Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21026
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dc.contributor.authorAngadi, V-
dc.contributor.authorMousavi, A-
dc.contributor.authorBartolome, D-
dc.contributor.authorTellarini, M-
dc.contributor.authorFazziani, M-
dc.coverage.spatialAthens-
dc.date.accessioned2020-06-18T00:25:07Z-
dc.date.available2020-06-18T00:25:07Z-
dc.date.issued2020-06-
dc.identifier.issn2351-9789-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/21026-
dc.description.abstractThe proposed work describes a dynamic regression based event-tracker for high speed production process. The methodology discussed is a causal system and provides trends and estimations of the sensors based on a flexible regression model of the historical sensor values. A safety threshold is defined that provides a boundary of the tolerant working for the regime condition of production. This threshold is used as a reference to calculate the remaining useful life of the critical component. The estimated remaining useful life is compared with the Weibull reliability analysis. The proposed methodology provides a remaining useful life of ∼ 10 weeks for the thermal regulator use-case when compared to ∼ 9 weeks for Weibull analysis. The overestimation of the methodology is discussed and along with the alternative methodology. The sensitivity analysis is conducted on the noise and training periods are studied for better prediction.en_US
dc.description.sponsorshipEuropean Union’s Horizon 2020en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.source30th International Conference on Flexible Automation and Intelligent Manufacturing-
dc.source30th International Conference on Flexible Automation and Intelligent Manufacturing-
dc.subjectPredictive Maintenanceen_US
dc.subjectMachine Learningen_US
dc.subjectRegressionen_US
dc.subjectFailure-Rateen_US
dc.subjectEvent Based Predictionen_US
dc.subjectIndustrial Ioten_US
dc.subjectIndustry 4.0en_US
dc.subjectDegradation Modelen_US
dc.subjectRemaining Useful Lifeen_US
dc.titleRegressive Event-Tracker: A Causal Prediction Modelling of Degradation in High Speed Manufacturingen_US
dc.typeConference Paperen_US
dc.relation.isPartOfProcedia Manufacturing-
pubs.finish-date2021-06-18-
pubs.finish-date2021-06-18-
pubs.publication-statusAccepted-
pubs.start-date2021-06-15-
pubs.start-date2021-06-15-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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