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Towards a probabilistic error correction approach for improved drone battery health.pdf (2.679Mb)
ESREL_PRESENTATION.pdf (6.456Mb)
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Title
Towards a probabilistic error correction approach for improved drone battery health assessment
Author
Alcibar, Jokin
Aizpurua Unanue, Jose Ignacio
Zugasti, Ekhi
Author (from another institution)
Alonso Montes, Carmen
Diez, Ibon
Research Group
Almacenamiento de energía
Análisis de datos y ciberseguridad
Other institutions
Ikerbasque
Alerion Technologies
Version
Postprint
Rights
© 2023 ESREL2023 Organizers
Access
Embargoed access
URI
https://hdl.handle.net/20.500.11984/6770
Publisher’s version
https://doi.org/10.3850/978-981-18-8071-1_P179-cd
Published at
European Safety and Reliability Conference (ESREL)  33 : 2023 : Southampton. 3-7 September, 2023
Publisher
Research Publishing, Singapore
Keywords
Health management
Batteries
Subject (UNESCO Thesaurus)
Health statistics
Abstract
Health monitoring of remote critical infrastructure, such as offshore wind turbines, is complex and expensive. For the offshore energy sector, the accessibility for on-site asset inspection is hamper ... [+]
Health monitoring of remote critical infrastructure, such as offshore wind turbines, is complex and expensive. For the offshore energy sector, the accessibility for on-site asset inspection is hampered due to their harsh and remote location. In this context, inspection drones are crucial assets. They can perform multiple tasks, which are benefitial for the industry and society, including the improved reliability of critical and remote infrastructure. However, the reliability and safety assurance of inspection drones is complex, as they are autonomous systems and they require incorporating run-time operation and degradation knowledge. Focusing on the health assessment of inspection drones, their battery is a key component, which is a single point of failure and determines the probability of a successful operation. In this context, this paper presents a novel concept for inspection drone battery health assessment through a probabilistic hybrid approach which combines physics-based battery discharge models with data-driven error forecasting strategies. Results are validated with real data obtained through different offshore wind inspection flights of drones. [-]
Funder
Gobierno Vasco
Gobierno Vasco
Gobierno Vasco
Program
Elkartek 2022
Ikertalde Convocatoria 2022-2023
Ikertalde Convocatoria 2022-2025
Number
KK-2022-00106
IT1451-22
IT1676-22
Award URI
Sin información
Sin información
Sin información
Project
Mecatrónica ultraprecisa, fiable y coordinada para la industria 4.0 (MECAPRES)
Teoría de la Señal y Comunicaciones. IKERTALDE 2022-2023
Grupo de sistemas inteligentes para sistemas industriales. IKERTALDE 2022-2025
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