Monitoring of Processes with Multiple Operating Modes through Multiple Principle Component Analysis Models

TLDR

Current multivariate statistical process monitoring methods assume a single nominal operating region, causing continuous false alarms when applied to processes with multiple normal operating modes. The study proposes a multiple‑PCA model–based monitoring method that uses principal angles to compare model similarities. The method constructs multiple PCA models for each operating mode, compares them with principal angles, and monitors deviations using squared prediction error and control limits, as demonstrated on the Tennessee‑Eastman and a fluidized catalytic cracking unit. The approach significantly reduces false alarms while accurately tracking process adjustments, as shown on the Tennessee‑Eastman and fluidized catalytic cracking processes.

Abstract

Because many of the current multivariate statistical process monitoring (MSPM) techniques are based on the assumption that the process has one nominal operating region, the application of these MSPM approaches to an industrial process with multiple operating modes would always trigger continuous warnings even when the process itself is operating under another normal steady-state operating conditions. Adopting principal angles to measure the similarities of any two models, this paper proposes a multiple principal component analysis model based process monitoring methodology. Some popular multivariate statistical measurements such as squared prediction error and its control limit can be incorporated straightforwardly to facilitate process monitoring. The efficiency of the proposed technique is demonstrated through application to the monitoring of the Tennessee−Eastman challenge process and an industrial fluidized catalytic cracking unit. The proposed scheme can significantly reduce the amount of false alarms while tracking the process adjustment.