Presentation
GAIA: A Generative AI Approach for Enabling Aircraft Digital Twin Creation
DescriptionThe integrity and reliability of Landing Gear System (LGS) is crucial for aircraft safety.
However, the scarcity of real-world fault data hinders the creation of effective Predictive Maintenance (PdM) strategies, especially those relying on modern Machine Learning (ML) techniques.
As a result, this paper presents GAIA: the first Generative Artificial Intelligence (GenAI) approach for enabling the creation of digital twins to support PdM in the aviation domain.
Specifically, by leveraging multi-physics modeling and data-driven techniques, GAIA generates realistic in-distribution faulty samples to augment existing datasets.
As a use case, we consider the LGS and introduce DSLG D/R, a novel dataset specifically designed for LGS fault classification, created in collaboration with omitted due to blind review.
Our results demonstrate a significant 10.56% improvement in fault classification accuracy compared to other data augmentation methods.
To showcase the broader applicability of our method, we also evaluate it on the Electrical Faults dataset, a well-established benchmark for power system fault diagnosis.
Again, GAIA consistently outperforms pure physics-driven and other data augmentation methods, highlighting its versatility across critical safety domains.
The code and dataset will be released upon acceptance.
However, the scarcity of real-world fault data hinders the creation of effective Predictive Maintenance (PdM) strategies, especially those relying on modern Machine Learning (ML) techniques.
As a result, this paper presents GAIA: the first Generative Artificial Intelligence (GenAI) approach for enabling the creation of digital twins to support PdM in the aviation domain.
Specifically, by leveraging multi-physics modeling and data-driven techniques, GAIA generates realistic in-distribution faulty samples to augment existing datasets.
As a use case, we consider the LGS and introduce DSLG D/R, a novel dataset specifically designed for LGS fault classification, created in collaboration with omitted due to blind review.
Our results demonstrate a significant 10.56% improvement in fault classification accuracy compared to other data augmentation methods.
To showcase the broader applicability of our method, we also evaluate it on the Electrical Faults dataset, a well-established benchmark for power system fault diagnosis.
Again, GAIA consistently outperforms pure physics-driven and other data augmentation methods, highlighting its versatility across critical safety domains.
The code and dataset will be released upon acceptance.
Event Type
Networking
Work-in-Progress Poster
TimeSunday, June 226:00pm - 7:00pm PDT
LocationLevel 3 Lobby