A New Framework for Identifying the Wear States of Ball Screws Based on Surface Profile Characterization and Machine Learning

Wear inevitably occurs in ball screw assemblies after long-term operation, leading to a decline in transmission performance and machining accuracy. Therefore, the accurate identification of wear states is crucial. In this study, we propose a wear state identification method based on the surface profile of the ball screw. This method effectively overcomes the limitations of traditional experimental approaches that require frequent disassembly of the ball screw or rely on vibration and current signals, which are prone to external interference. Surface profile data covering the entire service life of the screw were obtained through performance degradation experiments. A hybrid feature set was constructed by extracting parameters such as roughness, peak-to-valley height, root mean square, recurrence rate, and fractal characteristics, and classification was performed using a genetic-algorithm-optimized support vector machine (GA-SVM). The experimental results demonstrate that the proposed method can accurately characterize wear evolution, achieving an average identification accuracy of 98.48% while maintaining robustness and effectively avoiding interference from extraneous signals.