Lag-Wise Temporal Squeeze-and-Excitation Network for Root Cause Analysis in Industrial Process Faults

Root cause analysis is critical for identifying fault propagation paths and root cause variables in industrial processes. However, existing methods often overlook deep causal information, require per-variable modeling of temporal dependencies, and offer limited interpretability. To address these challenges, we propose a novel lag-wise temporal squeeze-and-excitation network. This method decomposes the causal inference task along the time lag dimension, employing subnetworks to extract deep-layer causal features. A unified temporal squeeze-and-excitation module then models temporal dependencies across all lags. The proposed scheme integrates lagged inputs, a temporal causal matrix, and interpretable predictions to enable exploration of causality variations and preservation of local causal patterns. Finally, a reachability matrix derived from the causal adjacency matrix quantifies root cause scores. Experimental validation on a real-world industrial mineral process demonstrates the effectiveness and superiority of the proposed method.