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dc.contributor.authorTeoh, YKen
dc.contributor.authorGill, SSen
dc.contributor.authorParlikad, Ajithen
dc.date.accessioned2021-01-09T00:30:19Z
dc.date.available2021-01-09T00:30:19Z
dc.date.issued2021-01-01en
dc.identifier.issn2327-4662
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/315942
dc.description.abstractThe assets in Industry 4.0 are categorised into physical, virtual and human. The innovation and popularisation of ubiquitous computing enhance the usage of smart devices: RFID tags, QR codes, LoRa tags, etc. for assets identification and tracking. The generated data from Industrial Internet of Things (IIoT) eases information visibility and process automation in Industry 4.0. Virtual assets include the data produced from IIoT. One of the applications of the industrial big data is to predict the failure of manufacturing equipment. Predictive maintenance enables the business owner to decide such as repairing or replacing the component before an actual failure which affects the whole production line. Therefore, Industry 4.0 requires an effective asset management to optimise the tasks distributions and predictive maintenance model. This paper presents the Genetic Algorithm (GA) based resource management integrating with machine learning for predictive maintenance in fog computing. The time, cost and energy performance of GA along with MinMin, MaxMin, FCFS, RoundRobin are simulated in the FogWorkflowsim. The predictive maintenance model is built in two-class logistic regression using real-time datasets. The results demonstrate that the proposed technique outperforms MinMin, MaxMin, FCFS, RoundRobin in execution time, cost and energy usage. The execution time is 0.48%faster, 5.43% lower cost and energy usage is 28.10% lower in comparison with second-best results. The training and testing accuracy of the prediction model is 95.1% and 94.5%, respectively.
dc.publisherInstitute of Electrical and Electronics Engineers
dc.rightsAll rights reserved
dc.rights.uri
dc.titleIoT and Fog Computing based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 using Machine Learningen
dc.typeArticle
prism.publicationDate2021en
prism.publicationNameIEEE Internet of Things Journalen
dc.identifier.doi10.17863/CAM.63052
dcterms.dateAccepted2021-01-06en
rioxxterms.versionofrecord10.1109/JIOT.2021.3050441en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2021-01-01en
dc.contributor.orcidParlikad, Ajith [0000-0001-6214-1739]
dc.identifier.eissn2327-4662
rioxxterms.typeJournal Article/Reviewen
cam.orpheus.successMon Feb 01 07:30:41 GMT 2021 - Embargo updated*
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rioxxterms.freetoread.startdate2021-01-01


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