[2N4-GS-10] AI application

[2N4-GS-10-04] Process Fault Detection Method based on Variational Graph Auto-encoder and Correlation among Process Variables and its application to Vinyl Acetate Monomer Process

〇Yoshiaki Uchida¹, Koichi Fujiwara¹ (1. Nagoya University)

Keywords:Fault Detection, Production Process, Variational Graph Auto-encoder, Nearest Correlation Method

In industries, process monitoring is critical for realizing efficient and safe production. The scale of industrial processes has become larger and larger and more and more complicated, and various process monitoring systems, such as multivariate statistical process control (MSPC), have been widely adopted. MSPC monitors process conditions based on the correlation among the process variables by extracting features from the highly correlated and high-dimensional process data. Principal component analysis (PCA) is a popular linear dimensionality reduction method and utilized in MSPC although it is difficult for PCA to deal with the nonlinearity and the process dynamics. In this study, a new process monitoring method based on the nearest correlation (NC) method and variational graph auto-encoder (VGAE) is proposed to cope with the process nonlinearity and dynamics. This study reported the results of its application to the vinyl acetate monomer production process and compared it with conventional process monitoring methods.