There always exists potential safety risk in chemical processes. Abnormalities or faults of the processes can lead to severe accidents with unexpected loss of life and property. Early and accurate fault detection and diagnosis (FDD) is essential to prevent these accidents. Many data-driven FDD models have been developed to identify process faults. However, most of the models are black-box models with poor explainability. In this paper, a process topology convolutional network (PTCN) model is proposed for fault diagnosis of complex chemical processes. Experiments on the benchmark Tennessee Eastman process showed that PTCN improved the fault diagnosis accuracy with simpler network structure and less reliance on the amount of training data and computation resources. In the meantime, the model building process becomes much more rational and the model itself is much more understandable.

化学过程中始终存在潜在的安全风险。过程异常或故障可能导致严重事故，并有意外的生命和财产损失。早期准确的故障检测与诊断（FDD）对于预防这些事故至关重要。已经开发了许多数据驱动的FDD模型来识别过程故障。但是，大多数模型是黑箱模型，其可解释性很差。本文提出了一种用于复杂化学过程故障诊断的过程拓扑卷积网络模型。在基准田纳西·伊士曼过程上进行的实验表明，PTCN通过简化网络结构并减少对训练数据和计算资源的依赖，提高了故障诊断的准确性。同时，