20.500.12556/DiRROS-19674 Reliability improvements for in-wheel motor 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. machine learning models low-cost device electric motor true false true Springer Švica Angleški jezik Ni določen © 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG Neznano 2024-07-23 11:29:35 2024-07-23 11:29:35 2024-07-24 03:48:01 0000-00-00 00:00:00 2024 0 0 Nasl. z nasl. zaslona; Opis vira z dne 22. 7. 2024; 1 spletni vir (1 PDF dokument (197–212 str.)) 0000-00-00 PostprintKoncna Objavljeno NiDoloceno 0000-00-00 0000-00-00 2024-04-22 2026-04-22 62 10.1007/978-3-031-59361-1_8 202396419 202391555 https://link.springer.com/chapter/10.1007/978-3-031-59361-1_8 1 542ff69e-48c5-11ef-b487-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=27170 RAZ_Petelin_Gasper_2024.pdf RAZ_Petelin_Gasper_2024.pdf 1 9F1F599EA21E38556EB03A0CED3A60B9 63f431238ff895ff7f8d2c3c6f08d6dd7121e63590f56fc7073b55e5b07c1863 b3e8bbf9-48c6-11ef-b487-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=27171 Institut Jožef Stefan 0 0 0