Data-based ensemble approach for semi-supervised anomaly detection in machine tool condition monitoring. (November 2021)

• Record Type: Journal Article
• Authors: Denkena, B.; Dittrich, M.-A.; Noske, H.; Stoppel, D.; Lange, D.
• Is Part Of: CIRP journal of manufacturing science and technology. Volume 35(2021)
• Page Start: 795
• Page End: 802
• Publication Date: 2021-11
• Subjects: Condition monitoring -- Machine learning -- Failure -- Ball screw -- Maintenance; Manufacturing processes -- Periodicals; 670.5
• DOI: 10.1016/j.cirpj.2021.09.003
• Languages: English
• ISSNs: 1755-5817

Abstract

Abstract: Data-based methods are capable to monitor machine components. Approaches for semi-supervised anomaly detection are trained using sensor data that describe the normal state of machine components. Thus, such approaches are interesting for industrial practice, since sensor data do not have to be labeled in a time-consuming and costly way. In this work, an ensemble approach for semi-supervised anomaly detection is used to detect anomalies. It is shown that the ensemble approach is suitable for condition monitoring of ball screws. For the evaluation of the approach, a data set of a regular test cycle of a ball screw from automotive industry is used.