Monitoring of time-varying processes using kernel independent component analysis
- 22 November 2012
- journal article
- Published by Elsevier BV in Chemical Engineering Science
- Vol. 88, 23-32
- https://doi.org/10.1016/j.ces.2012.11.008
Abstract
No abstract availableKeywords
Funding Information
- China’s National 973 program (2009CB320602, 2009CB320604)
- NSF (60974057, 61020106003)
This publication has 34 references indexed in Scilit:
- Eventual strong consensus with fault detection in the presence of dual failure mode on processors under dynamic networksJournal of Network and Computer Applications, 2012
- Fault detection in non-Gaussian vibration systems using dynamic statistical-based approachesMechanical Systems and Signal Processing, 2010
- Kernel Generalization of PPCA for Nonlinear Probabilistic MonitoringIndustrial & Engineering Chemistry Research, 2010
- Fuzzy PCA-Guided Robust $k$-Means ClusteringIEEE Transactions on Fuzzy Systems, 2009
- Multiblock PLS-based localized process diagnosisJournal of Process Control, 2005
- Statistical Process Control Charts for Batch Operations Based on Independent Component AnalysisIndustrial & Engineering Chemistry Research, 2004
- Real-time monitoring for a process with multiple operating modesControl Engineering Practice, 1999
- Mixture Principal Component Analysis Models for Process MonitoringIndustrial & Engineering Chemistry Research, 1999
- Nonlinear principal component analysis—Based on principal curves and neural networksComputers & Chemical Engineering, 1996
- Two generalizations of the common principal component modelBiometrika, 1987