TY - JOUR

T1 - Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools

AU - Denkena, Berend

AU - Dittrich, Marc-André

AU - Noske, Hendrik

AU - Lange, Dirk

AU - Benjamins, Carolin

AU - Lindauer, Marius

N1 - Funding Information: Open Access funding enabled and organized by Projekt DEAL. Funded by the Lower Saxony Ministry of Science and Culture under grant number ZN3489 within the Lower Saxony “Vorab” of the Volkswagen Foundation, Germany, supported by the Center for Digital Innovations (ZDIN), Germany. Carolin Benjamins and Marius Lindauer acknowledge support by European Union under the ERC Starting Grant “ixAutoML” (grant no. 101041029). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the ERC. Neither the European Union nor the ERC can be held responsible for them.

PY - 2023/7

Y1 - 2023/7

N2 - 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.

AB - 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.

KW - Condition Monitoring

KW - Machine Learning

KW - Ball Screw

KW - Failure

KW - Condition monitoring

KW - Machine learning

KW - Ball screw

UR - http://www.scopus.com/inward/record.url?scp=85160063119&partnerID=8YFLogxK

U2 - 10.1007/s00170-023-11524-9

DO - 10.1007/s00170-023-11524-9

M3 - Article

VL - 127

SP - 1143

EP - 1164

JO - The international journal of advanced manufacturing technology

JF - The international journal of advanced manufacturing technology

SN - 0268-3768

IS - 3-4

ER -