Weigert, Max (2023)
Analysis of diagnostic capabilities for degradation of brushless direct current motors depending on varying simulation data.
4th Asia Pacific Conference of the Prognostics and Health Management. Tokyo, Japan (11.09. – 14.09.2023)
doi: 10.36001/phmap.2023.v4i1.3645
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
As the use of unmanned aerial vehicles (UAVs) becomes more widespread and their missions more complex, the need for safety measures for their technical components is also increasing. Among the components that are critical for the operation of UAVs, Brushless Direct Current (BLDC) motors are particularly important. This is due to their compact design and low number of wear parts, which make them well-suited for use in UAVs. In this work, test rig and simulation data of BLDC motors degradation are utilized to investigate the capabilities and limitations of different machine learning algorithms. For this purpose, suitable features representing the motor behavior are discussed. Classification and regression tasks are applied to analyze both the fault type and the degradation progress. The simulated data allows for an assessment of the influence of noise and degradation progress on the diagnosis performance. Furthermore, characteristics of various fault types and the representation of their degradation process in the simulation are discussed. The database and the derived features are shared publicly.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Weigert, Max |
| Art des Eintrags: | Bibliographie |
| Titel: | Analysis of diagnostic capabilities for degradation of brushless direct current motors depending on varying simulation data |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Ort: | Tokyo |
| Verlag: | PHM Society |
| Reihe: | Proceedings of the Asia Pacific Conference of the PHM Society |
| Band einer Reihe: | 4,1 |
| Kollation: | 8 Seiten |
| Veranstaltungstitel: | 4th Asia Pacific Conference of the Prognostics and Health Management |
| Veranstaltungsort: | Tokyo, Japan |
| Veranstaltungsdatum: | 11.09. – 14.09.2023 |
| DOI: | 10.36001/phmap.2023.v4i1.3645 |
| Kurzbeschreibung (Abstract): | As the use of unmanned aerial vehicles (UAVs) becomes more widespread and their missions more complex, the need for safety measures for their technical components is also increasing. Among the components that are critical for the operation of UAVs, Brushless Direct Current (BLDC) motors are particularly important. This is due to their compact design and low number of wear parts, which make them well-suited for use in UAVs. In this work, test rig and simulation data of BLDC motors degradation are utilized to investigate the capabilities and limitations of different machine learning algorithms. For this purpose, suitable features representing the motor behavior are discussed. Classification and regression tasks are applied to analyze both the fault type and the degradation progress. The simulated data allows for an assessment of the influence of noise and degradation progress on the diagnosis performance. Furthermore, characteristics of various fault types and the representation of their degradation process in the simulation are discussed. The database and the derived features are shared publicly. |
| Zusätzliche Informationen: | Paper-ID: R05-01 |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Fachgebiet für Flugsysteme und Regelungstechnik (FSR) |
| Hinterlegungsdatum: | 25 Apr 2024 07:05 |
| Letzte Änderung: | 25 Apr 2024 07:05 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum