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Comprehensive Analysis of Current Developments, Challenges, and Opportunities for the Health Assessment of Smart Factory

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dc.contributor.authorRaouf, Izaz-
dc.contributor.authorKumar, Prashant-
dc.contributor.authorKhalid, Salman-
dc.contributor.authorKim, Heung Soo-
dc.date.accessioned2025-02-04T05:00:10Z-
dc.date.available2025-02-04T05:00:10Z-
dc.date.issued2025-07-
dc.identifier.issn2288-6206-
dc.identifier.issn2198-0810-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/57563-
dc.description.abstractPrognostic and health management (PHM) is an approach that allows real-time health monitoring of any system. With the advancement of Industry 4.0, which integrates updated technologies, such as the Internet of Things, artificial intelligence, and automation, to optimize the efficiency, productivity, and flexibility of manufacturing processes. PHM is critical for ensuring the dependability and availability of smart factory components, which due to continuous operation, are prone to wear and tear. This paper presents a comprehensive examination of the component level-based PHM in the smart factory. It introduces PHM, smart factory and its various subcomponents. Various aspects of PHM are discussed for robotic systems, sensors, electrical machines, and auxiliary components. The paper concludes with practical recommendations for researchers interested in implementing PHM in the smart factory.-
dc.format.extent18-
dc.language영어-
dc.language.isoENG-
dc.publisher한국정밀공학회-
dc.titleComprehensive Analysis of Current Developments, Challenges, and Opportunities for the Health Assessment of Smart Factory-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.1007/s40684-025-00694-4-
dc.identifier.scopusid2-s2.0-85217219360-
dc.identifier.wosid001401844900001-
dc.identifier.bibliographicCitationInternational Journal of Precision Engineering and Manufacturing-Green Technology, v.12, no.4, pp 1321 - 1338-
dc.citation.titleInternational Journal of Precision Engineering and Manufacturing-Green Technology-
dc.citation.volume12-
dc.citation.number4-
dc.citation.startPage1321-
dc.citation.endPage1338-
dc.type.docTypeReview-
dc.identifier.kciidART003212177-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.relation.journalResearchAreaScience & Technology - Other Topics-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryGreen & Sustainable Science & Technology-
dc.relation.journalWebOfScienceCategoryEngineering, Manufacturing-
dc.relation.journalWebOfScienceCategoryEngineering, Mechanical-
dc.subject.keywordPlusMECHANICAL FAULT-DETECTION-
dc.subject.keywordPlusDATA-DRIVEN-
dc.subject.keywordPlusELECTRICAL MOTORS-
dc.subject.keywordPlusLIFE ESTIMATION-
dc.subject.keywordPlusINDUSTRY 4.0-
dc.subject.keywordPlusDIAGNOSIS-
dc.subject.keywordPlusNETWORK-
dc.subject.keywordPlusACCELEROMETER-
dc.subject.keywordPlusMANAGEMENT-
dc.subject.keywordPlusALGORITHM-
dc.subject.keywordAuthorPrognostic health management-
dc.subject.keywordAuthorArtificial intelligence-
dc.subject.keywordAuthorIndustry automation-
dc.subject.keywordAuthorSmart factory-
dc.subject.keywordAuthorFault detection-
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