Online reconstruction and diagnosibility analysis of multiplicative fault models for process-related faults

DOI10.1016/J.JFRANKLIN.2022.02.035zbMath1489.93080OpenAlexW4220848166MaRDI QIDQ2137109

Yanyan Li

Publication date: 16 May 2022

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

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2022.02.035

zbMATH Keywords

diagnosabilitymultiplicative fault modelmultivariate statistical process monitoring

Mathematics Subject Classification ID

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

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
• Reconstruction-based contribution for process monitoring
• Fault diagnosis for processes with feedback control loops by shifted output sampling approach
• Parallel supervised additive and multiplicative faults detection for nonlinear process

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