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A Sensor Reduced Machine Learning Approach for Condition-based Energy Monitoring for Machine Tools

Sossenheimer, Johannes and Walther, Jessica and Fleddermann, Jan and 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), Elsevier B.V., pp. 570-575, 81, DOI: 10.1016/j.procir.2019.03.157,
[Article]

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.

Item Type: Article
Erschienen: 2019
Creators: Sossenheimer, Johannes and Walther, Jessica and Fleddermann, Jan and Abele, Eberhard
Title: A Sensor Reduced Machine Learning Approach for Condition-based Energy Monitoring for Machine Tools
Language: English
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.

Journal or Publication Title: Procedia CIRP, 52nd CIRP Conference on Manufacturing Systems, Ljubljana (Slovenia)
Volume: 81
Publisher: Elsevier B.V.
Uncontrolled Keywords: condition monitoring;energy monitoring;energy transparency;shop floor data
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Institute of Production Management, Technology and Machine Tools (PTW)
16 Department of Mechanical Engineering > Institute of Production Management, Technology and Machine Tools (PTW) > ETA Energy Technologies and Applications in Production
Date Deposited: 27 Aug 2019 05:31
DOI: 10.1016/j.procir.2019.03.157
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