Graph autoencoder with causal relationship inference for fault detection and root cause identification in complex industrial process

Modern industrial processes are characterized by highly interconnected and interdependent units, where disturbances and faults propagate dynamically across variables, equipment, and subsystems. This complex interaction makes root cause identification particularly challenging in real-world applications, such as semiconductor manufacturing. Although numerous existing methods are capable of performing root cause analysis, most fall short in effectively integrating temporal dynamics with spatial causal dependencies, thereby constraining their overall model performance. In this study, a novel network called graph autoencoder with causal relationship inference (GACRI) is proposed to tackle this challenge. Firstly, a dual-view decoder graph autoencoder (i.e., feature-level and causal-level decoders) is proposed to consider temporal features and spatial causal relationships concurrently. Secondly, a global index based on the learned features and the reconstructed residual space is developed for fault detection. The variable contribution degree is analyzed to isolate the fault variables via the reconstruction loss. Finally, a causal discovery network is designed to predict the causal relationship among fault variables for root-cause identification. The testing results on four processes (i.e., a numerical process, the Tennessee Eastman process, a semiconductor process and a hydraulic system) demonstrate the superior performance of GACRI in process monitoring.