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Enhancing predictive accuracy in aircraft engine mro using clustering and similarity methods

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dc.contributor.authorMendonça, Leonardo
dc.contributor.authorPires, Flávia
dc.contributor.authorBarbosa, José
dc.contributor.authorDuarte, Miguel
dc.contributor.authorLeitão, Paulo
dc.date.accessioned2026-03-26T16:16:24Z
dc.date.available2026-03-26T16:16:24Z
dc.date.issued2025
dc.description.abstractIn the aircraft engine Maintenance, Repair, and Overhaul (MRO) process, effective task planning relies heavily on the expertise of lead engineers. However, when predictive models are used to assist decision-making, issues with incomplete, unbalanced, and inconsistent data can lead to errors in the planning task. Therefore, reliable predictions are crucial for optimising the operational efficiency. This paper proposes a methodology to improve the prediction of maintenance times for an aircraft engine MRO process by integrating K-means clustering with Cosine and Jaccard similarity methods to define a reliable prediction interval. This methodology was compared with the predictions of the Simple Linear Regression method, which resulted in the prediction interval approach significantly reducing prediction errors, increasing prediction accuracy, while optimising process management and maintenance task planning throughout the MRO process.por
dc.description.sponsorshipThis work was supported by national funds: UID/05757 - Research Centre in Digitalization and Intelligent Robotics (CeDRI); and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020). Additionally, this work is cofinanced by Component 5 - Capitalization and Business Innovation, integrated in the Resilience Dimension of the Recovery and Resilience Plan within the scope of the Recovery and Resilience Mechanism (MRR) of the European Union (EU), framed in the Next Generation EU, for the period 2021 - 2026, within project Produtech_R3, with reference 60.
dc.identifier.citationMendonça, Leonardo; Pires, Flávia; Barbosa, José; Duarte, Miguel; Leitão, Paulo (2025). Enhancing predictive accuracy in aircraft engine mro using clustering and similarity methods. In 30th International Conference on Emerging Technologies and Factory Automation (ETFA). Porto, Portugal. p. 1-8. ISSN 1946-0759. DOI: 10.1109/ETFA65518.2025.11205599
dc.identifier.doi10.1109/ETFA65518.2025.11205599
dc.identifier.issn1946-0759
dc.identifier.urihttp://hdl.handle.net/10198/36329
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions - LA/P/0007/2020
dc.relation.hasversionhttps://ieeexplore.ieee.org/abstract/document/11205599
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSimilarity Methods
dc.subjectPrediction
dc.subjectMRO
dc.subjectAircraft Engines
dc.titleEnhancing predictive accuracy in aircraft engine mro using clustering and similarity methodseng
dc.typeconference object
dspace.entity.typePublication
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions - LA/P/0007/2020
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.conferenceDate2025
oaire.citation.conferencePlacePorto, Portugal
oaire.citation.endPage8
oaire.citation.startPage1
oaire.citation.title30th International Conference on Emerging Technologies and Factory Automation (ETFA)
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa
person.familyNameMendonça
person.familyNamePires
person.familyNameBarbosa
person.familyNameLeitão
person.givenNameLeonardo
person.givenNameFlávia
person.givenNameJosé
person.givenNamePaulo
person.identifierhttps://scholar.google.pt/citations?user=an9quSsAAAAJ&hl=pt-PT
person.identifier609187
person.identifierA-8390-2011
person.identifier.ciencia-id4013-B18E-3113
person.identifier.ciencia-idA119-72AB-6255
person.identifier.ciencia-id021B-4191-D8A5
person.identifier.ciencia-id8316-8F13-DA71
person.identifier.orcid0000-0001-7899-3020
person.identifier.orcid0000-0003-3151-6686
person.identifier.orcid0000-0002-2151-7944
person.identifier.ridA-5468-2011
person.identifier.scopus-author-id0000-0003-3112-7672
person.identifier.scopus-author-id57200412919
person.identifier.scopus-author-id48360905400
person.identifier.scopus-author-id35584388900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication1a0ce5ee-7310-4025-affe-74c544935e02
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