Optimal Variable Transmission for Distributed Local Fault Detection Incorporating RA and Evolutionary Optimization
Determining the variable transmission structure is the key step in designing a distributed monitoring scheme for multiunit processes. This paper proposes randomized algorithm (RA) integrated with evolutionary optimization-based data-driven distributed local fault detection scheme to achieve efficien...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8240584/ |
| _version_ | 1819169580225921024 |
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| author | Qingchao Jiang Yang Wang Xuefeng Yan |
| author_facet | Qingchao Jiang Yang Wang Xuefeng Yan |
| author_sort | Qingchao Jiang |
| collection | DOAJ |
| description | Determining the variable transmission structure is the key step in designing a distributed monitoring scheme for multiunit processes. This paper proposes randomized algorithm (RA) integrated with evolutionary optimization-based data-driven distributed local fault detection scheme to achieve efficient monitoring of multiunit chemical processes. First, the RA is employed to generate faulty validation data. Second, evolutionary optimization-based variable transmission structure determination is performed to achieve the minimal non-detection rate by selecting transferred variables. Then, a principal component analysis (PCA) or kernel PCA monitoring model is established for each operation unit to identify the status of the unit. Last, a comprehensive index is developed to identify the status of the entire process. The established local monitors consider the relationship with other units but avoid introducing redundant information, thereby exhibiting superior monitoring performance. Case studies on two numerical examples, including a linear and a nonlinear case and the Tennessee Eastman benchmark process, are provided. Comparative results of traditional local or global monitoring methods verify the efficiency of the proposed monitoring scheme. |
| first_indexed | 2024-12-22T19:21:45Z |
| format | Article |
| id | doaj.art-bb4dc8a6acd0420c902cbd350934e49d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:21:45Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bb4dc8a6acd0420c902cbd350934e49d2022-12-21T18:15:21ZengIEEEIEEE Access2169-35362018-01-0163201321110.1109/ACCESS.2017.27876208240584Optimal Variable Transmission for Distributed Local Fault Detection Incorporating RA and Evolutionary OptimizationQingchao Jiang0https://orcid.org/0000-0002-3402-9018Yang Wang1Xuefeng Yan2Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, ChinaSchool of Electric Engineering, Shanghai Dianji University, Shanghai, ChinaKey Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, ChinaDetermining the variable transmission structure is the key step in designing a distributed monitoring scheme for multiunit processes. This paper proposes randomized algorithm (RA) integrated with evolutionary optimization-based data-driven distributed local fault detection scheme to achieve efficient monitoring of multiunit chemical processes. First, the RA is employed to generate faulty validation data. Second, evolutionary optimization-based variable transmission structure determination is performed to achieve the minimal non-detection rate by selecting transferred variables. Then, a principal component analysis (PCA) or kernel PCA monitoring model is established for each operation unit to identify the status of the unit. Last, a comprehensive index is developed to identify the status of the entire process. The established local monitors consider the relationship with other units but avoid introducing redundant information, thereby exhibiting superior monitoring performance. Case studies on two numerical examples, including a linear and a nonlinear case and the Tennessee Eastman benchmark process, are provided. Comparative results of traditional local or global monitoring methods verify the efficiency of the proposed monitoring scheme.https://ieeexplore.ieee.org/document/8240584/Data-driven distributed monitoringfault detectionevolutionary algorithmrandomized algorithmmultiunit chemical processes |
| spellingShingle | Qingchao Jiang Yang Wang Xuefeng Yan Optimal Variable Transmission for Distributed Local Fault Detection Incorporating RA and Evolutionary Optimization IEEE Access Data-driven distributed monitoring fault detection evolutionary algorithm randomized algorithm multiunit chemical processes |
| title | Optimal Variable Transmission for Distributed Local Fault Detection Incorporating RA and Evolutionary Optimization |
| title_full | Optimal Variable Transmission for Distributed Local Fault Detection Incorporating RA and Evolutionary Optimization |
| title_fullStr | Optimal Variable Transmission for Distributed Local Fault Detection Incorporating RA and Evolutionary Optimization |
| title_full_unstemmed | Optimal Variable Transmission for Distributed Local Fault Detection Incorporating RA and Evolutionary Optimization |
| title_short | Optimal Variable Transmission for Distributed Local Fault Detection Incorporating RA and Evolutionary Optimization |
| title_sort | optimal variable transmission for distributed local fault detection incorporating ra and evolutionary optimization |
| topic | Data-driven distributed monitoring fault detection evolutionary algorithm randomized algorithm multiunit chemical processes |
| url | https://ieeexplore.ieee.org/document/8240584/ |
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