Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis
Top Cited Papers
- 8 May 2000
- journal article
- Published by Elsevier BV in Chemometrics and Intelligent Laboratory Systems
- Vol. 51 (1), 81-93
- https://doi.org/10.1016/s0169-7439(00)00058-7
Abstract
No abstract availableKeywords
This publication has 18 references indexed in Scilit:
- Isolation enhanced principal component analysisAIChE Journal, 1999
- Statistical monitoring of multivariable dynamic processes with state‐space modelsAIChE Journal, 1997
- Statistical process monitoring and disturbance diagnosis in multivariable continuous processesAIChE Journal, 1996
- Multivariate statistical methods for monitoring continuous processes: assessment of discrimination power of disturbance models and diagnosis of multiple disturbancesChemometrics and Intelligent Laboratory Systems, 1995
- Disturbance detection and isolation by dynamic principal component analysisChemometrics and Intelligent Laboratory Systems, 1995
- Detection of gross erros in data reconciliation by principal component analysisAIChE Journal, 1995
- Chemometric methods for process monitoring and high‐performance controller designAIChE Journal, 1992
- Multivariate statistical monitoring of process operating performanceThe Canadian Journal of Chemical Engineering, 1991
- Control Procedures for Residuals Associated with Principal Component AnalysisTechnometrics, 1979
- Stochastic theory of minimal realizationIEEE Transactions on Automatic Control, 1974