Spatial-temporal adaptive causality graph-based fault root cause location method for time-varying industrial process

• A novel spatial–temporal adaptive causality graph generation method is proposed. • Prior underlying knowledge is embedding for graph enhancement. • The proposed method outperforms the existing methods in TEP and ASU industry systems. Fault root cause location is crucial role for industrial system safe operation. However, most industrial processes are time-varying due to operating mode changes, demand transitions, and equipment degradation, causing system causality evolution. These variations can reduce the performance of conventional fault root cause location methods. To address this issue, a spatial–temporal adaptive causality graph generation method (STACGG) is proposed to update the causality graph for fault root cause location. In the STACGG method, an edge aggregation masking operator is designed, in which the common part of the causality can be well inherited, while the customized part is online learned within the allowable maximum causality variation range, thus achieving steady adaptive progressive causality graph update. First, the most similar historical causality graphs are matched by contrasting the temporal and spatial characteristics of the pairwise node feature. Then, an edge aggregation-based graph generator (EAGG) is developed to identify a compact edge structure between the edge intersection and the edge union of these similar causality graphs. Enabling the edge intersection as the learning lower bound means inheriting common part of causality while taking the edge union as the learning upper bound represents steadily learn the customized part within the maximum causality variation range. To achieve it, the EAGG is formulated as an optimization task that maximizes the mutual information entropy between a GNN’s fault detection and the possible edge structure distribution. Then, to enhance the expression power and the interpretability of the causality graph, the posterior data information and the prior physical knowledge are combined as the inductive bias and the learning bias to fine-tune the causality graph for graph performance boosting. Finally, the fault root cause location performance is validated on two real-word industrial cases.