Browse by author
Lookup NU author(s): Dr Jie ZhangORCiD
This is the authors' accepted manuscript of an article that has been published in its final definitive form by American Chemical Society, 2019.
For re-use rights please refer to the publisher's terms and conditions.
Kernel principal component analysis (KPCA) has shown its excellent performance in monitoring nonlinear industrial processes. However, model building, updating, and online monitoring using KPCA are generally time-consuming when massive data are obtained under the normal operation condition (NOC). The main reason is that the eigen-decomposition of high-dimensional kernelmatrix constructed from massive NOC samples is computationally complex. Many studies have been devoted to solving this problem through reducing the NOC samples, but KPCA model constructed from the reduced sample set cannot ensure good performance in monitoring nonlinear industrial processes. The performance of KPCA model depends on whether the results of eigen-decomposition of the reduced kernel matrix can well approximate that of the original kernel matrix. To improve the efficiency of KPCA based process monitoring, this paper proposes randomized KPCA for monitoring nonlinear industrial processes with massive data. The proposed method uses random sampling to compress kernel matrix into a subspace which maintains most of the usefulinformation about process monitoring. Then, the reduced kernel matrix is operated to obtain desired eigen-decomposition results. Based on these approximated eigendecomposition results, the proposed randomized KPCA can enhance the performance in monitoring nonlinear industrial processes. This is because the commonly used monitoring statistics are related to the eigenvalues and eigenvectors of kernel matrix. Finally, numerical simulation and the benchmark TE chemical process are used to demonstrate the effectiveness of the proposed method.
Author(s): Zhou Z, Du N, Xu J, Li Z, Wang P, Zhang J
Publication type: Article
Publication status: Published
Journal: Industrial & Engineering Chemistry Research
Year: 2019
Volume: 58
Issue: 24
Pages: 10410-10417
Print publication date: 19/06/2019
Online publication date: 28/05/2019
Acceptance date: 28/05/2019
Date deposited: 30/05/2019
ISSN (print): 0888-5885
ISSN (electronic): 1520-5045
Publisher: American Chemical Society
URL: https://doi.org/10.1021/acs.iecr.9b00300
DOI: 10.1021/acs.iecr.9b00300
Altmetrics provided by Altmetric
