An industrial analytics methodology and fog computing cyber-physical system for Industry 4.0 embedded machine learning applications

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dc.check.embargoformatEmbargo not applicable (If you have not submitted an e-thesis or do not want to request an embargo)en
dc.check.infoNot applicableen
dc.check.opt-outNot applicableen
dc.check.reasonNot applicableen
dc.check.typeNo Embargo Required
dc.contributor.advisorO'Sullivan, Dominicen
dc.contributor.authorO'Donovan, Peter
dc.contributor.funderIrish Research Councilen
dc.date.accessioned2018-08-03T11:03:04Z
dc.date.available2018-08-03T11:03:04Z
dc.date.issued2018
dc.date.submitted2018
dc.description.abstractIndustrial cyber-physical systems are the primary enabling technology for Industry 4.0, which combine legacy industrial and control engineering, with emerging technology paradigms (e.g. big data, internet-of-things, artificial intelligence, and machine learning), to derive self-aware and self-configuring factories capable of delivering major production innovations. However, the technologies and architectures needed to connect and extend physical factory operations to the cyber world have not been fully resolved. Although cloud computing and service-oriented architectures demonstrate strong adoption, such implementations are commonly produced using information technology perspectives, which can overlook engineering, control and Industry 4.0 design concerns relating to real-time performance, reliability or resilience. Hence, this research compares the latency and reliability performance of cyber-physical interfaces implemented using traditional cloud computing (i.e. centralised), and emerging fog computing (i.e. decentralised) paradigms, to deliver real-time embedded machine learning engineering applications for Industry 4.0. The findings highlight that despite the cloud’s highly scalable processing capacity, the fog’s decentralised, localised and autonomous topology may provide greater consistency, reliability, privacy and security for Industry 4.0 engineering applications, with the difference in observed maximum latency ranging from 67.7% to 99.4%. In addition, communication failures rates highlighted differences in both consistency and reliability, with the fog interface successfully responding to 900,000 communication requests (i.e. 0% failure rate), and the cloud interface recording failure rates of 0.11%, 1.42%, and 6.6% under varying levels of stress.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationO'Donovan, P. 2018. An industrial analytics methodology and fog computing cyber-physical system for Industry 4.0 embedded machine learning applications. PhD Thesis, University College Cork.en
dc.identifier.endpage217en
dc.identifier.urihttps://hdl.handle.net/10468/6574
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2018, Peter O'Donovan.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectIndustry 4.0en
dc.subjectSmart manufacturingen
dc.subjectMachine learningen
dc.subjectCyber-physical systemsen
dc.thesis.opt-outfalse
dc.titleAn industrial analytics methodology and fog computing cyber-physical system for Industry 4.0 embedded machine learning applicationsen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhDen
ucc.workflow.supervisordominic.osullivan@ucc.ie
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