Canonical correlation analysis-based fault detection methods with application to alumina evaporation process |
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| Affiliation: | 1. Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Bismarckstrasse 81 BB, 47057 Duisburg, Germany;2. School of Physics and Electronics, University of Central South, 410083 Changsha, China;1. Department of Automation, TNList, Tsinghua University, Beijing 100084, China;2. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China;1. Key Laboratory for Advanced Control of Iron and Steel Process of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, PR China;2. Departments of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA;1. Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Bismarckstrasse 81 BB, 47057 Duisburg, Germany;2. Research Center of Shaanxi Fast Auto Drive Group Company, Western Avenue 129, 710119 Xi’an, PR China;3. Key Laboratory for Advanced Control of Iron and Steel Process, School of Automation and Electrical Engineering, University of Science and Technology of Beijing, 100083 Beijing, PR China;1. Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, CA 90089, USA;2. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning 110819, China;3. School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China |
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| Abstract: | In this paper, canonical correlation analysis (CCA)-based fault detection methods are proposed for both static and dynamic processes. Different from the well-established process monitoring and fault diagnosis systems based on multivariate analysis techniques like principal component analysis and partial least squares, the core of the proposed methods is to build residual signals by means of the CCA technique for the fault detection purpose. The proposed methods are applied to an alumina evaporation process, and the achieved results show that both methods are applicable for fault detection, while the dynamic one delivers better detection performance. |
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| Keywords: | Canonical correlation analysis Residual generation Fault detection Alumina evaporation process |
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