Journal Article

Quality-Related Process Monitoring Based on a Bayesian Classifier

Zhou, Hongping, Kong, Xiangyu, An, Qiusheng, Luo, Jiayu, Li, Hongzeng

2023

Overview

Multivariate statistical analysis approaches are extensively employed in process monitoring because they can effectively detect abnormal conditions in industrial processes. However, both Gaussian and non-Gaussian variables are often present in industrial processes. A single multivariate statistical process monitoring method often has difficulty simultaneously dealing with variable information of mixed distribution characteristics. This paper proposes a multivariate quality-related process monitoring method based on a Bayesian classifier to address this issue. The proposed method separates the variables into Gaussian and non-Gaussian parts using a Jarque–Bera test. Then, Gaussian and non-Gaussian properties are extracted through modified kernel partial least squares and kernel independent component analysis. After feature extraction, a Bayesian-based classifier relevance vector machine is constructed to monitor quality-related information of the process, which avoids the construction of a threshold in conventional methods and offsets the drawbacks of insufficient single statistic information. A numerical simulation and the Tennessee-Eastman process verify the effectiveness of the method.

• Publisher: Korean Society for Precision Engineering,Springer Nature B.V
• Subject: Algorithms / Bayesian analysis / Data processing / Decomposition / Eigenvalues / Engineering / False alarms / Independent component analysis / Industrial and Production Engineering / Industrial production / Machine learning / Materials Science / Methods / Monitoring / Multivariate analysis / Multivariate statistical analysis / Normal distribution / Regular Paper / Statistical analysis / Statistics / Variables