標題: Adaptive Kernel Principal Component Analysis (KPCA) for Monitoring Small Disturbances of Nonlinear Processes
作者: Cheng, Chun-Yuan
Hsu, Chun-Chin
Chen, Mu-Chen
運輸與物流管理系 註:原交通所+運管所
Department of Transportation and Logistics Management
公開日期: 3-三月-2010
摘要: The Tennessee Eastman (TE) process, created by Eastman Chemical Company, is a complex nonlinear process. Many, previous studies focus on the detectability of monitoring a multivariate process by using TE process as an example. Principal component analysis (PCA) is a widely used dimension-reduction tool for monitoring multivariate linear process. Recently, the kernel principal component analysis (KPCA) has emerged as all effective method to tackling the problem of nonlinear data. Nevertheless, the conventional KPCA used the sum of squares of latest observations is the monitoring statistics and hence failed to detect small disturbance of the process. To enhance the delectability of the KPCA-based monitoring method, an adaptive KPCA-based monitoring statistic is proposed in this paper. The basic idea of the proposed method is first adopting the multivariate exponentially moving average to predict the process mean shifts and then combining the estimated mean shifts with the extracted components by KPCA to construct the adaptive monitoring statistic. The efficiency of the proposed monitoring scheme is implemented in a simulated nonlinear system and in the TE process. The experimental results indicate that the proposed method outperforms the traditional PCA and KPCA monitoring schemes.
URI: http://dx.doi.org/10.1021/ie900521b
http://hdl.handle.net/11536/5738
ISSN: 0888-5885
DOI: 10.1021/ie900521b
期刊: INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
Volume: 49
Issue: 5
起始頁: 2254
結束頁: 2262
顯示於類別:期刊論文


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