Quality-related fault detection using linear and nonlinear principal component regression

DOI10.1016/J.JFRANKLIN.2016.03.021zbMath1347.93274OpenAlexW2313268156MaRDI QIDQ328104

Hao Luo, Kaixiang Peng, Guang Wang

Publication date: 20 October 2016

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

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2016.03.021

zbMATH Keywords

partial least squares (PLS)principal component regression (PCR)process monitoringquality-related fault detection

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Linear systems in control theory (93C05) Least squares and related methods for stochastic control systems (93E24) Observability (93B07)

Related Items (6)

A key performance indicator‐relevant approach based on kernel entropy component regression model for industrial system ⋮ A distributed principal component regression method for quality-related fault detection and diagnosis ⋮ An enhanced kernel learning data-driven method for multiple fault detection and identification in industrial systems ⋮ An adaptive subspace data-driven method for nonlinear dynamic systems ⋮ Assessment of \(T^2\)- and \(Q\)-statistics for detecting additive and multiplicative faults in multivariate statistical process monitoring ⋮ Parallel supervised additive and multiplicative faults detection for nonlinear process

Cites Work

• Geometric properties of partial least squares for process monitoring
• Quality-relevant fault detection and diagnosis for hot strip mill process with multi-specification and multi-batch measurements
• 10.1162/15324430260185556

This page was built for publication: Quality-related fault detection using linear and nonlinear principal component regression