Risk assessment methods are often used in complex industrial systems to avoid risks and reduce losses. The existing methods have not effectively solved the problems of lack of evaluation data and the interpretability of the entire evaluation process. This paper proposes a new risk assessment model based on the belief rule base (BRB) and Fault Tree Analysis (FTA). The FTA algorithm overcomes the difficulties of traditional BRB model in obtaining expert knowledge, clear indicators, and establishing logical relationships. This method establishes FTA rules based on the BRB model and expands the knowledge base through the FTA algorithm. A Bayesian network is applied as a conversion bridge between the FTA and BRB model. In addition, the model is optimized to reduce the uncertainty in the model. The method proposed is described by a case and its effectiveness is verified.

风险评估方法通常用于复杂的工业系统中，以避免风险并减少损失。现有方法未能有效解决评估数据缺乏和整个评估过程的可解释性的问题。本文提出了一种基于信念规则库（BRB）和故障树分析（FTA）的风险评估模型。FTA算法克服了传统BRB模型在获得专家知识，清晰指标和建立逻辑关系方面的困难。该方法基于BRB模型建立FTA规则，并通过FTA算法扩展知识库。贝叶斯网络被用作FTA和BRB模型之间的转换桥梁。另外，对模型进行了优化以减少模型中的不确定性。