针对现有数据驱动的航空发动机气路故障诊断技术精度有待提升以及算法可解释性存在不足的问题，提出了一种融合发动机专家知识的深度图神经网络（KD-DeepGNN）算法，并通过搭建的JT9D发动机仿真平台生成运行数据用于模型的训练和验证。采用气路分析法对各部件衰退计算过程涉及的相关征兆量进行分析以划分传感器空间，并综合考虑各部件之间的气路联系和传动关系实现图结构的表征和生成。在此基础上，通过堆叠图卷积模块及设置分层池化层自适应提取数据中的故障特征信息，进一步提升了算法在复杂环境和工况条件下的故障诊断精度和泛化能力。研究表明，所提出的方法在测试集上的故障分类精度均可达97%以上，且对比现有方法精度提升至少2.2%。

To address the issues of insufficient diagnostic accuracy and poor interpretability in existing data-driven gas path fault diagnosis techniques for aircraft engines, a knowledge driven deep graph neural network (KD-DeepGNN) algorithm is proposed, which integrates expert knowledge of engines. The JT9D engine simulation platform was developed to generate operational data for the training and validation of the algorithm. Gas path analysis was employed to analyze the relevant symptoms involved in the degradation calculation of each component, thereby partitioning the sensor space. The graph structure was characterized and generated by comprehensively considering the gas path connections and transmission relationships between rotational components. On this basis, the algorithm uses stacked graph convolutional modules and hierarchical graph pooling to adaptively extract degradation feature information from the data, thereby enhancing the fault diagnosis accuracy and generalization capability of the algorithm under complex environments and operating conditions. The study demonstrates that the proposed model achieves a fault classification accuracy of over 97% on the test sets, with an improvement of at least 2.2% compared to existing methods.