A distributed principal component regression method for quality-related fault detection and diagnosis

DOI10.1016/j.ins.2022.03.069OpenAlexW4221120431MaRDI QIDQ6118946

Yizhen Yin, Haobo Kang, Unnamed Author, Hong-Jun Ma

Publication date: 28 February 2024

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2022.03.069

zbMATH Keywords

fault detectionfault diagnosisdistributed kernel principal component regression (DKPCR)quality-related process monitoring

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics in engineering and industry; control charts (62P30) Control/observation systems involving computers (process control, etc.) (93C83)

Cites Work

• Quality-related fault detection using linear and nonlinear principal component regression
• Reconstruction-based contribution for process monitoring
• Fault detection of uncertain chemical processes using interval partial least squares-based generalized likelihood ratio test
• Noise-robust image fusion with low-rank sparse decomposition guided by external patch prior
• $\ell _{2,p}$ -Norm Based PCA for Image Recognition

This page was built for publication: A distributed principal component regression method for quality-related fault detection and diagnosis