Condition Recognition Strategy Based on Fuzzy Clustering With Information Granulation for Blast Furnace

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dc.authoridDu, Sheng/0000-0001-8396-7388
dc.authoridWu, Min/0000-0002-0668-8315
dc.authoridHu, Jie/0000-0002-1725-6366
dc.authorwosidDu, Sheng/B-9621-2019
dc.contributor.authorHuang, Yuanfeng
dc.contributor.authorDu, Sheng
dc.contributor.authorHu, Jie
dc.contributor.authorPedrycz, Witold
dc.contributor.authorWu, Min
dc.date.accessioned2024-05-19T14:39:29Z
dc.date.available2024-05-19T14:39:29Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe temperature of the cooling stave (TCS) is an important state parameter to indicate the states of the slag crust during the blast furnace ironmaking process. The state of the slag crust affects the quality and production of pig iron, and the gas flow distribution in the blast furnace. Thus, it is necessary to recognize the states of the slag crust. This article proposes a condition recognition strategy based on fuzzy clustering endowed with a novel distance with information granulation for recognizing the states of the slag crust. First, the raw TCS time-series data are split into segments according to the appropriate segmentation length, and the segments are represented in a granular form by the information granulation method. Then, information granules are clustered using fuzzy clustering endowed with a novel distance. After completing the data representation, each information granule is compounded of a lower bound and an upper bound that indicate the dynamic characteristics of the corresponding segments. In the fuzzy clustering, information granulation distance, a new distance, is established to measure the similarity between two information granules. Finally, the data experiments using the datasets from the UCR time-series database and actual industrial data from the blast furnace demonstrate the effectiveness and superiority of the proposed condition recognition strategy.en_US
dc.description.sponsorshipNational Natural Science Foundation of Chinaen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1109/TII.2023.3341253
dc.identifier.issn1551-3203
dc.identifier.issn1941-0050
dc.identifier.scopus2-s2.0-85181572940en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1109/TII.2023.3341253
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4789
dc.identifier.wosWOS:001134427500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Industrial Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectBlast Furnaceen_US
dc.subjectCondition Recognitionen_US
dc.subjectDistance Measureen_US
dc.subjectFuzzy Clusteringen_US
dc.subjectInformation Granulationen_US
dc.titleCondition Recognition Strategy Based on Fuzzy Clustering With Information Granulation for Blast Furnaceen_US
dc.typeArticleen_US

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