Industrial Process Monitoring Based on DiPCA and ARMA-Transformer

This study addresses the problem of incipient fault detection in industrial processes by developing an enhanced dynamic inner principal component analysis (DiPCA) framework. This framework integrates a Transformer attention mechanism and a masked linear autoregressive moving average (ARMA) structure. In contrast to the original DiPCA, this approach can effectively characterize complex dynamic processes while maintaining the theoretical integrity of the original framework. Here, the sliding-window singular value decomposition is chosen to construct the dynamic feature matrix. The ARMA-Transformer network can capture the multi-step prediction features. A DiPCA-based dual monitoring system can then be used to establish dynamic and static residual statistics. The simulations of the Tennessee Eastman Process (TEP) demonstrate that the proposed method yields fault detection rates (FDRs) of 77.80% and 69.50% for incipient faults 3 and 9. These are higher than those of traditional DiPCA, ARMA, PCA, and DPCA by more than 40% on average. Validation on the Case Western Reserve University (CWRU) bearing dataset further shows an average FDR of 94.74% across 13 fault conditions. It thus offers an effective approach for early anomaly identification and safety monitoring in complex industrial processes.