LLM-based method for constructing fault trees

Fault Tree Analysis (FTA) is a systematic deductive technique for identifying causal relationships that link component failures to undesired system-level events. It is used to build a logical model of the undesired event, enabling the evaluation of system reliability. Building fault trees typically requires considerable modeling effort and is subject to analyst bias. To address these challenges, this work proposes a method based on Large Language Models (LLMs) to support safety analysts in building FTs. The method is evaluated on an artificial case study of a liquid mixing subsystem of a chemical plant.