標題: | Integrating independent component analysis and support vector machine for multivariate process monitoring |
作者: | Hsu, Chun-Chin Chen, Mu-Chen Chen, Long-Sheng 運輸與物流管理系 註:原交通所+運管所 Department of Transportation and Logistics Management |
關鍵字: | ICA;PCA;SVM;TE process;Fault detection rate |
公開日期: | 1-八月-2010 |
摘要: | This study aims to develop an intelligent algorithm by integrating the independent component analysis (ICA) and support vector machine (SVM) for monitoring multivariate processes. For developing a successful SVM-based fault detector, the first step is feature extraction. In real industrial processes, process variables are rarely Gaussian distributed. Thus, this study proposes the application of ICA to extract the hidden information of a non-Gaussian process before conducting SVM. The proposed fault detector will be implemented via two simulated processes and a case study of the Tennessee Eastman process. Results demonstrate that the proposed method possesses superior fault detection when compared to conventional monitoring methods, including PCA, ICA, modified ICA, ICA-PCA and PCA-SVM. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved. |
URI: | http://dx.doi.org/10.1016/j.cie.2010.03.011 http://hdl.handle.net/11536/32330 |
ISSN: | 0360-8352 |
DOI: | 10.1016/j.cie.2010.03.011 |
期刊: | COMPUTERS & INDUSTRIAL ENGINEERING |
Volume: | 59 |
Issue: | 1 |
起始頁: | 145 |
結束頁: | 156 |
顯示於類別: | 期刊論文 |