论文标题
贝叶斯学习系统可靠性的因果关系
Bayesian Learning of Causal Relationships for System Reliability
论文作者
论文摘要
因果理论现已广泛开发，在医学和公共卫生中有许多应用。但是，在可靠性学科中，尽管因果关系是该领域的关键概念，但理论上的关注却少得多。在本文中，我们将演示如何通过树（更具体地说是链事件图）转化已建立的因果方法的某些方面，以帮助可靠性理论的领域，以帮助失败的概率建模。我们进一步展示了如何将特定于可靠性的特定因果关系的特定特定概念导入到更通用的因果代数中，从而演示这些学科如何互相告知。本文通过与大型电气配电公司相关的维护记录进行详细分析来告知本文。使用我们此处介绍的新图形框架提取和分析了这些自然语言文本中嵌入的因果假设。
Causal theory is now widely developed with many applications to medicine and public health. However within the discipline of reliability, although causation is a key concept in this field, there has been much less theoretical attention. In this paper, we will demonstrate how some aspects of established causal methodology can be translated via trees, and more specifically chain event graphs, into domain of reliability theory to help the probability modeling of failures. We further show how various domain specific concepts of causality particular to reliability can be imported into more generic causal algebras and so demonstrate how these disciplines can inform each other. This paper is informed by a detailed analysis of maintenance records associated with a large electrical distribution company. Causal hypotheses embedded within these natural language texts are extracted and analyzed using the new graphical framework we introduced here.
