Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference

TLDR

Multivariate statistical process monitoring relies on dimension reduction and latent feature extraction, yet including all measured variables can degrade performance by adding irrelevant information. This study investigates how selecting fault‑relevant variables influences PCA‑based monitoring performance. The authors propose a distributed method that optimizes variable subsets for each fault, builds a sub‑PCA model per subset, and fuses the monitoring results through Bayesian inference. The approach reduces redundancy and complexity, captures local fault behaviors, and significantly improves monitoring accuracy, as demonstrated in numerical, Tennessee Eastman, and industrial‑scale case studies.

Abstract

Multivariate statistical process monitoring involves dimension reduction and latent feature extraction in large-scale processes and typically incorporates all measured variables. However, involving variables without beneficial information may degrade monitoring performance. This study analyzes the effect of variable selection on principal component analysis (PCA) monitoring performance. Then, it proposes a fault-relevant variable selection and Bayesian inference-based distributed method for efficient fault detection and isolation. First, the optimal subset of variables is identified for each fault using an optimization algorithm. Second, a sub-PCA model is established in each subset. Finally, the monitoring results of all of the subsets are combined through Bayesian inference. The proposed method reduces redundancy and complexity, explores numerous local behaviors, and provides accurate description of faults, thus improving monitoring performance significantly. Case studies on a numerical example, the Tennessee Eastman benchmark process, and an industrial-scale plant demonstrate the efficiency.