INTELLIGENT PREDICTIVE MAINTENANCE FOR UAVs: A DIGITAL TWIN-BASED APPROACH

Descrizione

INTELLIGENT PREDICTIVE MAINTENANCE FOR UAVs: A DIGITAL TWIN-BASED APPROACH Sintesi in italiano (traduzione automatica): L'organizzazione internazionale offre un progetto di dottorato in Francia focalizzato sulla manutenzione predittiva per i veicoli aerei senza pilota (UAV). Il candidato svilupperà un framework innovativo basato su un Digital Twin, integrando dati in tempo reale da sensori e tecniche avanzate di intelligenza artificiale e deep learning. Le mansioni principali includono il monitoraggio continuo della salute dei sistemi di propulsione degli UAV, la rilevazione precoce di anomalie e l'ottimizzazione delle decisioni di manutenzione. È richiesta una laurea in ingegneria aerospaziale o un campo correlato. Il progetto prevede anche la validazione sperimentale delle metodologie proposte su banchi di prova e piattaforme UAV reali. Unmanned Aerial Vehicles (UAVs) have experienced rapid growth across a wide range of application domains, including infrastructure inspection, precision agriculture, logistics, civil security, and defense. This widespread adoption is accompanied by increasingly stringent requirements in terms of safety, availability, and reliability, in compliance with European regulations such as the Specific Operations Risk Assessment (SORA) framework. In this context, maintenance has become a major challenge, as critical UAV components—particularly propulsion systems—are subject to aging and degradation processes that may lead to costly failures or even mission loss. Current maintenance strategies primarily rely on scheduled inspections or post-flight analysis of recorded flight data. These approaches do not enable effective anticipation of failures nor optimization of maintenance operations. The objective of this PhD project is to develop an innovative predictive maintenance framework based on the integration of a Digital Twin continuously updated in real time using data collected from onboard sensors and the flight controller, combined with advanced Artificial Intelligence (AI) and Deep Learning techniques. This framework will enable continuous health monitoring of UAV propulsion systems, early anomaly detection, estimation of the Remaining Useful Life (RUL) of critical components, and optimization of maintenance decision-making. The proposed methods will be experimentally validated using instrumented propulsion test benches and experimental campaigns on real UAV platforms in order to demonstrate their robustness, accuracy, and potential for industrial deployment. Funding category: Contrat doctoral 50% ESTACA 50% MIS PHD Country: France Annuncio in inglese. Fonte: Euraxess (Commissione europea).