区域供热网络泄漏故障在线高效监测可以减少检修反映时间，保障居民的用热需求。然而，常规泄漏故障诊断方法的数据特征提取能力有限，难以处理复杂供热网络高维度非线性压力流量监测数据，使得其诊断性能较弱。因此，提出了一种基于卷积神经网络（convolutional neural networks, CNN）和Transformer的供热管网泄漏故障诊断模型。提出的CNN-Transformer诊断模型将CNN与Transformer网络相结合，实现了不同时间尺度和空间特征的联合学习。其中CNN网络用于提取局部特征，Transformer网络用于捕获全局特征。通过模拟环状供热管网系统得到的故障数据集验证了模型的有效性。结果表明，提出的基于故障特征的多级特征提取与融合机制的CNN-Transforme诊断模型，显著提升了泄漏诊断的准确率。CNN-Transformer方法在测试集上准确率最高，与其他故障诊断方法（长短期记忆循环网络、门控循环网络、CNN和Transformer）相比，在测试集上的准确率分别提高了13.21%、7.49%、6.1%和4.62%。

On-line and efficient monitoring of leakage faults in district heating network can effectively increase the quality of heat transmission and reduce energy consumption. However, the data feature extraction ability of conventional leakage fault diagnosis method is limited, and it is difficult to deal with the high dimensional nonlinear pressure flow monitoring data for complex heating network, which makes its diagnostic performance weak. Therefore, a fault diagnosis model of heating network leakage based on convolutional neural network (CNN) and Transformer was proposed. The proposed CNN-Transformer diagnostic model combines CNN and Transformer network to realize joint learning of different time scales and spatial features. The CNN network was used to extract local features, and the Transformer network was used to capture global features. The validity of the model was verified by simulating the fault data set of the annular heating pipe network system. The results show that the proposed CNN-Transformer diagnosis model based on multi-stage feature extraction and fusion mechanism of fault features significantly improves the accuracy of leak diagnosis. The CNN-Transformer method has the highest accuracy on the test set, with an accuracy increase of 13.21%, 7.49%, 6.1% and 4.62%, respectively, compared to other fault diagnosis methods including long short-term memory network, gate recurrent network, CNN and Transformer.