Efficient Sparse Matrix Estimation and Dissimilarity Detection Method for Incipient Faults in Dynamic Industrial Processes

Dynamic characteristics are inherent in industrial processes. With increasing system complexity and the growing number of sensors, data-driven modeling faces challenges in terms of computational burden, and incipient faults become harder to detect promptly. To address these issues, this study proposes a vector autoregressive-based dynamic process modeling method that decomposes the monitoring space into dynamic and static components. An efficient sparse dynamic matrix estimation algorithm is further developed for offline model optimization, and a dissimilarity analysis-based approach is introduced for incipient-fault detection in the static component. Experiments on the Tennessee Eastman process benchmark model validate the effectiveness of the proposed method.