Quality-Related Process Monitoring Based on a Bayesian Classifier

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.