Fault Detection and Identification via Monitoring Modules Based on Clusters of Interacting Measurements

Abstract

This work introduces a novel control-aware distributed process monitoring methodology based on modules comprised of clusters of interacting measurements. The methodology relies on the process flow diagram (PFD) and control system structure without requiring cross-correlation data to create monitoring modules. The methodology is validated on the Tennessee Eastman Process benchmark using full Principal Component Analysis (f-PCA) in the monitoring modules. The results are comparable to nonlinear techniques implemented in a centralized manner such as Kernel PCA (KPCA), Autoencoders (AE), and Recurrent Neural Networks (RNN), or distributed techniques like the Distributed Canonical Correlation Analysis (DCCA). Temporal plots of fault detection by different modules show clearly the magnitude and propagation of the fault through each module, pinpointing the module where the fault originates, and separating controllable faults from other faults. This information, combined with PCA contribution plots, helps detection and identification as effectively as more complex nonlinear centralized or distributed methods.