A novel white component analysis for dynamic process monitoring

• Published in: Journal of process control Vol. 127; p. 102998
• Main Authors: Huo, Kaida, Huang, Dexian, Shang, Chao
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023
• Subjects: Alternating direction method of multipliers; Dynamic principal component analysis; Fault diagnosis; Latent variable models; Process monitoring; Process monitoring; Fault diagnosis; Alternating direction method of multipliers; Latent variable models; Dynamic principal component analysis
• ISSN: 0959-1524; 1873-2771
• DOI: 10.1016/j.jprocont.2023.102998

Dynamic principal component analysis has long been a popular multivariate statistical process monitoring method. However, the resulting residuals are typically subject to serial correlations, thereby compromising the detection capability in face of dynamic data. In this paper, a novel white component analysis (WCA) model is proposed to enforce latent variables to have noise-like properties, thereby well catering to the independent assumption that is critical in monitoring statistic design. A new whiteness index for finite-length time series is put forward, which acts as the objective of each component. To solve the optimization problem, a tailored algorithm based on alternating direction method of multipliers is developed. Using “white” components, a new W-statistic is coined to effectively detect violation of dynamic relations by inspecting the presence of unmodeled dynamics. By incorporating classical variance-based statistics, we arrive at a new dynamic process monitoring scheme that offers deep insights into abnormal situations. Comprehensive case studies corroborate the validity of the WCA-based process monitoring approach, and in particular, the sensitivity of W-statistic to dynamics anomalies. •Propose white component analysis for dynamic process monitoring.•Propose a novel whiteness index for time series data.•Enforce latent variables to be as white as possible.•Develop a novel whiteness-based statistic to detect dynamics anomalies.