KPI relevant and irrelevant fault monitoring with neighborhood component analysis and two-level PLS

DOI10.1016/J.JFRANKLIN.2018.07.016zbMath1398.93364OpenAlexW2890457970MaRDI QIDQ1796752

Ting Lan, Xiaoxia Chen, Xuhua Shi, Chudong Tong, Yu-Wei Chen

Publication date: 17 October 2018

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

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2018.07.016

zbMATH Keywords

fault monitoringkey performance indicatortwo-level partial least square modeling

Mathematics Subject Classification ID

Reliability, availability, maintenance, inspection in operations research (90B25) Least squares and related methods for stochastic control systems (93E24)

Related Items (1)

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

Cites Work

• Assessment of \(T^2\)- and \(Q\)-statistics for detecting additive and multiplicative faults in multivariate statistical process monitoring
• A novel dynamic non-Gaussian approach for quality-related fault diagnosis with application to the hot strip mill process
• Direct projection to latent variable space for fault detection
• Fault detection and diagnosis in industrial systems

This page was built for publication: KPI relevant and irrelevant fault monitoring with neighborhood component analysis and two-level PLS