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The Bridge Between Artificial Intelligence and Predictive Maintenance in Industry 4.0

2026-05-14Documento de conferência Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorArez, Daniel
dc.contributor.authorNavas, Helena V. G.
dc.contributor.authorGaspar, Pedro
dc.contributor.editorAndrea Prati
dc.date.accessioned2026-07-30T10:05:15Z
dc.date.available2026-07-30T10:05:15Z
dc.date.issued2026-05-14
dc.description.abstractThis systematic literature review explores the intersection of Artificial Intelligence (AI) and Predictive Maintenance (PdM) within Industry 4.0. Using a PRISMA-based methodology, 123 studies published between 2014 and April 2024 were analyzed to characterize technological trends, algorithmic choices, industrial applications, and evaluation practices. The review reveals a consistent growth of research interest, driven by the widespread adoption of Internet of Things (IoT) devices and increased data availability. The manufacturing sector dominates the literature, although most studies rely on standardized datasets rather than real industrial environments. Among the identified AI methods, Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT) and K-Nearest Neighbors (KNNs) represent the most frequently applied algorithms for tasks such as failure prediction, fault detection, and remaining useful life (RUL) estimation. Model performance is commonly evaluated with Accuracy (Acc), Precision, Recall, F1-Score, and Root Mean Square Error (RMSE), reflecting the prevalence of both classification and regression-based PdM analyses. Despite significant advances, this review identifies persistent gaps, including limited domain diversity, scarce long-term real-world validation, and insufficient use of eXplainable AI (XAI) techniques. The findings highlight the need for broader domain coverage, improved interpretability, and validation under realistic industrial conditions. Overall, this review consolidates current knowledge on AI-enabled PdM and outlines critical directions to enhance reliability, transparency, and industrial relevance in the context of Industry 4.0.eng
dc.identifier.doi10.3390/app16104882
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10400.26/63945
dc.language.isoeng
dc.peerreviewedyes
dc.relation.hasversionhttps://research.ebsco.com/plink/c3c934a3-3e95-3f62-a5d8-232ce8daaaeb
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial Intelligence
dc.subjectmachine learning
dc.subjectXAI
dc.subjectpredictive maintenance
dc.subjectIndustry 4.0
dc.subjectSmart Industry
dc.titleThe Bridge Between Artificial Intelligence and Predictive Maintenance in Industry 4.0por
dc.title.alternativeSystematic Reviewpor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferenceDate2026-05
oaire.citation.endPage23
oaire.citation.issue4882
oaire.citation.startPage1
oaire.citation.titleApplied Sciences
oaire.citation.volume2026, 16
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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EN - LIGM - Linha de Investigação em Gestão da Manutenção