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

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dc.contributor.advisor O'Sullivan, Dominic en
dc.contributor.author O'Donovan, Peter
dc.date.accessioned 2018-08-03T11:03:04Z
dc.date.available 2018-08-03T11:03:04Z
dc.date.issued 2018
dc.date.submitted 2018
dc.identifier.citation O'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.endpage 217 en
dc.identifier.uri http://hdl.handle.net/10468/6574
dc.description.abstract Industrial 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.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2018, Peter O'Donovan. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject Industry 4.0 en
dc.subject Smart manufacturing en
dc.subject Machine learning en
dc.subject Cyber-physical systems en
dc.title An industrial analytics methodology and fog computing cyber-physical system for Industry 4.0 embedded machine learning applications en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD en
dc.internal.availability Full text available en
dc.check.info Not applicable en
dc.description.version Accepted Version
dc.contributor.funder Irish Research Council en
dc.description.status Not peer reviewed en
dc.internal.school Civil and Environmental Engineering en
dc.check.type No Embargo Required
dc.check.reason Not applicable en
dc.check.opt-out Not applicable en
dc.thesis.opt-out false
dc.check.embargoformat Embargo not applicable (If you have not submitted an e-thesis or do not want to request an embargo) en
ucc.workflow.supervisor dominic.osullivan@ucc.ie
dc.internal.conferring Autumn 2018 en


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© 2018, Peter O'Donovan. Except where otherwise noted, this item's license is described as © 2018, Peter O'Donovan.
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