Integrating independent component analysis and support vector machine for multivariate process monitoring

Authors:
Chun-Chin Hsu;Mu-Chen Chen;Long-Sheng Chen
Affiliations:
Department of Industrial Engineering and Management, Chaoyang University of Technology, 168 Jifong E. Rd., Wufong Township Taichung County 41349, Taiwan;Institute of Traffic and Transportation, National Chiao Tung University, 114 Chung Hsiao W. Rd., Sec. 1, Taipei 10012, Taiwan;Department of Information Management, Chaoyang University of Technology, 168 Jifong E. Rd., Wufong Township Taichung County 41349, Taiwan
Venue:
Computers and Industrial Engineering
Year:
2010

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Abstract

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.