Kernel PCA-based GLRT for nonlinear fault detection of chemical processes

被引:74
作者
Mansouri, Majdi [2 ]
Nounou, Mohamed [1 ]
Nounou, Hazem [2 ]
Karim, Nazmul [3 ]
机构
[1] Texas A&M Univ QATAR, Chem Engn Program, Doha, Qatar
[2] Texas A&M Univ Qatar, Elect & Comp Engn Program, Doha, Qatar
[3] Texas A&M Univ, Dept Chem Engn, College Stn, TX 77843 USA
关键词
CSTR process; Nonlinear fault detection; Generalized likelihood ratio test; Kernel principal component analysis;
D O I
10.1016/j.jlp.2016.01.011
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Fault detection is often utilized for proper operation of chemical processes. In this paper, a nonlinear statistical fault detection using kernel principal component analysis (KPCA)-based generalized likelihood ratio test (GLRT) is proposed. The objective of this work is to extend our previous work (Harrou et al. (2013) to achieve further improvements and widen the applicability of the developed method in practice by using the KPCA method. The KPCA presented here is derived from the nonlinear case of principal component analysis (PCA) algorithm and it is investigated here as modeling algorithm in the task of fault detection. The fault detection problem is addressed so that the data are first modeled using the KPCA algorithm and then the faults are detected using GLRT. The detection stage is related to the evaluation of detection indices, which are signals that reveal the fault presence. Those indices are obtained from the analysis of the difference between the process measurements and their estimations using the KPCA technique. The fault detection performance is illustrated through two simulated examples, one using synthetic data and the other using simulated continuously stirred tank reactor (CSTR) data. The results demonstrate the effectiveness of the KPCA-based GLRT method over the conventional KPCA method through its two charts T-2 and Q for detection of single as well as multiple sensor faults. (C) 2016 Elsevier Ltd. All rights reserved.
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页码:334 / 347
页数:14
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