Surface pitting detection of ball screw based on deep learning

Yonghe Wei; Weiguang Li

doi:10.1117/12.3025527

28 February 2024 Surface pitting detection of ball screw based on deep learning

Yonghe Wei, Weiguang Li

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130713B (2024) https://doi.org/10.1117/12.3025527
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

In view of the fact that most of the existing research on the pitting failure of ball screw focuses on the vibration signal experiment and the establishment of the degradation model, and the use of more intuitive visual aspects is less, the deep learning is studied in the pitting detection of the surface of the ball screw. The application of Faster R-CNN and Mask R-CNN two network models were built, and the two were compared and analyzed through experiments. The results show that both Faster R-CNN and Mask R-CNN can guarantee high classification accuracy under different learning rates, and both can excellently complete the detection task of pitting on the surface of the ball screw. While locating the eclipse, the mask of the pitting is output synchronously, which is helpful in the face of the subsequent task of describing the size of the pitting, and has more advantages in the face of small-area pitting, and there are fewer missed detections.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yonghe Wei and Weiguang Li "Surface pitting detection of ball screw based on deep learning", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130713B (28 February 2024); https://doi.org/10.1117/12.3025527

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