| Title: | Reliability improvements for in-wheel motor |
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| Authors: | ID Petelin, Gašper, Institut Jožef Stefan (Author)
ID Hribar, Rok, Institut Jožef Stefan (Author)
ID Ciglarič, Stane (Author)
ID Herman, Jernej (Author)
ID Biasizzo, Anton, Institut Jožef Stefan (Author)
ID Korošec, Peter, Institut Jožef Stefan (Author)
ID Papa, Gregor, Institut Jožef Stefan (Author) |
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| Files: |  URL - Source URL, visit https://link.springer.com/chapter/10.1007/978-3-031-59361-1_8
 PDF - Presentation file. (1,27 MB, This file will be accessible after 22.04.2026)
MD5: 9F1F599EA21E38556EB03A0CED3A60B9
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| Language: | English |
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| Typology: | 1.16 - Independent Scientific Component Part or a Chapter in a Monograph |
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| Organization: |  IJS - Jožef Stefan Institute
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| 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. |
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| Keywords: | machine learning models, low-cost device, electric motor |
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| Publication status: | Published |
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| Publication version: | Author Accepted Manuscript |
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| Publication date: | 22.04.2024 |
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| Publisher: | Springer |
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| Year of publishing: | 2024 |
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| Number of pages: | 1 spletni vir (1 PDF dokument (197–212 str.)) |
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| Source: | Švica |
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| PID: | 20.500.12556/DiRROS-19674  |
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| UDC: | 62 |
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| DOI: | 10.1007/978-3-031-59361-1_8 |
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| COBISS.SI-ID: | 202396419 |
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| Copyright: | © 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 22. 7. 2024;
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| Publication date in DiRROS: | 23.07.2024 |
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| Views: | 270 |
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| Downloads: | 121 |
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