Data‐based models of drive technology for automation in automotive production. Issue 1 (25th January 2021)

• Record Type: Journal Article
• Authors: Dierkes, Eva; Jung, Francesca; Büskens, Christof
• Other Names: Kuhl D. guestEditor.; Meister A. guestEditor.; Ricoeur A. guestEditor.; Wünsch O. guestEditor.
• Is Part Of: Proceedings in applied mathematics and mechanics. Volume 20:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-25
• Subjects: Applied mathematics -- Periodicals; Engineering mathematics -- Periodicals; Mathematical physics -- Periodicals; 519
• DOI: 10.1002/pamm.202000286
• Languages: English
• ISSNs: 1617-7061

Abstract

Abstract: The digital revolution, especially in the field of manufacturing, has great potential to change the economy sustainably. In this work, the development of methods for predictive maintenance and condition monitoring is a central focus. Data‐based models for drive technology in automotive production are investigated in order to generate adequate models, to make statements about the life cycle of the drives, or to suggest system modifications, such as the adjustment of weights. One task in this field is to detect anomalies or disturbances in given engine data. Since this collected data is variable and noisy, the detection of faults is non‐trivial. In this context two different methods for anomaly detection are studied. First, a model based on statistical analyses and second, a machine learning model is evaluated.