Anomaly Tracing Method Based on Attention-Based Postnonlinear Causal Model

Accurate causal discovery is of great significance for data-driven root cause diagnosis. For multivariate complex industrial processes, traditional methods rarely conduct causal discovery of multiple causes and single effect from the perspective of quantifying causal strength. In this regard, this article proposed an anomaly tracing method based on attention-based postnonlinear (PNL) causal model. The attention mechanism is introduced into the multivariate PNL model to quantitatively calculate the causal contribution of each cause to the effect. To address the issue of distinguishing inherent causal relationships from anomaly propagation paths, a comparative causal diagram analysis method is proposed. It analyzes the changes in attention weights of the cause variables and effect variable under normal and abnormal conditions to determine the anomaly propagation paths. To tackle the problem of multiple root nodes in causal diagram, a root cause scoring method is proposed. The feasibility of the proposed method is demonstrated through simulation and real industrial case study. Compared with existing and ablation methods, the proposed method can provide the root cause more promptly and accurately, as well as identify anomaly propagation path that align with mechanism analysis.