Academic Journal
Fault Detection for Multibatch Dynamic Nonstationary Processes Using Dynamic CCA and Multibatch Benchmark Distance.
| Τίτλος: | Fault Detection for Multibatch Dynamic Nonstationary Processes Using Dynamic CCA and Multibatch Benchmark Distance. |
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| Συγγραφείς: | Feng, Liwei, Tian, Bin, Zhao, Chenhao, Cui, Zhenhao, Li, Yuan |
| Πηγή: | Asia-Pacific Journal of Chemical Engineering; Nov/Dec2025, Vol. 20 Issue 6, p1-14, 14p |
| Θεματικοί όροι: | Fault diagnosis, Batch processing, Multivariate analysis, Chemical products manufacturing, Stochastic processes, Statistical models |
| Περίληψη: | Modern chemical production processes are complex and diverse, and fault detection methods have always been an important part of complex chemical production process research. The variance and mean of the multibatch dynamic nonstationary process vary with time, which makes the dynamic canonical correlation analysis (DCCA) method deficient in calculating statistics and modeling the multibatch process. Therefore, the DCCA with multibatch benchmark distance (DCCA–MBD) for fault detection method is proposed in this paper. Based on the use of DCCA, the detection of multibatch dynamic nonstationary process is based on DCCA improved by the multibatch modeling scheme and statistic calculation method of the MBD method. Through numerical simulation and monitoring experiments of continuous stirred tank reactor production, it is verified that the method of this paper is more effective in dealing with multibatch dynamic nonstationary process compared with conventional methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Asia-Pacific Journal of Chemical Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 190384252 RelevancyScore: 1023 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1023.09326171875 |
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| Items | – Name: Title Label: Title Group: Ti Data: Fault Detection for Multibatch Dynamic Nonstationary Processes Using Dynamic CCA and Multibatch Benchmark Distance. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Feng%2C+Liwei%22">Feng, Liwei</searchLink><br /><searchLink fieldCode="AR" term="%22Tian%2C+Bin%22">Tian, Bin</searchLink><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Chenhao%22">Zhao, Chenhao</searchLink><br /><searchLink fieldCode="AR" term="%22Cui%2C+Zhenhao%22">Cui, Zhenhao</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Yuan%22">Li, Yuan</searchLink> – Name: TitleSource Label: Source Group: Src Data: Asia-Pacific Journal of Chemical Engineering; Nov/Dec2025, Vol. 20 Issue 6, p1-14, 14p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Batch+processing%22">Batch processing</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+products+manufacturing%22">Chemical products manufacturing</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+processes%22">Stochastic processes</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Modern chemical production processes are complex and diverse, and fault detection methods have always been an important part of complex chemical production process research. The variance and mean of the multibatch dynamic nonstationary process vary with time, which makes the dynamic canonical correlation analysis (DCCA) method deficient in calculating statistics and modeling the multibatch process. Therefore, the DCCA with multibatch benchmark distance (DCCA–MBD) for fault detection method is proposed in this paper. Based on the use of DCCA, the detection of multibatch dynamic nonstationary process is based on DCCA improved by the multibatch modeling scheme and statistic calculation method of the MBD method. Through numerical simulation and monitoring experiments of continuous stirred tank reactor production, it is verified that the method of this paper is more effective in dealing with multibatch dynamic nonstationary process compared with conventional methods. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Asia-Pacific Journal of Chemical Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/apj.70087 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: Fault diagnosis Type: general – SubjectFull: Batch processing Type: general – SubjectFull: Multivariate analysis Type: general – SubjectFull: Chemical products manufacturing Type: general – SubjectFull: Stochastic processes Type: general – SubjectFull: Statistical models Type: general Titles: – TitleFull: Fault Detection for Multibatch Dynamic Nonstationary Processes Using Dynamic CCA and Multibatch Benchmark Distance. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Feng, Liwei – PersonEntity: Name: NameFull: Tian, Bin – PersonEntity: Name: NameFull: Zhao, Chenhao – PersonEntity: Name: NameFull: Cui, Zhenhao – PersonEntity: Name: NameFull: Li, Yuan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov/Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19322135 Numbering: – Type: volume Value: 20 – Type: issue Value: 6 Titles: – TitleFull: Asia-Pacific Journal of Chemical Engineering Type: main |
| ResultId | 1 |