速列车运行中的HRA问题的说明性示例，以证明所提出的HRA模型的应用和有效性。结果表明，专家更喜欢通过模糊集而不是清晰的值来表达自己的偏好，并且在建议的模型中可以更好地描述每次点击费用之间的相互依赖性。提出了高速列车运行中HRA问题的说明性示例，以证明所提出的HRA模型的应用和有效性。结果表明，专家更喜欢通过模糊集而不是清晰的值来表达自己的偏好，并且在建议的模型中可以更好地描述每次点击费用之间的相互依赖性。提出了高速列车运行中HRA问题的说明性示例，以证明所提出的HRA模型的应用和有效性。结果表明，专家更喜欢通过模糊集而不是清晰的值来表达自己的偏好，并且在建议的模型中可以更好地描述每次点击费用之间的相互依赖性。