Decentralized fault diagnosis of large-scale processes using multiblock kernel principal component analysis

被引：31

作者：

Zhang Y.-W. ^{[1
]}

Zhou H. ^{[1
]}

Qin S.J. ^{[2
]}

机构：

[1] Key Laboratory of Integrated Automation of Process Industry, Ministry of Education

[2] Mork Family Department of Chemical Engineering and Materials Science, University of Southern California

来源：

Zidonghua Xuebao/ Acta Automatica Sinica | 2010年 / 36卷 / 04期

关键词：

Fault detection; Multiblock kernel methods; Nonlinear component analysis; Principal component analysis (PCA); Process monitoring;

D O I：

10.3724/SP.J.1004.2010.00593

中图分类号：

学科分类号：

摘要：

In this paper, a multiblock kernel principal component analysis (MBKPCA) algorithm is proposed. Based on MBKPCA, a new fault detection and diagnosis approach is proposed to monitor large-scale processes. In particular, definitions of nonlinear block contributions to T2 and the squared prediction error (SPE) statistics are first proposed in order to diagnose nonlinear faults. In addition, the relative contribution, which is the ratio of the contribution to the corresponding upper control limit, is considered to find process variables or blocks responsible for faults. The proposed method is applied to fault detection and diagnosis in the Tennessee Eastman process. The proposed decentralized nonlinear approach effectively captures the nonlinear relationship in the block process variables and shows superior fault diagnosis ability compared with other methods. Copyright © 2010 Acta Automatica Sinica. All rights reserved.

引用

页码：593 / 597

页数：4

共 24 条

[21] Zhang Y.W., Qin S.J., Fault detection of nonlinear processes using multiway kernel independent component analysis, Industrial and Engineering Chemistry Research, 46, 23, pp. 7780-7787, (2007)
[22] Zhang Y.W., Qin S.J., Improved nonlinera fault detection technique and statistical analysis, American Institute of Chemical Engineers Journal, 54, 12, pp. 3207-3220, (2008)
[23] Lee J.M., Yoo C.K., Lee I.B., Fault detection of batch processes using multiway kernel principal component analysis, Computers and Chemical Engineering, 28, 9, pp. 1837-1847, (2004)
[24] Lee J.M., Yoo C.K., Choi S.W., Vanrolleghem P.A., Lee I.B., Nonlinear process monitoring using kernel principal component analysis, Chemical Engineering Science, 59, 1, pp. 223-234, (2004)

← 1 2 3 →