@ARTICLE{Wu_Qin_A_Early,
 author={Wu, Qin and Li, Jianxiong and Wang, Xinglian},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e154733},
 howpublished={online},
 year={Early Access},
 abstract={In response to the challenge of identifying fault types in ball screws of CNC machine tools, particularly under complex operating conditions where classification accuracy is often low, we propose a convolutional neural network fault diagnosis model that incorporates multi-scale convolution and an attention mechanism (MSCAM). First, we collect fault data corresponding to various fault types of the ball screw and establish a comprehensive fault dataset. Next, we apply the S-transform to the original data to generate time-frequency diagrams, which serve as input for the two-dimensional neural network. In this paper, we present a multi-scale convolutional layer integrated with an attention mechanism, designed to highlight key features in fault information and extract more comprehensive characteristics. Ultimately, the model's superior recognition and classification capabilities are validated through experimental datasets, and its robustness is thoroughly analyzed.},
 type={Article},
 title={A Convolutional Neural Network Based on MSCAM for Intelligent Diagnosis of Ball Screws},
 URL={http://ochroma.man.poznan.pl/Content/135266/PDF-MASTER/BPASTS-05035-EA.pdf},
 doi={10.24425/bpasts.2025.154733},
 keywords={complex working conditions, ball screw, S transform, convolutional neural network, attention mechanism},
}