Continuous Causal Learning of Multimode Industrial Processes for Comprehensive Root Cause Analysis

Accurately inferring the causality among variables is essential for diagnosing the root causes of industrial process faults. The multimode characteristics of industrial processes augment the complexity of fault evolution processes, which presenting challenges to traditional methods. To this end, the continuous causal learning framework is proposed for comprehensive root cause diagnosis of multimode processes. Firstly, the temporal feature interference aided causal discovery network is designed to achieve collaborative quantification of multivariate causality, which can facilitate continuous learning while avoiding pairwise causal modeling. Then, to address the “catastrophic forgetting" issue in continuous learning, an elastic weight consolidation strategy is introduced to balance the newly emerged modes and previously learned modes through adaptive parameter regularization during sequential model updating. Subsequently, the root cause diagnosis index impact score is designed to locate the real root cause according to the causality variations from normal to faults with Cramer-von Mises test. Additionally, the propagation paths are identified through the SL metric by involving causality strength and time lag simultaneously. Finally, the proposed method significantly outperform the existing methods in terms of root cause diagnosis on both simulated and real-world multimode processes.