Elling, Magnus von ; Weber, Markus ; Berchtenbreiter, Viktor ; Weigold, Matthias
Hrsg.: Bauernhansl, Thomas ; Verl, Alexander ; Liewald, Mathias ; Möhring, Hans-Christian (2024)
Model-based spindle bearing monitoring using vibration sensors and artificial neural networks.
13th Congress of the German Academic Association for Production Technology (WGP). Freudenstadt (20.11.2023-23.11.2023)
doi: 10.1007/978-3-031-47394-4_25
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
To ensure the longevity of the bearings of a motor spindle, it is advantageous to know the precise loads on the bearings during operation. Since sensor-based monitoring involves a great deal of effort due to the limited space available, and simulating the bearing load is not real-time capable, we investigated how the bearing loads can be estimated using machine learning methods. To estimate the bearing load, a co-simulation was first set up that generates large amounts of training data based on measured cutting forces and spindle vibration velocities. Measured and simulated quantities are then used to train artificial neural networks. The best-performing neural networks can estimate the surface pressure between rolling elements and bearing rings with an error of less than 2%. This deviation refers to the contact stress that was calculated for comparison with the simulation results.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Herausgeber: | Bauernhansl, Thomas ; Verl, Alexander ; Liewald, Mathias ; Möhring, Hans-Christian |
| Autor(en): | Elling, Magnus von ; Weber, Markus ; Berchtenbreiter, Viktor ; Weigold, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | Model-based spindle bearing monitoring using vibration sensors and artificial neural networks |
| Sprache: | Englisch |
| Publikationsjahr: | 2024 |
| Ort: | Cham |
| Verlag: | Springer |
| Buchtitel: | Production at the leading edge of technology : proceedings of the 13th Congress of the German Academic Association for Production Technology (WGP) |
| Reihe: | Lecture Notes in Production Engineering |
| Veranstaltungstitel: | 13th Congress of the German Academic Association for Production Technology (WGP) |
| Veranstaltungsort: | Freudenstadt |
| Veranstaltungsdatum: | 20.11.2023-23.11.2023 |
| DOI: | 10.1007/978-3-031-47394-4_25 |
| URL / URN: | https://link.springer.com/chapter/10.1007/978-3-031-47394-4_... |
| Kurzbeschreibung (Abstract): | To ensure the longevity of the bearings of a motor spindle, it is advantageous to know the precise loads on the bearings during operation. Since sensor-based monitoring involves a great deal of effort due to the limited space available, and simulating the bearing load is not real-time capable, we investigated how the bearing loads can be estimated using machine learning methods. To estimate the bearing load, a co-simulation was first set up that generates large amounts of training data based on measured cutting forces and spindle vibration velocities. Measured and simulated quantities are then used to train artificial neural networks. The best-performing neural networks can estimate the surface pressure between rolling elements and bearing rings with an error of less than 2%. This deviation refers to the contact stress that was calculated for comparison with the simulation results. |
| Freie Schlagworte: | condition monitoring, machine learning, motor spindle |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > TEC Fertigungstechnologie |
| Hinterlegungsdatum: | 15 Jan 2024 09:54 |
| Letzte Änderung: | 23 Aug 2024 11:45 |
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