Publications Details

Publication Details

Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools

authored by
Berend Denkena, Marc-André Dittrich, Hendrik Noske, Dirk Lange, Carolin Benjamins, Marius Lindauer

Ball screws are frequently used as drive elements in the feed axes of machine tools. The failure of ball screw drives is associated with high downtimes and costs for manufacturing companies, which harm competitiveness. Data-based monitoring approaches derive the ball screw condition based on sensor data in cases where no knowledge is available to derive a physical model-based approach. An essential criterion for selecting the condition assessment method is the availability of fault data. In the literature, fault patterns are often artificially created in an experimental test bench scenario. This paper presents ball screw drive monitoring approaches for machine tool fleets based on machine learning. First, the potentials of automated machine learning for supervised anomaly detection are investigated. It is shown that the AutoML tool Auto-Sklearn achieves a higher monitoring quality compared to literature approaches. However, fault data are often not available. Therefore, unified outlier scores are applied in a semi-supervised anomaly detection mode. The unified outlier score approach outperforms threshold-based approaches commonly used in industry. The considered data set originates from a machine tool fleet used in series production in the automotive industry collected over 8 months. Within the observation period, multiple ball screw failures are observed so that sensor data about the transient phases between normal and fault conditions is available.

Institute of Artificial Intelligence
Machine Learning Section
Institute of Production Engineering and Machine Tools
External Organisation(s)
The international journal of advanced manufacturing technology
No. of pages
Publication date
Publication status
E-pub ahead of print
Peer reviewed
ASJC Scopus subject areas
Software, Mechanical Engineering, Control and Systems Engineering, Industrial and Manufacturing Engineering, Computer Science Applications
Electronic version(s) (Access: Open)