Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data Description
This study proposes a multi-block fault detection method based on fault-sensitive slow features for large-scale dynamic industrial processes. Firstly, slow feature analysis (SFA) can effectively extract the process dynamic information. However, the slowest changing features may not contain more faul...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9130672/ |
| _version_ | 1818433328852762624 |
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| author | Chao Zhai Xiaochen Sheng Weili Xiong |
| author_facet | Chao Zhai Xiaochen Sheng Weili Xiong |
| author_sort | Chao Zhai |
| collection | DOAJ |
| description | This study proposes a multi-block fault detection method based on fault-sensitive slow features for large-scale dynamic industrial processes. Firstly, slow feature analysis (SFA) can effectively extract the process dynamic information. However, the slowest changing features may not contain more fault information. Thus, through the analysis of T<sup>2</sup> statistic in SFA-based process monitoring model, a fault sensitivity coefficient is defined as a new slow feature sorting criterion to select the most sensitive slow features to fault in each variable direction. Then, considering the unknown characteristics of the fault in the real-time monitoring process, the monitoring model is established for each dimension of variables based on the multi-block strategy. Finally, the support vector data description is used as a fusing method to integrate the statistics calculated in each sub-block to obtain an intuitive detection result. The effectiveness and superiority of the proposed strategy are demonstrated by the experiments on Tennessee Eastman benchmark process and an actual blast furnace ironmaking process. |
| first_indexed | 2024-12-14T16:19:21Z |
| format | Article |
| id | doaj.art-b308ca222cee4a008f6d915c741f4fdf |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T16:19:21Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b308ca222cee4a008f6d915c741f4fdf2022-12-21T22:54:51ZengIEEEIEEE Access2169-35362020-01-01812073712074510.1109/ACCESS.2020.30062829130672Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data DescriptionChao Zhai0https://orcid.org/0000-0003-2687-5078Xiaochen Sheng1https://orcid.org/0000-0003-4580-1293Weili Xiong2https://orcid.org/0000-0002-9427-8809Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi, ChinaKey Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi, ChinaKey Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi, ChinaThis study proposes a multi-block fault detection method based on fault-sensitive slow features for large-scale dynamic industrial processes. Firstly, slow feature analysis (SFA) can effectively extract the process dynamic information. However, the slowest changing features may not contain more fault information. Thus, through the analysis of T<sup>2</sup> statistic in SFA-based process monitoring model, a fault sensitivity coefficient is defined as a new slow feature sorting criterion to select the most sensitive slow features to fault in each variable direction. Then, considering the unknown characteristics of the fault in the real-time monitoring process, the monitoring model is established for each dimension of variables based on the multi-block strategy. Finally, the support vector data description is used as a fusing method to integrate the statistics calculated in each sub-block to obtain an intuitive detection result. The effectiveness and superiority of the proposed strategy are demonstrated by the experiments on Tennessee Eastman benchmark process and an actual blast furnace ironmaking process.https://ieeexplore.ieee.org/document/9130672/Multi-block strategyslow feature analysisfault sensitivity coefficientsupport vector data descriptionfault detection |
| spellingShingle | Chao Zhai Xiaochen Sheng Weili Xiong Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data Description IEEE Access Multi-block strategy slow feature analysis fault sensitivity coefficient support vector data description fault detection |
| title | Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data Description |
| title_full | Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data Description |
| title_fullStr | Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data Description |
| title_full_unstemmed | Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data Description |
| title_short | Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data Description |
| title_sort | multi block fault detection for plant wide dynamic processes based on fault sensitive slow features and support vector data description |
| topic | Multi-block strategy slow feature analysis fault sensitivity coefficient support vector data description fault detection |
| url | https://ieeexplore.ieee.org/document/9130672/ |
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