Margarita Antoniou

Journal articlesshow recent

δ-perturbation of bilevel optimization problems: An error bound analysis, Oper. Res. Perspect. (2024)
Differential Evolution with Estimation of Distribution for Worst-case Scenario Optimization, MATHEMATICS (2021)
Multi-objective approaches to ground station scheduling for optimization of communication with satellites, OPTIM ENG (2021)

Conference papersshow all

A Study on Optimistic and Pessimistic Pareto-Fronts in Multiobjective Bilevel Optimization via δ-Perturbation, EMO 2025
An evolutionary approach to pessimistic bilevel optimization problems, BILEVEL 2023

Books & Chaptersshow all

A Framework for Applying Data-Driven AI/ML Models in Reliability, Recent Advances in Microelectronics Reliability, Springer (2024)

On Suitability of the Customized Measuring Device for Electric Motor

R. Hribar, G. Petelin, M. Antoniou, A. Biasizzo, S. Ciglarič, G. Papa
Proc. 48th Annual Conference of the IEEE Industrial Electronics Society
Brussels, Belgium: 17-20 October 2022
pages: 1-6
DOI: 10.1109/IECON49645.2022.9968876

BibTex:
@INPROCEEDINGS{HribarEtAlIECON2022,
author={Hribar, Rok and Petelin, Gašper and Antoniou, Margarita and Biasizzo, Anton and Ciglarič, Stanko and Papa, Gregor},
booktitle={IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society},
title={On Suitability of the Customized Measuring Device for Electric Motor},
year={2022},
volume={},
number={},
pages={1-6},
doi={10.1109/IECON49645.2022.9968876}
}

abstract:
Setting up a reliable electric propulsion system in the automotive domain calls for a smart condition monitoring device that is able to reliably assess the state and the health of the electric motor. To allow massive integration of such monitoring devices, it is required of them to be low-cost and miniature. Those requirements pose limitations on their accuracy, however, we show in this paper that those limitations can be significantly reduced by suitably processing the sensor data. We used machine learning models (random forest and XGBoost) to transform very noisy measurements of motor winding insulation resistance measured by a low-cost device to the much more reliable value with which we are able to compete with measurements made by the state-of-the-art high-priced measuring system. The proposed methodology represents a crucial building block in future smart condition monitoring system and enables low-cost and accurate assessment of electric motor health connected to the state of its winding insulation.