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dc.contributor.authorAlcibar, Jokin
dc.contributor.authorAizpurua, Jose I.
dc.contributor.authorZugasti, Ekhi
dc.contributor.authorPeñagarikano, Oier
dc.date.accessioned2025-03-17T10:12:28Z
dc.date.available2025-03-17T10:12:28Z
dc.date.issued2025
dc.identifier.issn0952-1976en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=180315en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6919
dc.description.abstractMonitoring the health of remote critical infrastructure poses significant challenges due to limited accessibility and harsh operational environments. Inspection drones are ubiquitous assets that enhance the reliability of critical infrastructures through improved accessibility. However, due to the harsh operation environment, it is crucial to monitor their health to ensure successful inspection operations. The battery is a key component that determines the reliability of the inspection drones and, with an appropriate health management approach, contributes to reliable and robust inspections. This paper introduces a novel hybrid probabilistic approach for predicting the end-of-discharge (EOD) voltage of lithium polymer (Li-Po) batteries in inspection drones. The proposed approach integrates Monte Carlo (MC) dropout based Convolutional Neural Networks (CNN) with electrochemistry-based battery discharge model. This integration employs an error-correction configuration that combines electrochemistry-based EOD prediction with probabilistic error correction using CNN with MC dropout. The approach is designed to infer aleatoric and epistemic uncertainty, facilitating robust battery discharge predictions through uncertainty-aware predictions. The proposed approach is empirically evaluated using a dataset comprising EOD voltage measurements under varying load conditions. The dataset, obtained from real inspection drones during offshore wind turbine inspections, underscores the practical applicability of the proposed approach. Comparative analysis with various probabilistic methods, including Quantile Linear Regression, Quantile Regression Forest, and Quantile Gradient Boosting, demonstrates a 14.8% improvement in probabilistic accuracy compared to the best-performing method. Additionally, the estimation of different uncertainties enhances the diagnosis of battery health states, contributing to more reliable inspection operations and highlighting the practical value of the work.en
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2025 Elsevier Ltd.en
dc.subjectConvolutional Neural Networksen
dc.subjectUncertainty quantificationen
dc.subjectPrognostics and health managementen
dc.subjectHybrid health monitoringen
dc.subjectRobustnessen
dc.titleA hybrid probabilistic battery health management approach for robust inspection drone operationsen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.sourceEngineering Applications of Artificial Intelligenceen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1016/j.engappai.2025.110246en
local.embargo.enddate2027-04-30
local.source.detailsVol. 146. N. art. 110246. April, 2025en
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen
oaire.funderNameGobierno Vascoen
oaire.funderNameGobierno Vascoen
oaire.funderNameGobierno Vascoen
oaire.funderIdentifierhttps://ror.org/00pz2fp31 / http://data.crossref.org/fundingdata/funder/10.13039/501100003086en
oaire.funderIdentifierhttps://ror.org/00pz2fp31 / http://data.crossref.org/fundingdata/funder/10.13039/501100003086
oaire.funderIdentifierhttps://ror.org/00pz2fp31 / http://data.crossref.org/fundingdata/funder/10.13039/501100003086
oaire.fundingStreamElkartek 2024en
oaire.fundingStreamIkertalde Convocatoria 2022-2025en
oaire.fundingStreamElkartek 2023en
oaire.awardNumberKK-2024-00030en
oaire.awardNumberIT1676-22en
oaire.awardNumberKK-2023-00042en
oaire.awardTitleMecatrónica cognitiva para el diseño de las maquinas industriales (MECACOGNIT)en
oaire.awardTitleGrupo de sistemas inteligentes para sistemas industriales (IKERTALDE 2022-2025)en
oaire.awardTitleRedes eléctricas altamente resilientes: diseño, control y protección de los activos energéticos para garantizar la robustez, flexibilidad y seguridad de suministro (RESINET)en
oaire.awardURISin informaciónen
oaire.awardURISin informaciónen
oaire.awardURISin informaciónen


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