The Bayesian network (BN) is an efficient tool for probabilistic modeling and causal inference, and it has gained considerable attentions in the field of reliability assessment. The common cause failure (CCF) is simultaneous failure of multiple elements in a system under a common cause, and it is a common phenomenon in engineering systems with dependent elements. Several models and methods have been proposed for modeling and assessment of complex systems with CCF. In this paper, a new reliability assessment method is proposed for the systems suffering from CCF in a dynamic environment. The CCF among components is characterized by a BN, which allows for bidirectional reasoning. A proportional hazards model is applied to capture the dynamic working environment of components and then the reliability function of the system is obtained. The proposed method is validated through an illustrative example, and some comparative studies are also presented.

中文翻译：

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基于贝叶斯网络和比例风险模型的共因故障系统可靠性评估

贝叶斯网络（BN）是一种用于概率建模和因果推理的有效工具，在可靠性评估领域已引起了广泛的关注。共因故障（CCF）是在共同原因下系统中多个元素同时发生故障，这在具有从属元素的工程系统中是常见现象。已经提出了几种使用CCF对复杂系统进行建模和评估的模型和方法。本文针对动态环境下遭受CCF的系统提出了一种新的可靠性评估方法。组件之间的CCF以BN为特征，该BN允许双向推理。应用比例风险模型捕获部件的动态工作环境，然后获得系统的可靠性函数。