Advanced process monitoring using an on-line non-linear multiscale principal component analysis methodology

被引：25

作者：

Fourie, SH ^{[1
]}

de Vaal, P ^{[1
]}

机构：

[1] Univ Pretoria, Dept Chem Engn, ZA-0002 Pretoria, South Africa

来源：

COMPUTERS & CHEMICAL ENGINEERING | 2000年 / 24卷 / 2-7期

关键词：

process monitoring; fault detection; non-linear principal component analysis;

D O I：

10.1016/S0098-1354(00)00417-8

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A nonlinear multiscale principal component analysis (NLMSPCA) methodology is proposed for process monitoring and fault detection based on multilevel wavelet decomposition and nonlinear principal component analysis via an input-training neural network. Performance monitoring charts with non-parametric control limits are applied to identify the occurrence of non-conforming operation prior to interrogating differential contribution plots to help identify the potential source of the fault. A novel summary display is used to present the information contained in bivariate graphs in order to facilitate global visualization. Positive results were achieved through assessing the capabilities of the monitoring scheme on a nonlinear industrial process. (C) 2000 Elsevier Science Ltd. All rights reserved.

引用

页码：755 / 760

页数：6

共 22 条

[1] Multiscale PCA with application to multivariate statistical process monitoring [J].

Bakshi, BR .

AICHE JOURNAL, 1998, 44 (07) :1596-1610

[2] Application of wavelets and neural networks to diagnostic system development, 1, feature extraction [J].

Chen, BH ;

Wang, XZ ;

Yang, SH ;

McGreavy, C .

COMPUTERS & CHEMICAL ENGINEERING, 1999, 23 (07) :899-906

[3] ORTHONORMAL BASES OF COMPACTLY SUPPORTED WAVELETS [J].

DAUBECHIES, I .

COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS, 1988, 41 (07) :909-996

[4] Nonlinear principal component analysis - Based on principal curves and neural networks [J].

Dong, D ;

McAvoy, TJ .

COMPUTERS & CHEMICAL ENGINEERING, 1996, 20 (01) :65-78

[5] IDEAL SPATIAL ADAPTATION BY WAVELET SHRINKAGE [J].

DONOHO, DL ;

JOHNSTONE, IM .

BIOMETRIKA, 1994, 81 (03) :425-455

[6] Adapting to unknown smoothness via wavelet shrinkage [J].

Donoho, DL ;

Johnstone, IM .

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1995, 90 (432) :1200-1224

[7]

Donoho DL, 1996, ANN STAT, V24, P508

[8] Identification of faulty sensors using principal component analysis [J].

Dunia, R ;

Qin, SJ ;

Edgar, TF ;

McAvoy, TJ .

AICHE JOURNAL, 1996, 42 (10) :2797-2812

[9]

DUNIA R, 1813, AICHE J, V44, P1998

[10]

Hall P, 1996, ANN STAT, V24, P2462

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