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

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.