期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 127, 期 -, 页码 195-209出版社
ELSEVIER
DOI: 10.1016/j.chemolab.2013.07.001
关键词
Kernel principal component analysis; Local structure analysis; Fault diagnosis; Fault detection; Fault identification; Nonlinear process
类别
资金
- Natural Science Foundation of Shandong Province, China [ZR2011FM014]
- Fundamental Research Funds for the Central Universities [10CX04046A]
- National Natural Science Foundation of China [61273160]
Traditional kernel principal component analysis (KPCA) concentrates on the global structure analysis of data sets but omits the local information which is also important for process monitoring and fault diagnosis. In this paper, a modified KPCA, referred to as the local KPCA (LKPCA), is proposed based on local structure analysis for nonlinear process fault diagnosis. In order to extract data feature better, local structure analysis is integrated within the KPCA, and this results in a new optimisation objective which naturally involves both global and local structure information. With the application of usual kernel trick, the optimisation problem is transformed into a generalised eigenvalue decomposition on the kernel matrix. For the purpose of fault detection, two monitoring statistics, known as the T-2 and Q statistics, are built based on the LKPCA model and confidence limit is computed by kernel density estimation. In order to identify fault variables, contribution plots for monitoring statistics are constructed based on the idea of sensitivity analysis to locate the fault variables. Simulation using the Tennessee Eastman benchmark process shows that the proposed method outperforms the traditional KPCA, in terms of fault detection performance. The results obtained also demonstrate the potential of the proposed fault identification approach. (C) 2013 Elsevier B.V. All rights reserved.
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