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The Bridge Between Artificial Intelligence and Predictive Maintenance in Industry 4.0

2026-05-14Recensão Acesso aberto
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dc.contributor.authorArez, Daniel
dc.contributor.authorNavas, Helena V. G.
dc.contributor.authorGaspar, Pedro
dc.contributor.institutionUNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
dc.contributor.institutionDEMI - Departamento de Engenharia Mecânica e Industrial
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2026-07-10T08:36:01Z
dc.date.available2026-07-10T08:36:01Z
dc.date.issued2026-05-14
dc.descriptionPublisher Copyright: © 2026 by the authors.
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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent23
dc.format.extent1227884
dc.identifier.doi10.3390/app16104882
dc.identifier.issn2076-3417
dc.identifier.otherPURE: 164515479
dc.identifier.otherPURE UUID: 4222e171-03e8-4f02-a187-4720faccd321
dc.identifier.otherScopus: 105040268047
dc.identifier.otherWOS: 001774294800001
dc.identifier.urihttp://hdl.handle.net/10362/204418
dc.identifier.urlhttps://www.scopus.com/pages/publications/105040268047
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001774294800001
dc.language.isoeng
dc.peerreviewedyes
dc.subjectArtificial Intelligence
dc.subjectIndustry 4.0
dc.subjectMachine learning
dc.subjectPredictive maintenance
dc.subjectSmart Industry
dc.subjectXAI
dc.subjectGeneral Materials Science
dc.subjectInstrumentation
dc.subjectGeneral Engineering
dc.subjectProcess Chemistry and Technology
dc.subjectComputer Science Applications
dc.subjectFluid Flow and Transfer Processes
dc.titleThe Bridge Between Artificial Intelligence and Predictive Maintenance in Industry 4.0en
dc.title.subtitleA Systematic Reviewen
dc.typereview
degois.publication.firstPage1
degois.publication.issue10
degois.publication.lastPage23
degois.publication.titleApplied Sciences (Switzerland)
degois.publication.volume16
dspace.entity.typePublication
rcaap.rightsopenAccess

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