Safety-critical infrastructures must operate in a safe and reliable way. Fault tree analysis is a widespread method used for risk assessment of these systems: fault trees (FTs) are required by, e.g., the Federal Aviation Administration and the Nuclear Regulatory Commission. In spite of their popularity, little work has been done on formulating structural queries about FT and analyzing these, e.g., when evaluating potential scenarios, and to give practitioners instruments to formulate queries on FTs in an understandable yet powerful way. In this paper, we aim to fill this gap by extending BFL [32], a logic that reasons about Boolean FTs. To do so, we introduce a Probabilistic Fault tree Logic (PFL). PFL is a simple, yet expressive logic that supports easier formulation of complex scenarios and specification of FT properties that comprise probabilities. Alongside PFL, we present LangPFL, a domain specific language to further ease property specification. We showcase PFL and LangPFL by applying them to a COVID-19 related FT and to a FT for an oil/gas pipeline. Finally, we present theory and model checking algorithms based on binary decision diagrams (BDDs).
翻译:安全关键基础设施必须以安全可靠的方式运行。故障树分析是一种广泛用于风险评估的方法,需要符合例如联邦航空管理局和核监管委员会的要求。尽管备受青睐,但在关于FT的结构性查询和分析方面,很少有研究工作,例如在评估潜在情景时,给从业人员提供了在可理解但功能强大的方法上查询关于FT的工具。在本文中,我们旨在通过扩展BFL [32],一种关于布尔FT进行推理的逻辑来填补这一空白。为此,我们引入了一种基于概率的故障树逻辑(PFL)。PFL是一种简单但富有表现力的逻辑,支持更容易地制定复杂的场景和规定包含概率的FT属性。与PFL一起,我们提出了LangPFL,一种领域特定语言,进一步简化了属性规范。我们通过应用它们于COVID-19相关FT和油气管道FT来展示PFL和LangPFL。最后,我们提出了基于二进制决策图的理论和模型检查算法。