Soft-Sensing of burn-through point based on weighted kernel just-in-time learning and fuzzy broad-learning system in sintering process

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dc.authorscopusidWitold Pedrycz / 56854903200
dc.authorwosidWitold Pedrycz / FPE-7309-2022
dc.contributor.authorHu, Jie
dc.contributor.authorWu, Min
dc.contributor.authorCao, Weihua
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2025-04-18T10:34:06Z
dc.date.available2025-04-18T10:34:06Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractBurn-through point (BTP) is an essential thermal state parameter in a sintering process, which is a direct reflection of the stability of this process. However, it cannot be measured online. Soft-sensing technology offers a reliable method for estimating unmeasurable variables in industrial processes. Here, a soft-sensing model for BTP based on weighted kernel just-in-time learning (WKJITL) and fuzzy broad-learning system (FBLS) is built. First, an abnormal production data detection and correction strategy is employed to process the production data, and the mechanism analysis and mutual information analysis are utilized to specify the detectable process variables that are directly related to BTP. Then, the WKJITL method is proposed to obtain historical production data similar to the query data of BTP for local learning modeling, and the FBLS is utilized as an efficient modeling method for the soft-sensing prediction of BTP. Finally, the results of simulation experiments based on actual sintering production data reveal that the developed soft-sensing model of BTP exhibits better prediction accuracy and efficiency compared with some advanced modeling methods. Furthermore, the proposed method is of general nature and can also be easily applied to other industrial processes.
dc.identifier.citationHu, J., Wu, M., Cao, W., & Pedrycz, W. (2024). Soft-Sensing of Burn-Through Point Based on Weighted Kernel Just-in-Time Learning and Fuzzy Broad-Learning System in Sintering Process. IEEE Transactions on Industrial Informatics.
dc.identifier.doi10.1109/TII.2024.3359444
dc.identifier.endpage7324
dc.identifier.issn1551-3203
dc.identifier.issn1941-0050
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85185375791
dc.identifier.scopusqualityQ1
dc.identifier.startpage7316
dc.identifier.urihttp://dx.doi.org/10.1109/TII.2024.3359444
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7131
dc.identifier.volume20
dc.identifier.wosWOS:001221255100066
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartofIEEE Transactions on Industrial Informatics
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectProduction
dc.subjectData Models
dc.subjectSintering
dc.subjectKernel
dc.subjectPredictive Models
dc.subjectInput Variables
dc.subjectComputational Modeling
dc.subjectActual Production Data
dc.subjectBurn-Through Point (BTP)
dc.subjectFuzzy Broad-Learning System (FBLS)
dc.subjectSintering Process
dc.subjectWeighted Kernel Just-in-Time Learning (WKJITL)
dc.titleSoft-Sensing of burn-through point based on weighted kernel just-in-time learning and fuzzy broad-learning system in sintering process
dc.typeArticle

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