Optimized Distributed Nonstationary Process Monitoring Approach Based on Fault-Related Information and Partial Correlation Fusion

In order to emphasize the fault-related information and to reduce the redundant information simultaneously, an optimized distributed cointegration analysis (CA) monitoring approach is proposed in this article for large-scale nonstationary processes. In the optimized distributed strategy, fault-related nonstationary variables are selected by further analyzing different faulty datasets. In addition, the subblocks with high partial correlations are fused to obtain the optimized blocks with less redundant information. On this basis, CA methods are constructed for fault-related nonstationary blocks to explore long-term equilibrium relationships. Finally, the monitoring results of each block are integrated by the Bayesian inference criterion (BIC). With the fault-related variables selection and partial correlation fusion strategy, the optimized blocks are able to characterize the different faulty modes and to retain beneficial nonstationary information. The performance of the proposed approach is compared to other process monitoring methods on the benchmark simulated vinyl acetate monomer (VAM) process.