Self-Explanatory Fault Diagnosis Framework for Industrial Processes Using Graph Attention
Chae Sun Kim, Han Kim, Jong Min Lee
Abstract
Explanations of deep learning fault diagnosis models have been crucial for risk management and subsequent maintenance actions. Furthermore, purely data-driven approaches for fault diagnosis in industrial processes, without integrating process knowledge or guidance, are limited in generalization ability. This article proposes a graph-based self-explanatory fault diagnosis model. The model employs a graph attention mechanism on a constructed graph data representation of the industrial process, facilitating to capture the causal relationships between process variables. Once the model is fully trained, variations in attention coefficients from normal operating condition are used to identify the root cause of the faulty scenario. This self-explanatory methodology elucidates the model's actual reasoning, obviating the need for additional separate explainable AI methods. Validations through benchmark processes demonstrate significant improvement in fault classification accuracy. Furthermore, variations in attention coefficients effectively identified precise origins of various fault types, including faults that had not been encountered during model training phase.