Fault diagnosis of batch processes using discriminant model

DOI10.1080/00207540310001602928zbMath1059.93126OpenAlexW2082034594MaRDI QIDQ4826250

Hyun Woo Cho, Kwang-Jae Kim

Publication date: 11 November 2004

Published in: International Journal of Production Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207540310001602928

zbMATH Keywords

discriminant analysisprincipal component analysisfault diagnosisstatistical classificationpolymerization processbatch processes

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Application models in control theory (93C95) Estimation and detection in stochastic control theory (93E10) Signal detection and filtering (aspects of stochastic processes) (60G35) Reliability and life testing (62N05) Chemistry (general) in thermodynamics and heat transfer (80A50)

Related Items (2)

Enhanced diagnostics using orthogonal de-noising based nonlinear discriminant analysis and its application to multivariate data ⋮ Diagnosing batch processes with insufficient fault data: generation of pseudo batches

Cites Work

• Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy - a survey and some new results
• Analytical redundancy and the design of robust failure detection systems
• Model-based diagnosis of special causes in statistical process control
• Statistical monitoring and diagnosis of automatic controlled processes using dynamic PCA
• An integrated intelligent approach to process diagnosis in process industries
• Multivariate SPC Charts for Monitoring Batch Processes
• A direct LDA algorithm for high-dimensional data -- with application to face recognition

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