New Conference Paper Published

We’re pleased to announce a new conference contribution presented at the 2025 IEEE 34th International Symposium on Industrial Electronics (ISIE):

“GAIA: A Comprehensive Pipeline for Enabling Aircraft Digital Twin Creation” by Francesco Biondani, Luigi Capogrosso, Uzair Khan, Nicola Dall’Ora, Enrico Fraccaroli, Domenico Fabio Migliore, Francesco Acerra, Marco Cristani, and Franco Fummi.

Abstract

The integrity and reliability of Landing Gear Systems (LGSs) are crucial for aircraft safety. However, the scarcity of real-world fault data hinders the creation of effective Predictive Maintenance (PdM) strategies, especially those that rely on modern Machine Learning (ML) techniques. As a result, this article presents GAIA: the first comprehensive pipeline that enables the creation of digital twins to support PdM in the aviation domain. Specifically, combining multi-physics modeling and data-driven techniques, GAIA allows learning models to achieve superior performance in fault detection and diagnosis tasks. As a use case, we consider a LGS system, and introduce DSLG D/R, a novel dataset specifically designed for LGS fault classification, created in collaboration with Leonardo S.p.A. Our results demonstrate a significant 10.56% improvement in fault classification accuracy compared to other data augmentation methods. To further demonstrate the applicability of our method, we also evaluated it on the Electrical Fault dataset, a well-established benchmark for the diagnosis of power system faults, highlighting the GAIA versatility across different critical safety domains. The code and dataset are available at https://github.com/esd-univr/GAIA.

Details

• Title: GAIA: A Comprehensive Pipeline for Enabling Aircraft Digital Twin Creation
• Authors: Francesco Biondani, Luigi Capogrosso, Uzair Khan, Nicola Dall’Ora, Enrico Fraccaroli, Domenico Fabio Migliore, Francesco Acerra, Marco Cristani, Franco Fummi
• Conference: 2025 IEEE 34th International Symposium on Industrial Electronics (ISIE)
• Year: 2025
• Pages: 1-8

Links

• Code & Dataset: GAIA on GitHub
• DOI: 10.1109/ISIE62713.2025.11124604

This work introduces GAIA and demonstrates its value for creating digital twins to support predictive maintenance in aviation. We thank all collaborators and contributors for their effort.