SPA-Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring
Many industrial processes are operated in multiple modes due to different manufacturing strategies. Multimodality of process data is often accompanied with nonlinear and non-Gaussian characteristics, which makes data-driven monitoring more complicated. In this paper, statistics pattern analysis (SPA...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5517062 |
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| author | Zhaojing Wang Weidong Yang Hong Zhang Ying Zheng |
| author_facet | Zhaojing Wang Weidong Yang Hong Zhang Ying Zheng |
| author_sort | Zhaojing Wang |
| collection | DOAJ |
| description | Many industrial processes are operated in multiple modes due to different manufacturing strategies. Multimodality of process data is often accompanied with nonlinear and non-Gaussian characteristics, which makes data-driven monitoring more complicated. In this paper, statistics pattern analysis (SPA) is introduced to extract low- and high-order statistics from raw process data. Support vector data description (SVDD), which can deal with nonlinear and non-Gaussian problems, is applied to monitor multimode process in this paper. To improve detection performance of SVDD for training multimode data with outliers, modified local reachability density ratio (mLRDR) is proposed as a weight factor to be embedded in the weighted-SVDD (wSVDD) model, in which the local neighbors in terms of both space and time are considered. Finally, the effectiveness and superiority of our proposed method are demonstrated by the Tennessee-Eastman (TE) process and wastewater treatment process (WWTP). |
| format | Article |
| id | doaj-art-0b69bebea1944d6a9ce09ff0c4832b89 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0b69bebea1944d6a9ce09ff0c4832b892025-02-03T06:06:30ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55170625517062SPA-Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process MonitoringZhaojing Wang0Weidong Yang1Hong Zhang2Ying Zheng3The Key Laboratory of Image Information Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaThe Key Laboratory of Image Information Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaThe Key Laboratory of Image Information Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaThe Key Laboratory of Image Information Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaMany industrial processes are operated in multiple modes due to different manufacturing strategies. Multimodality of process data is often accompanied with nonlinear and non-Gaussian characteristics, which makes data-driven monitoring more complicated. In this paper, statistics pattern analysis (SPA) is introduced to extract low- and high-order statistics from raw process data. Support vector data description (SVDD), which can deal with nonlinear and non-Gaussian problems, is applied to monitor multimode process in this paper. To improve detection performance of SVDD for training multimode data with outliers, modified local reachability density ratio (mLRDR) is proposed as a weight factor to be embedded in the weighted-SVDD (wSVDD) model, in which the local neighbors in terms of both space and time are considered. Finally, the effectiveness and superiority of our proposed method are demonstrated by the Tennessee-Eastman (TE) process and wastewater treatment process (WWTP).http://dx.doi.org/10.1155/2021/5517062 |
| spellingShingle | Zhaojing Wang Weidong Yang Hong Zhang Ying Zheng SPA-Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring Complexity |
| title | SPA-Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring |
| title_full | SPA-Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring |
| title_fullStr | SPA-Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring |
| title_full_unstemmed | SPA-Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring |
| title_short | SPA-Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring |
| title_sort | spa based modified local reachability density ratio wsvdd for nonlinear multimode process monitoring |
| url | http://dx.doi.org/10.1155/2021/5517062 |
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