Causal‐oriented kernel regularized canonical variate analysis for incipient fault detection in large‐scale processes

Abstract

To address the problem of balancing sensitivity and robustness in incipient fault detection (IFD) for large‐scale processes, a distributed monitoring framework based on causal‐oriented kernel regularized canonical variate analysis (CO‐KRCVA) is proposed. The framework divides operational units into sub‐blocks according to the sequence of production. To reveal the information transfer relationships and the underlying mechanisms of dynamic changes among variables, a causal difference matrix (CDM) using transfer entropy estimation is constructed for each sub‐block. Based on this, a CO‐KRCVA model is established, which specifically captures targeted minor variations of process variables to enhance the sensitivity and reliability. Its regularization strategy further overcomes the effects of random noise or interference to reduce false alarms. In addition, the Wasserstein distance (WD)‐based statistic is employed to sensitively reflect distribution shifts from both probabilistic and geometric perspectives. To facilitate global detection, Bayesian inference is used to fuse the results from local models. Finally, the proposed method and monitoring framework are validated for effectiveness on different types of faults in the vinyl acetate monomer (VAM) process and Tennessee Eastman (TE) process. Experimental results show that compared with CVA, the proposed method improves the early fault detection rate by 28.47%, 46.21%, and 12.6%, respectively, for the three fault types in the TE process, while reducing the false alarm rate by 0.52%, 1.44%, and 1.36%.