Aircraft predictive maintenance modeling using a hybrid imbalance learning approach

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dc.contributor.authorDangut, Maren David
dc.contributor.authorSkaf, Zakwan
dc.contributor.authorJennions, Ian
dc.date.accessioned2021-03-03T15:44:13Z
dc.date.available2021-03-03T15:44:13Z
dc.date.issued2020-10-26
dc.description.abstractThe continued development of the industrial internet of things (IIoT) has caused an increase in the availability of industrial datasets. The massive availability of assets operational dataset has prompted more research interest in the area of condition-based maintenance, towards the API-lead integration for assets predictive maintenance modelling. The large data generated by industrial processes inherently comes along with different analytical challenges. Data imbalance is one of such problems that exist in datasets. It affects the performance of machine learning algorithms, which yields imprecise prediction. In this paper, we propose an advanced approach to handling imbalance classification problems in equipment heterogeneous datasets. The technique is based on a hybrid of soft mixed Gaussian processes with the EM method to improves the prediction of the minority class during learning. The algorithm is then used to develop a prognostic model for predicting aircraft component replacement. We validate the feasibility and effectiveness of our approach using real-time aircraft operation and maintenance datasets. The dataset spans over seven years. Our approach shows better performance compared to other similar methods.en_UK
dc.identifier.citationMaren DD, Zakwan S, Ian KJ (2020) Aircraft predictive maintenance modeling using a hybrid imbalance learning approach. In: TESConf 2020 - 9th International Conference on Through-life Engineering Services, Online, 3-4 November 2020en_UK
dc.identifier.urihttp://dx.doi.org/10.2139/ssrn.3718065
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16437
dc.language.isoenen_UK
dc.publisherSSRNen_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectprognosticsen_UK
dc.subjectdata-drivenen_UK
dc.subjectdata imbalanceen_UK
dc.subjectpredictive maintenanceen_UK
dc.subjectaircraften_UK
dc.titleAircraft predictive maintenance modeling using a hybrid imbalance learning approachen_UK
dc.typeConference paperen_UK

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