Application of latent variable methods to process control and multivariate statistical process control in industry

TLDR

Latent variable methods have gained traction in multivariate monitoring and control, with industrial adopters reporting success in fault detection, diagnosis, and process transitions across continuous, batch, and image‑analysis applications. This paper reviews recent advances in multivariate statistical process control and their application to fault detection and isolation in industrial settings. The authors critically assess MSPC methodologies and explain how they are implemented in industrial processes. The study shows that preserving multivariate data structure during compression and preprocessing is essential, as univariate approaches can create spurious correlations and compromise analysis validity. © 2005 John Wiley & Sons, Ltd.

Abstract

Abstract Multivariate monitoring and control schemes based on latent variable methods have been receiving increasing attention by industrial practitioners in the last 15 years. Several companies have enthusiastically adopted the methods and have reported many success stories. Applications have been reported where multivariate statistical process control, fault detection and diagnosis is achieved by utilizing the latent variable space, for continuous and batch processes, as well as, for process transitions as for example start ups and re‐starts. This paper gives an overview of the latest developments in multivariate statistical process control (MSPC) and its application for fault detection and isolation (FDI) in industrial processes. It provides a critical review of the methodology and describes how it is transferred to the industrial environment. Recent applications of latent variable methods to process control as well as to image analysis for monitoring and feedback control are discussed. Finally it is emphasized that the multivariate nature of the data should be preserved when data compression and data preprocessing is applied. It is shown that univariate data compression and reconstruction may hinder the validity of multivariate analysis by introducing spurious correlations. Copyright © 2005 John Wiley & Sons, Ltd.