A Process Monitoring Method Based on Global-Local Structure Analysis in Principal Component Reconstruction Space

A combination method of kernel principal component analysis (KPCA) and independent component analysis (ICA) for process monitoring is proposed. The new method is a two-phase algorithm: whitened KPCA plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The application to the Tennessee Eastman (TE) simulated process indicates that the proposed process monitoring method can effectively capture the nonlinear relationship in process variables. Its performance significantly outperforms monitoring method based on ICA or KPCA.

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Batch Process Monitoring with Tensor Global–Local Structure Analysis

Industrial & Engineering Chemistry Research ◽

10.1021/ie402355f ◽

2013 ◽

Vol 52 (50) ◽

pp. 18031-18042 ◽

Cited By ~ 23

Author(s):

Lijia Luo ◽

Shiyi Bao ◽

Zengliang Gao ◽

Jingqi Yuan

Keyword(s):

Structure Analysis ◽

Process Monitoring ◽

Local Structure ◽

Batch Process ◽

Batch Process Monitoring

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Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2013.07.001 ◽

2013 ◽

Vol 127 ◽

pp. 195-209 ◽

Cited By ~ 74

Author(s):

Xiaogang Deng ◽

Xuemin Tian ◽

Sheng Chen

Keyword(s):

Principal Component Analysis ◽

Fault Diagnosis ◽

Structure Analysis ◽

Local Structure ◽

Principal Component ◽

Nonlinear Process ◽

Component Analysis ◽

Kernel Principal Component Analysis ◽

Process Fault Diagnosis

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Process monitoring method based on improved dynamic multi-scale principal component analysis

2018 Chinese Control And Decision Conference (CCDC) ◽

10.1109/ccdc.2018.8407553 ◽

2018 ◽

Author(s):

Huaitao Shi ◽

Lei Guo ◽

Lixiu Zhang ◽

Gang Han ◽

Jianchang Liu

Keyword(s):

Principal Component Analysis ◽

Process Monitoring ◽

Principal Component ◽

Component Analysis ◽

Monitoring Method ◽

Multi Scale

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Novel Nonlinear Process Monitoring Based on KPCA-ICA

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.588-589.1054 ◽

2012 ◽

Vol 588-589 ◽

pp. 1054-1057

Author(s):

Ying Wang Xiao ◽

Chen Zhong Zhang

Keyword(s):

Process Monitoring ◽

Principal Component ◽

Nonlinear Process ◽

Component Analysis ◽

Kernel Principal Component Analysis ◽

Process Variables ◽

Two Phase ◽

Monitoring Method ◽

Nonlinear Process Monitoring ◽

Non Gaussian

A novel nonlinear process monitoring method based on kernel principal component analysis (KPCA) - independent component analysis (ICA) is proposed. The new method is a two-phase algorithm: whitened KPCA plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The application to the Tennessee Eastman (TE) simulated process indicates that the proposed process monitoring method can effectively capture the nonlinear relationship in process variables. Its performance significantly outperforms monitoring method based on ICA.

Download Full-text