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dc.contributor.authorSerradilla Casado, Oscar
dc.contributor.authorZugasti Uriguen, Ekhi
dc.contributor.authorCernuda García, Carlos
dc.contributor.authorZurutuza Ortega, Urko
dc.contributor.otherAranburu Irastorza, Andoitz
dc.contributor.otherRamirez de Okariz Telleria, Iñigo
dc.date.accessioned2020-11-03T11:00:33Z
dc.date.available2020-11-03T11:00:33Z
dc.date.issued2020
dc.identifier.isbn978-1-7281-6932-3en
dc.identifier.issn1558-4739en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=161567en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/1883
dc.description.abstractThis paper presents the implementation and explanations of a remaining life estimator model based on machine learning, applied to industrial data. Concretely, the model has been applied to a bushings testbed, where fatigue life tests are performed to find more suitable bushing characteristics. Different regressors have been compared Environmental and Operational Condition and setting variables as input data to prognosticate the remaining life on each observation during fatigue tests, where final model is a Random Forest was chosen given its accuracy and explainability potential. The model creation, optimisation and interpretation has been guided combining eXplainable Artificial Intelligence with domain knowledge. Precisely, ELI5 and LIME explainable techniques have been used to perform local and global explanations. These were used to understand the relevance of predictor variables in individual and overall remaining life estimations. The achieved results have been process knowledge gain and expert knowledge validation, assertion of huge potential of data-driven models in industrial processes and highlight the need of collaboration between expert knowledge technicians and eXplainable Artificial Intelligence techniques to understand advanced machine learning models.es
dc.language.isoengen
dc.publisherIEEEen
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectExplainable Artificial Intelligenceen
dc.subjectinterpreten
dc.subjectMachine learningen
dc.subjectdata-driven modelen
dc.subjectRemaining Useful Lifeen
dc.subjectprognosisen
dc.subjectindustrial processen
dc.subjectdomain knowledgeen
dc.titleInterpreting Remaining Useful Life estimations combining Explainable Artificial Intelligence and domain knowledge in industrial machineryen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dcterms.accessRightsinfo:eu-repo/semantics/openAccessen
dcterms.source2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)en
dc.description.versioninfo:eu-repo/semantics/acceptedVersionen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1109/FUZZ48607.2020.9177537en
local.embargo.enddate2022-08-31
local.contributor.otherinstitutionFagor Arrasate, S.Coop.es
local.contributor.otherinstitutionKoniker S.Coop.es
local.source.details2020en


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