Any minor fault may spread, accumulate and enlarge through the causal link of fault in a closed-loop complex system of civil aircraft, and eventually result in unplanned downtime. In this paper, the fault propagation path model (FPPM) is proposed for system-level decomposition and simplifying the process of fault propagation analysis by combining the improved ant colony optimization algorithm (I-ACO) with the Bayesian network (BN). In FPPM, the modeling of the fault propagation path consists of three models, namely shrinking model (SM), ant colony optimization model (ACOM), and assessment model (AM). Firstly, the state space is shrunk by the most weight supported tree algorithm (MWST) at the initial establishment stage of BN. Next, I-ACO is designed to improve the structure of BN in order to study the fault propagation path accurately. Finally, the experiment is conducted from two different perspectives for the rationality of the well-trained BN’s structure. An example of practical application for the propagation path model of typical faults on the A320 air turbine starter is given to verify the validity and feasibility of the proposed method.

在民用飞机的闭环复杂系统中，任何微小的故障都可能通过故障的因果关系蔓延、积累和扩大，最终导致计划外停机。本文提出故障传播路径模型（FPPM），将改进的蚁群优化算法（I-ACO）与贝叶斯网络（BN）相结合，进行系统级分解，简化故障传播分析过程。在FPPM中，故障传播路径的建模由三个模型组成，分别是收缩模型（SM）、蚁群优化模型（ACOM）和评估模型（AM）。首先，在BN的初始建立阶段，状态空间被最大权重支持树算法（MWST）缩小。接下来，I-ACO 被设计来改进 BN 的结构，以便准确地研究故障传播路径。最后，实验从两个不同的角度进行，以考察训练有素的 BN 结构的合理性。以A320型空气涡轮启动器典型故障传播路径模型的实际应用为例，验证了所提方法的有效性和可行性。