Nonlinear dynamic principal component analysis for on-line process monitoring and diagnosis

被引：56

作者：

Lin, WL ^{[1
]}

Qian, Y ^{[1
]}

Li, XX ^{[1
]}

机构：

[1] S China Univ Technol, Chem Engn Res Ctr, Guangzhou 510640, Peoples R China

来源：

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

关键词：

principal component analysis; neural networks; process monitoring; fault diagnosis;

D O I：

10.1016/S0098-1354(00)00433-6

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A nonlinear dynamic principal component analysis (ND-PCA) approach is developed in this paper based on dynamic PCA and the sigmoid basis function feed forward neural network (SBFN). Through ND-PCA an integrated framework for on-line monitoring and root-cause diagnosis is developed. The approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study while noises were added on sensor readings. Results show that the proposed ND-PCA approach performs good incipient diagnosis capability and overall diagnosis correctness rate. (C) 2000 Elsevier Science Ltd. All rights reserved.

引用

页码：423 / 429

页数：7

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