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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorSerradilla, Oscar
dc.contributor.authorZugasti, Ekhi
dc.contributor.authorZurutuza, Urko
dc.contributor.otherRamirez de Okariz, Julian
dc.contributor.otherRodríguez, Jon
dc.date.accessioned2021-09-02T09:14:52Z
dc.date.available2021-09-02T09:14:52Z
dc.date.issued2021
dc.identifier.issn2076-3417en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=164497en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/5357
dc.description.abstractPredictive maintenance (PdM) has the potential to reduce industrial costs by anticipating failures and extending the work life of components. Nowadays, factories are monitoring their assets and most collected data belong to correct working conditions. Thereby, semi-supervised data-driven models are relevant to enable PdM application by learning from assets’ data. However, their main challenges for application in industry are achieving high accuracy on anomaly detection, diagnosis of novel failures, and adaptability to changing environmental and operational conditions (EOC). This article aims to tackle these challenges, experimenting with algorithms in press machine data of a production line. Initially, state-of-the-art and classic data-driven anomaly detection model performance is compared, including 2D autoencoder, null-space, principal component analysis (PCA), one-class support vector machines (OC-SVM), and extreme learning machine (ELM) algorithms. Then, diagnosis tools are developed supported on autoencoder’s latent space feature vector, including clustering and projection algorithms to cluster data of synthetic failure types semi-supervised. In addition, explainable artificial intelligence techniques have enabled to track the autoencoder’s loss with input data to detect anomalous signals. Finally, transfer learning is applied to adapt autoencoders to changing EOC data of the same process. The data-driven techniques used in this work can be adapted to address other industrial use cases, helping stakeholders gain trust and thus promote the adoption of data-driven PdM systems in smart factories.en
dc.description.sponsorshipDiputación Foral de Gipuzkoaes
dc.description.sponsorshipComisión Europeaes
dc.language.isoengen
dc.publisherMDPIen
dc.rights© 2021 by the authors. Licensee MDPIen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectfault detectionen
dc.subjectdiagnosisen
dc.subjectpredictive maintenanceen
dc.subjectdeep learningen
dc.subjectautoencoderen
dc.subjectExplainable Artificial Intelligenceen
dc.subjecttransfer learningen
dc.subjectsemi-superviseden
dc.subjectpress machineen
dc.subjectIndustry 4.0en
dc.titleAdaptable and Explainable Predictive Maintenance: Semi-Supervised Deep Learning for Anomaly Detection and Diagnosis in Press Machine Dataen
dc.typeinfo:eu-repo/semantics/articleen
dcterms.accessRightsinfo:eu-repo/semantics/openAccessen
dcterms.sourceApplied Sciencesen
dc.description.versioninfo:eu-repo/semantics/publishedVersionen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.3390/app11167376en
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/825030/EU/Digital Reality in Zero Defect Manufacturing/QU4LITYen
local.relation.projectIDDFG/Programa de Red Guipuzcoana de Ciencia, Tecnología e Innovación 2020/OF-326-2020/GIP/Hacia una metodologia que guíe a la industria al mantenimiento predictivo y explicativo/MEANERen
local.rights.publicationfeeAPCen
local.rights.publicationfeeamount1845 EUR 2000 CHFen
local.contributor.otherinstitutionKoniker, S. Coop.es
local.source.detailsVol. 11. N. 16. N. artículo 7376, 2021en


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International