Canonical correlation analysis-based explicit relation discovery for statistical process monitoring

DOI10.1016/J.JFRANKLIN.2020.01.049zbMath1437.93085OpenAlexW3006428951MaRDI QIDQ2181391

Chudong Tong, Shengjun Meng, Ting Lan, Haizhen Yu

Publication date: 19 May 2020

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2020.01.049

zbMATH Keywords

statistical process monitoringcanonical correlation analysis algorithmexplicit relation between interacted variablesstatic and dynamic processes

Mathematics Subject Classification ID

Measures of association (correlation, canonical correlation, etc.) (62H20) Applications of statistics in engineering and industry; control charts (62P30) Control/observation systems involving computers (process control, etc.) (93C83)

Related Items (3)

Asymptotic properties of Spearman's footrule and Gini's gamma in bivariate normal model ⋮ Fault detection and isolation of multi-variate time series data using spectral weighted graph auto-encoders ⋮ Multivariate statistical process monitoring based on principal discriminative component analysis

Cites Work

• Assessment of \(T^2\)- and \(Q\)-statistics for detecting additive and multiplicative faults in multivariate statistical process monitoring
• A learning algorithm for adaptive canonical correlation analysis of several data sets
• Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis
• Reconstruction-based contribution for process monitoring
• Fault diagnosis of non-Gaussian process based on FKICA
• KPI relevant and irrelevant fault monitoring with neighborhood component analysis and two-level PLS
• Multi-subspace factor analysis integrated with support vector data description for multimode process monitoring
• Fault detection and diagnosis in industrial systems

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