Multivariate statistical process monitoring based on principal discriminative component analysis

DOI10.1016/J.JFRANKLIN.2021.07.041zbMath1476.62128OpenAlexW3190759061MaRDI QIDQ2235402

Yanyan Li

Publication date: 21 October 2021

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

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2021.07.041

zbMATH Keywords

principal discriminative component analysis

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics in engineering and industry; control charts (62P30) Stationary stochastic processes (60G10)

Related Items (1)

Fault detection and isolation of multi-variate time series data using spectral weighted graph auto-encoders

Cites Work

• Assessment of \(T^2\)- and \(Q\)-statistics for detecting additive and multiplicative faults in multivariate statistical process monitoring
• Feature selection for multivariate contribution analysis in fault detection and isolation
• Fault diagnosis of non-Gaussian process based on FKICA
• Multi-subspace factor analysis integrated with support vector data description for multimode process monitoring
• Canonical correlation analysis-based explicit relation discovery for statistical process monitoring
• Global-and-local-structure-based neural network for fault detection
• An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks
• Locating sensors in large-scale engineering systems for fault isolation based on fault feature reduction
• Parallel supervised additive and multiplicative faults detection for nonlinear process

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