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Generative Digital Twin Framework for Reliable and Robust AI-Powered Prognostic Systems

Fulltext:


Authors:

Zafer Yigit, Håkan Forsberg, Masoud Daneshtalab

Research group:

Heterogeneous systems - hardware software co-design

Publication Type:

Conference/Workshop Paper

Venue:

30th Ada-Europe International Conference on Reliable Software Technologies

Abstract

Reliable operation of heavy-duty construction equipment is essential for maintaining productivity and minimizing maintenance costs. However, developing robust Prognostics and Health Management (PHM) systems is challenging due to limited labeled failure data. This paper investigates an engine-focused digital twin framework that combines machine learning, physics-informed modeling, and synthetic data generation. The approach leverages a digital twin to generate physically consistent datasets using control-driven inputs and physics-based system responses. Previous work demonstrated the feasibility of data-driven PHM on real engine data, but limited data coverage restricts generalization. The proposed framework enables scalable generation of diverse operating conditions. Early results indicate improved robustness and prediction stability compared to purely data-driven models.

Bibtex

@inproceedings{Yigit7399,
author = {Zafer Yigit and H{\aa}kan Forsberg and Masoud Daneshtalab},
title = {Generative Digital Twin Framework for Reliable and Robust AI-Powered Prognostic Systems},
month = {June},
year = {2026},
booktitle = {30th Ada-Europe International Conference on Reliable Software Technologies},
url = {http://www.es.mdu.se/publications/7399-}
}