Process topology convolutional network model for chemical process fault diagnosis

Abstract

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.