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    Damage discrimination on drone fuselage with OBR Based Fibre-Optic networks

    This paper presents a methodology for a structural health monitoring (SHM) system based on optical backscatter reflectometry (OBR) fibre optic networks. The study focuses on the use of machine learning methods to monitor the microdeformation of a drone fuselage and to detect and discriminate be-tween different types of damage when the structure is subjected to static loads. A total of 1500 points are measured for each load, providing a comprehensive understanding of the structural response. In addition, four types of damage, in-cluding partial disbonding at different locations, are induced with varying de-grees of extension to evaluate the sensitivity and accuracy of the SHM system. The results show that the OBR-based SHM system is capable of detecting and localising damage with high accuracy, indicating its potential as a reliable and effective tool for real-time structural health monitoring in aerospace applica-tions. The methodology presented in this study could be extended to other types of structures and damage scenarios, contributing to the development of more advanced and effective SHM systems. ; Postprint (published version)

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    Damage discrimination on drone fuselage with OBR Based Fibre-Optic networks

    This paper presents a methodology for a structural health monitoring (SHM) system based on optical backscatter reflectometry (OBR) fibre optic networks. The study focuses on the use of machine learning methods to monitor the microdeformation of a drone fuselage and to detect and discriminate be-tween different types of damage when the structure is subjected to static loads. A total of 1500 points are measured for each load, providing a comprehensive understanding of the structural response. In addition, four types of damage, in-cluding partial disbonding at different locations, are induced with varying de-grees of extension to evaluate the sensitivity and accuracy of the SHM system. The results show that the OBR-based SHM system is capable of detecting and localising damage with high accuracy, indicating its potential as a reliable and effective tool for real-time structural health monitoring in aerospace applica-tions. The methodology presented in this study could be extended to other types of structures and damage scenarios, contributing to the development of more advanced and effective SHM systems. ; Postprint (published version)

    Access

    Download

    Damage discrimination on drone fuselage with OBR Based Fibre-Optic networks

    This paper presents a methodology for a structural health monitoring (SHM) system based on optical backscatter reflectometry (OBR) fibre optic networks. The study focuses on the use of machine learning methods to monitor the microdeformation of a drone fuselage and to detect and discriminate be-tween different types of damage when the structure is subjected to static loads. A total of 1500 points are measured for each load, providing a comprehensive understanding of the structural response. In addition, four types of damage, in-cluding partial disbonding at different locations, are induced with varying de-grees of extension to evaluate the sensitivity and accuracy of the SHM system. The results show that the OBR-based SHM system is capable of detecting and localising damage with high accuracy, indicating its potential as a reliable and effective tool for real-time structural health monitoring in aerospace applica-tions. The methodology presented in this study could be extended to other types of structures and damage scenarios, contributing to the development of more advanced and effective SHM systems. ; Postprint (published version)

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    Title:

    Damage discrimination on drone fuselage with OBR Based Fibre-Optic networks

    Contributors:

    Mujica Delgado, Luis Eduardo (author) / Ruiz Ordóñez, Magda (author) / Fernández López, Antonio (author) / Universitat Politècnica de Catalunya. Departament de Matemàtiques / Universitat Politècnica de Catalunya. CoDAlab - Control, Dades i Intel·ligència Artificial

    Publication date:

    2024-01-01

    DOI:

    https://doi.org/10.58286/29566

    Type of media:

    Conference paper

    Type of material:

    Electronic Resource

    Language:

    English

    Keywords:

    Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària , Machine Learning , OBR sensor network , Unmanned Aerial Vehicle , Drone

    Classification:

    DDC:  620 / 624

    Licence:

    http://creativecommons.org/licenses/by-nc-nd/4.0/

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