Sossenheimer, Johannes ; Walther, Jessica ; Fleddermann, Jan ; Abele, Eberhard (2019)
A Sensor Reduced Machine Learning Approach for Condition-based Energy Monitoring for Machine Tools.
In: Procedia CIRP, 52nd CIRP Conference on Manufacturing Systems, Ljubljana (Slovenia), 81
doi: 10.1016/j.procir.2019.03.157
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
With the ongoing digitalization of industrial production, innovative ways of creating energy transparency on the shop floor are emerging. This paper presents a sensor reduced approach to enable condition-based energy monitoring for different degrees of machine data availability. It differentiates between scenarios in which a wide range of machine data can be accessed and thus, machine learning approaches can be applied, and others in which only basic process information can be correlated to data from mobile power measurements. The presented approach is deployed and discussed for an EMAG machine tool in the ETA research factory at the Technische Universität Darmstadt.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Sossenheimer, Johannes ; Walther, Jessica ; Fleddermann, Jan ; Abele, Eberhard |
| Art des Eintrags: | Bibliographie |
| Titel: | A Sensor Reduced Machine Learning Approach for Condition-based Energy Monitoring for Machine Tools |
| Sprache: | Englisch |
| Publikationsjahr: | 2019 |
| Verlag: | Elsevier B.V. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia CIRP, 52nd CIRP Conference on Manufacturing Systems, Ljubljana (Slovenia) |
| Jahrgang/Volume einer Zeitschrift: | 81 |
| DOI: | 10.1016/j.procir.2019.03.157 |
| Kurzbeschreibung (Abstract): | With the ongoing digitalization of industrial production, innovative ways of creating energy transparency on the shop floor are emerging. This paper presents a sensor reduced approach to enable condition-based energy monitoring for different degrees of machine data availability. It differentiates between scenarios in which a wide range of machine data can be accessed and thus, machine learning approaches can be applied, and others in which only basic process information can be correlated to data from mobile power measurements. The presented approach is deployed and discussed for an EMAG machine tool in the ETA research factory at the Technische Universität Darmstadt. |
| Freie Schlagworte: | condition monitoring;energy monitoring;energy transparency;shop floor data |
| 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) > ETA Energietechnologien und Anwendungen in der Produktion |
| Hinterlegungsdatum: | 27 Aug 2019 05:31 |
| Letzte Änderung: | 27 Aug 2019 05:31 |
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