A review of statistical process monitoring methods for non‐linear and non‐Gaussian industrial processes
Yang Zhou, Kai Wang, Yilan Zhang, Dan Liang, Jia Li
Abstract
Abstract In modern industrial processes, the growing emphasis on product quality and efficiency has led to increased attention on safety and quality issues within industrial processes. Over the past two decades, there has been extensive research into multivariate statistical process monitoring methods. However, basic statistical process monitoring methods still face significant challenges when applied in diverse real‐world operating conditions. This paper offers a comprehensive review of statistical process monitoring methods for industrial processes. First, this paper begins by outlining the methodologies and modelling procedures commonly used in statistical process monitoring for industrial processes. Then, examine the current research landscape across various aspects of these methods. Finally, this paper delves into the extensions, opportunities, and challenges within statistical process monitoring for industrial processes, offering insights for future research directions.