A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures

Fatigue life prediction is essential in both the design and operational phases of any aircraft, and in this sense safety in the aerospace industry requires early detection of fatigue cracks to prevent in-flight failures. Robust and precise fatigue life predictors are thus essential to ensure safety....

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Detalles Bibliográficos
Autores: Ladrón, Ángel, Sánchez-Domínguez, Miguel, Rozalén, Javier, Sánchez, Fernando R., Vicente, Javier de, Lacasa, Lucas, Valero, Eusebio, Rubio, Gonzalo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/424581
Acceso en línea:http://hdl.handle.net/10261/424581
http://arxiv.org/abs/2509.10227v3
Access Level:acceso abierto
Palabra clave:Statistical validation
Aerospace certification
Aerospace safety
Fatigue life
Machine learning
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oai_identifier_str oai:digital.csic.es:10261/424581
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
title A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
spellingShingle A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
Ladrón, Ángel
Statistical validation
Aerospace certification
Aerospace safety
Fatigue life
Machine learning
title_short A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
title_full A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
title_fullStr A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
title_full_unstemmed A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
title_sort A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
dc.creator.none.fl_str_mv Ladrón, Ángel
Sánchez-Domínguez, Miguel
Rozalén, Javier
Sánchez, Fernando R.
Vicente, Javier de
Lacasa, Lucas
Valero, Eusebio
Rubio, Gonzalo
author Ladrón, Ángel
author_facet Ladrón, Ángel
Sánchez-Domínguez, Miguel
Rozalén, Javier
Sánchez, Fernando R.
Vicente, Javier de
Lacasa, Lucas
Valero, Eusebio
Rubio, Gonzalo
author_role author
author2 Sánchez-Domínguez, Miguel
Rozalén, Javier
Sánchez, Fernando R.
Vicente, Javier de
Lacasa, Lucas
Valero, Eusebio
Rubio, Gonzalo
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
Agencia Estatal de Investigación (España)
Ministerio de Ciencia, Innovación y Universidades (España)
European Commission
Ladrón, Ángel [0009-0001-9467-2371]
Sánchez-Domínguez, Miguel [0009-0001-2015-2824]
Rubio, Gonzalo [0000-0002-6231-4801]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Statistical validation
Aerospace certification
Aerospace safety
Fatigue life
Machine learning
topic Statistical validation
Aerospace certification
Aerospace safety
Fatigue life
Machine learning
description Fatigue life prediction is essential in both the design and operational phases of any aircraft, and in this sense safety in the aerospace industry requires early detection of fatigue cracks to prevent in-flight failures. Robust and precise fatigue life predictors are thus essential to ensure safety. Traditional engineering methods, while reliable, are time consuming and involve complex workflows, including steps such as conducting several Finite Element Method (FEM) simulations, deriving the expected loading spectrum, and applying cycle counting techniques like peak-valley or rainflow counting. These steps often require collaboration between multiple teams and tools, added to the computational time and effort required to achieve fatigue life predictions. Machine learning (ML) offers a promising complement to traditional fatigue life estimation methods, enabling faster iterations and generalization, providing quick estimates that guide decisions alongside conventional simulations. In this paper, we present a ML-based pipeline that aims to estimate the fatigue life of different aircraft wing locations given the flight parameters of the different missions that the aircraft will be operating throughout its operational life. We validate the pipeline in a realistic use case of fatigue life estimation, yielding accurate predictions alongside a thorough statistical validation and uncertainty quantification. Our pipeline constitutes a complement to traditional methodologies by reducing the amount of costly simulations and, thereby, lowering the required computational and human resources.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/424581
http://arxiv.org/abs/2509.10227v3
url http://hdl.handle.net/10261/424581
http://arxiv.org/abs/2509.10227v3
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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#PLACEHOLDER_PARENT_METADATA_VALUE#
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CEX2021-001164-M
info:eu-repo/grantAgreement/EC/HE/101194363
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PLEC2023-010251
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114324GB-C22
info:eu-repo/grantAgreement/AEI//PID2024-157526NB-I00
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.engfailanal.2025.110334
Ladrón, Ángel; Sánchez-Domínguez, Miguel; Rozalén, Javier; Sánchez, Fernando R.; Vicente, Javier de; Lacasa, Lucas; Valero, Eusebio; Rubio, Gonzalo; 2025; A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures [Preprint]; arXiv; v3; https://doi.org/10.48550/arXiv.2509.10227
https://doi.org/10.1016/j.engfailanal.2025.110334
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dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
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spelling A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft StructuresLadrón, ÁngelSánchez-Domínguez, MiguelRozalén, JavierSánchez, Fernando R.Vicente, Javier deLacasa, LucasValero, EusebioRubio, GonzaloStatistical validationAerospace certificationAerospace safetyFatigue lifeMachine learningFatigue life prediction is essential in both the design and operational phases of any aircraft, and in this sense safety in the aerospace industry requires early detection of fatigue cracks to prevent in-flight failures. Robust and precise fatigue life predictors are thus essential to ensure safety. Traditional engineering methods, while reliable, are time consuming and involve complex workflows, including steps such as conducting several Finite Element Method (FEM) simulations, deriving the expected loading spectrum, and applying cycle counting techniques like peak-valley or rainflow counting. These steps often require collaboration between multiple teams and tools, added to the computational time and effort required to achieve fatigue life predictions. Machine learning (ML) offers a promising complement to traditional fatigue life estimation methods, enabling faster iterations and generalization, providing quick estimates that guide decisions alongside conventional simulations. In this paper, we present a ML-based pipeline that aims to estimate the fatigue life of different aircraft wing locations given the flight parameters of the different missions that the aircraft will be operating throughout its operational life. We validate the pipeline in a realistic use case of fatigue life estimation, yielding accurate predictions alongside a thorough statistical validation and uncertainty quantification. Our pipeline constitutes a complement to traditional methodologies by reducing the amount of costly simulations and, thereby, lowering the required computational and human resources.The authors acknowledge funding from project TIFON (PLEC2023-010251) funded by MCIN/AEI/10.13039/501100011033, Spain. LL acknowledges partial support from projects MISLAND (PID2020-114324GB-C22), project CSxAI (PID2024-157526NB-I00) funded by MICIU/AEI/10.13039/501100011033/FEDER, UE, project MdM Seal of Excellence (CEX2021-001164-M) funded by MICIU/AEI/10.13039/501100011033 and from the European Commission Chips Joint Undertaking project No. 101194363 (NEHIL).With funding from the Spanish government through the "Maria de Maeztu Centre of Excellence" accreditation (CEX2021-001164-M).Peer reviewedElsevierMinisterio de Ciencia e Innovación (España)Agencia Estatal de Investigación (España)Ministerio de Ciencia, Innovación y Universidades (España)European CommissionLadrón, Ángel [0009-0001-9467-2371]Sánchez-Domínguez, Miguel [0009-0001-2015-2824]Rubio, Gonzalo [0000-0002-6231-4801]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202620262026info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/424581http://arxiv.org/abs/2509.10227v3reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CEX2021-001164-Minfo:eu-repo/grantAgreement/EC/HE/101194363info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PLEC2023-010251info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114324GB-C22info:eu-repo/grantAgreement/AEI//PID2024-157526NB-I00The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.engfailanal.2025.110334Ladrón, Ángel; Sánchez-Domínguez, Miguel; Rozalén, Javier; Sánchez, Fernando R.; Vicente, Javier de; Lacasa, Lucas; Valero, Eusebio; Rubio, Gonzalo; 2025; A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures [Preprint]; arXiv; v3; https://doi.org/10.48550/arXiv.2509.10227https://doi.org/10.1016/j.engfailanal.2025.110334Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4245812026-05-22T06:33:51Z
score 15,812429

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