A propagation path-based interpretable neural network model for fault detection and diagnosis in chemical process systems
Benjamin Nguyen, Moncef Chioua
Abstract
Process monitoring through automated fault detection and diagnosis (FDD) plays a crucial role in maintaining a productive and reliable chemical process system. Developments in AI and machine learning have boosted FDD model performances especially with deep learning methods. However, these neural network models are considered black-boxes where the reasoning behind a diagnosis is unclear, preventing industrial adoption. Therefore, in this study, an interpretable neural network model is proposed for FDD in chemical processes. This framework detects and diagnoses faults based on the propagation paths of different faults which are embedded into the architecture through graph convolutional networks. A mechanism for interpreting the node activations which represent process variables is developed for decision verification. The proposed method is evaluated on the benchmark Tennessee Eastman Process where it achieves a 93.56% accuracy on selected faults. • Interpretable neural network for FDD using process data and fault propagation path. • Mechanism for interpreting node activations for model decision validation. • Sensitivity analysis and tuning guidelines for hyperparameters are provided.