Rok Hribar

Journal articlesshow all

Advanced computing to support urban climate neutrality, Energy Sustain. Soc. (2025)
Models for forecasting the traffic flow within the city of Ljubljana, ETRE (2023)

Conference papersshow all

Books & Chaptersshow all

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

Reliability Improvements for In-Wheel Motor

G. Petelin, R. Hribar, S. Ciglarič, J.Herman, A. Biasizzo, P. Korošec, G. Papa
DOI: 10.1007/978-3-031-59361-1_8

BibTex:
@Inbook{Petelin2024, author="Petelin, Ga{\v{s}}per and Hribar, Rok and Ciglari{\v{c}}, Stanko and Herman, Jernej and Biasizzo, Anton and Koro{\v{s}}ec, Peter and Papa, Gregor", editor="van Driel, Willem Dirk and Pressel, Klaus and Soyturk, Mujdat", title="Reliability Improvements for In-Wheel Motor", bookTitle="Recent Advances in Microelectronics Reliability: Contributions from the European ECSEL JU project iRel40", year="2024", publisher="Springer International Publishing", address="Cham", pages="197--212", isbn="978-3-031-59361-1", doi="10.1007/978-3-031-59361-1_8", url="https://doi.org/10.1007/978-3-031-59361-1_8" }

abstract:
Setting up a reliable electric propulsion system in the automotive sector requires an intelligent condition monitoring device capable of reliably assessing the state and the health of the electric motor. To allow for a massive integration of such monitoring devices, they must be inexpensive and small. These requirements limit their accuracy. However, we show in this chapter that these limitations can be significantly reduced by appropriate processing of the sensor data. We have used machine learning models (random forest and XGBoost) to transform very noisy motor winding insulation resistance measurements made by a low-cost device into a much more reliable value that can compete with measurements made by a high-priced state-of-the-art measurement system. The proposed method is an important building block for a future smart condition monitoring system and enables a cost-effective and accurate assessment of the condition of electric motor health in connection with the condition of their winding insulation.