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dc.contributor.authorCanizo, Mikel
dc.contributor.authorConde, Angel
dc.contributor.authorCharramendieta, Santiago
dc.contributor.authorMinon, Raul
dc.contributor.authorCid-Fuentes, Raul G.
dc.contributor.authorOnieva, Enrique
dc.date.accessioned2019-06-24T11:20:26Z
dc.date.available2019-06-24T11:20:26Z
dc.date.issued2019
dc.identifier.citationCanizo, Mikel, Angel Conde, Santiago Charramendieta, Raul Minon, Raul G. Cid-Fuentes, and Enrique Onieva. “Implementation of a Large-Scale Platform for Cyber-Physical System Real-Time Monitoring.” IEEE Access 7 (2019): 52455–52466. doi:10.1109/access.2019.2911979.en
dc.identifier.issn2169-3536en
dc.identifier.urihttp://hdl.handle.net/11556/726
dc.description.abstractThe emergence of Industry 4.0 and the Internet of Things (IoT) has meant that the manufacturing industry has evolved from embedded systems to cyber-physical systems (CPSs). This transformation has provided manufacturers with the ability to measure the performance of industrial equipment by means of data gathered from on-board sensors. This allows the status of industrial systems to be monitored and can detect anomalies. However, the increased amount of measured data has prompted many companies to investigate innovative ways to manage these volumes of data. In recent years, cloud computing and big data technologies have emerged among the scientific communities as key enabling technologies to address the current needs of CPSs. This paper presents a large-scale platform for CPS real-time monitoring based on big data technologies, which aims to perform real-time analysis that targets the monitoring of industrial machines in a real work environment. This paper is validated by implementing the proposed solution on a real industrial use case that includes several industrial press machines. The formal experiments in a real scenario are conducted to demonstrate the effectiveness of this solution and also its adequacy and scalability for future demand requirements. As a result of the implantation of this solution, the overall equipment effectiveness has been improved.en
dc.description.sponsorshipThe authors are grateful to Goizper and Fagor Arrasate for providing the industrial case study, and specifically Jon Rodriguez and David Chico (Fagor Arrasate) for their help and support. Any opinions, findings and conclusions expressed in this article are those of the authors and do not necessarily reflect the views of the funding agencies.en
dc.language.isoengen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en
dc.titleImplementation of a Large-Scale Platform for Cyber-Physical System Real-Time Monitoringen
dc.typearticleen
dc.identifier.doi10.1109/access.2019.2911979en
dc.rights.accessRightsopenAccessen
dc.subject.keywordsAnomaly detectionen
dc.subject.keywordsBig dataen
dc.subject.keywordsCyber-physical systemen
dc.subject.keywordsIndustry 4.0en
dc.subject.keywordsReal-time processingen
dc.journal.titleIEEE Accessen
dc.page.final52466en
dc.page.initial52455en
dc.volume.number7en


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