Recursive-CPLS-Based Quality-Relevant and Process-Relevant Fault Monitoring With Application to the Tennessee Eastman Process
In industrial processes, the quality of the product is crucial. The batch partial least squares (PLS) monitoring model can effectively monitor for quality-related faults. In process monitoring, to overcome time-varying disturbances, the monitoring model needs to be updated regularly. Efficiently upd...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8824081/ |
| _version_ | 1818621708544770048 |
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| author | Changhua Hu Zhongying Xu Xiangyu Kong Jiayu Luo |
| author_facet | Changhua Hu Zhongying Xu Xiangyu Kong Jiayu Luo |
| author_sort | Changhua Hu |
| collection | DOAJ |
| description | In industrial processes, the quality of the product is crucial. The batch partial least squares (PLS) monitoring model can effectively monitor for quality-related faults. In process monitoring, to overcome time-varying disturbances, the monitoring model needs to be updated regularly. Efficiently updating the monitoring model represents a serious problem. This paper proposes a recursive concurrent projection to latent structures (RCPLS) algorithm, which can both update models more efficiently with historical model parameters and new data and provide better quality-related fault monitoring results than can static concurrent projection to latent structures (CPLS). Based on RCPLS, a complete set of process monitoring technologies is proposed. These technologies can automatically filter and store modellable data and adaptively update the online monitoring model. The updated computational quantities of the RCPLS model and the CPLS model are compared through the Tennessee Eastman process (TEP). The effectiveness of the RCPLS algorithm is verified, and a comprehensive comparison of the quality-related fault detection capabilities of RCPLS and CPLS is performed. The results show that RCPLS can significantly reduce the computational burden and increase the monitoring performance. |
| first_indexed | 2024-12-16T18:13:34Z |
| format | Article |
| id | doaj.art-29e914aee93546adab9075742827e82d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T18:13:34Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-29e914aee93546adab9075742827e82d2022-12-21T22:21:42ZengIEEEIEEE Access2169-35362019-01-01712874612875710.1109/ACCESS.2019.29391638824081Recursive-CPLS-Based Quality-Relevant and Process-Relevant Fault Monitoring With Application to the Tennessee Eastman ProcessChanghua Hu0Zhongying Xu1Xiangyu Kong2Jiayu Luo3https://orcid.org/0000-0003-3453-2286Xi’an Research Institute of High Technology, Xi’an, ChinaXi’an Research Institute of High Technology, Xi’an, ChinaXi’an Research Institute of High Technology, Xi’an, ChinaXi’an Research Institute of High Technology, Xi’an, ChinaIn industrial processes, the quality of the product is crucial. The batch partial least squares (PLS) monitoring model can effectively monitor for quality-related faults. In process monitoring, to overcome time-varying disturbances, the monitoring model needs to be updated regularly. Efficiently updating the monitoring model represents a serious problem. This paper proposes a recursive concurrent projection to latent structures (RCPLS) algorithm, which can both update models more efficiently with historical model parameters and new data and provide better quality-related fault monitoring results than can static concurrent projection to latent structures (CPLS). Based on RCPLS, a complete set of process monitoring technologies is proposed. These technologies can automatically filter and store modellable data and adaptively update the online monitoring model. The updated computational quantities of the RCPLS model and the CPLS model are compared through the Tennessee Eastman process (TEP). The effectiveness of the RCPLS algorithm is verified, and a comprehensive comparison of the quality-related fault detection capabilities of RCPLS and CPLS is performed. The results show that RCPLS can significantly reduce the computational burden and increase the monitoring performance.https://ieeexplore.ieee.org/document/8824081/Projection to latent structureprocess monitoringquality-relatedmodel updating |
| spellingShingle | Changhua Hu Zhongying Xu Xiangyu Kong Jiayu Luo Recursive-CPLS-Based Quality-Relevant and Process-Relevant Fault Monitoring With Application to the Tennessee Eastman Process IEEE Access Projection to latent structure process monitoring quality-related model updating |
| title | Recursive-CPLS-Based Quality-Relevant and Process-Relevant Fault Monitoring With Application to the Tennessee Eastman Process |
| title_full | Recursive-CPLS-Based Quality-Relevant and Process-Relevant Fault Monitoring With Application to the Tennessee Eastman Process |
| title_fullStr | Recursive-CPLS-Based Quality-Relevant and Process-Relevant Fault Monitoring With Application to the Tennessee Eastman Process |
| title_full_unstemmed | Recursive-CPLS-Based Quality-Relevant and Process-Relevant Fault Monitoring With Application to the Tennessee Eastman Process |
| title_short | Recursive-CPLS-Based Quality-Relevant and Process-Relevant Fault Monitoring With Application to the Tennessee Eastman Process |
| title_sort | recursive cpls based quality relevant and process relevant fault monitoring with application to the tennessee eastman process |
| topic | Projection to latent structure process monitoring quality-related model updating |
| url | https://ieeexplore.ieee.org/document/8824081/ |
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