Work

Predictive Maintenance of Ball Screws: A Comparative Study Using Real-World Industrial Data

Year: 2025

Type: article

Abstract: Ball screws are widely used in machine tool feed drives. With increasing degradation of the ball screws, machining accuracy and economic efficiency decrease. Past investigations have shown that condition monitoring models can predict this degradation. However, these models are typically trained and evaluated using datasets derived from test benches, questioning their applicability to real machine tools. In this article, a comparative evaluation of a selection of these condition monitoring models using an industrial dataset is described. This dataset consists of measurement data from a total of nine ball screws used in three machine tools until failure. It was shown that when using data of multiple ball screws or machines, artificial neural networks or automated machine learning methods achieve a higher accuracy than statistical methods. However, for smaller datasets, statistical methods perform almost as well. The results provide an insight into the industrial applicability of the evaluated condition monitoring models.

Source: Procedia CIRP

DOI: 10.1016/j.procir.2025.02.135

Authors: Daniel Piendl, Maximilian-Peter Radtke, Hendrik Jacobsen, Jürgen Bock, Michael F. Zaeh

Institutions: Technical University of Munich, Technische Hochschule Ingolstadt

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funders: Bayerisches Staatsministerium für Wirtschaft, Infrastruktur, Verkehr und Technologie, Bayerische Staatsministerium für Wirtschaft, Landesentwicklung und Energie

Award: G8566708203