Data-Driven Optimized Distributed Dynamic PCA for Efficient Monitoring of Large-Scale Dynamic Processes

Abstract

Dynamic principal component analysis (DPCA) is generally employed in monitoring dynamic processes and typically incorporates all measured variables. However, for a large-scale process, the inclusion of variables without fault-relevant information may cause redundancy and degrade monitoring performance. In this paper, the influence of variable and time-lagged variable selection on the DPCA monitoring performance is analyzed. Then, a fault-relevant performance-driven distributed monitoring scheme is proposed to achieve efficient fault detection and diagnosis. First, performance-driven process decomposition is performed, and the optimal subset of variables and time-lagged variables for each fault are selected through a stochastic optimization algorithm. Second, local DPCA models are established to characterize the process dynamics and generate fault signature evidence. Finally, a Bayesian diagnosis system with the most efficient evidence sources is established to identify the process status. Case studies on a numerical example and the Tennessee Eastman benchmark process demonstrate the efficiency of the proposed monitoring scheme.