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

verfasst von: Berend Denkena, Marc-André Dittrich, Hendrik Noske, Dirk Lange, Carolin Benjamins, Marius Lindauer
Abstract: 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.
Organisationseinheit(en): Institut für Fertigungstechnik und Werkzeugmaschinen
Institut für Künstliche Intelligenz
Externe Organisation(en): MARPOSS Monitoring Solutions GmbH
Typ: Artikel
Journal: The international journal of advanced manufacturing technology
Band: 127
Seiten: 1143-1164
Anzahl der Seiten: 22
ISSN: 0268-3768
Publikationsdatum: 07.2023
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Software, Maschinenbau, Steuerungs- und Systemtechnik, Wirtschaftsingenieurwesen und Fertigungstechnik, Angewandte Informatik
Elektronische Version(en): https://doi.org/10.1007/s00170-023-11524-9 (Zugang: Offen)

BibTeX

@article{c12e213e177f459880a80154165b2504,
title = "Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools",
abstract = "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.",
keywords = "Condition Monitoring, Machine Learning, Ball Screw, Failure, Condition monitoring, Machine learning, Ball screw",
author = "Berend Denkena and Marc-Andr{\'e} Dittrich and Hendrik Noske and Dirk Lange and Carolin Benjamins and Marius Lindauer",
note = "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.",
year = "2023",
month = jul,
doi = "10.1007/s00170-023-11524-9",
language = "English",
volume = "127",
pages = "1143--1164",
journal = "The international journal of advanced manufacturing technology",
issn = "0268-3768",
publisher = "Springer London",
number = "3-4",
}

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

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