Hard constraints and soft learning dual-graph anomaly detection for industrial processes

Anomaly detection is critical for safe and stable operation in industrial processes. Industrial data exhibits strong spatiotemporal dependence, while variable interactions often evolve dynamically. Traditional methods struggle to model both fixed physical constraints and dynamic data relationships. This paper proposes a hard constraints and soft learning dual-graph anomaly detection (HCSL-DGAD). First, a macro-graph with hard constraints is constructed based on the connections between various components of the industrial process, and micro-graphs with soft learning is constructed through an adaptive method based on attention mechanisms. The macro-graph transmits physical constraints through a spatiotemporal graph convolutional network to ensure the rationality of the abnormal propagation path. The micro-graph uses a multi-head attention mechanism to dynamically learn the implicit relationship between nodes and capture coupling information not covered by the physical topology. Secondly, to address the multi-scale anomalies in the spatiotemporal domain, a dual-channel architecture is employed to extract features from both the macro and micro-graphs. Edge weights and node states are alternately updated in the micro-graph channel to accurately identify anomaly patterns at different scales. At the same time, temporal attention and variable attention are combined in the macro-graph channel to jointly improve the detection accuracy. Extensive experiments on three benchmarks show that HCSL-DGAD achieves average F1-scores of 98.24%, 89.92%, and 87.00% on the Tennessee Eastman (TE) process, Secure Water Treatment (SWaT) and PROcess NeTwork Optimization (PRONTO) datasets, respectively.