Lightweight Recognition of Degradation States for the Servo Turret Power Head Based on Dynamic Multi-scale Attention Mechanism

doi:10.3901/JME.260503

Abstract

Abstract: Addressing the limitations of deep learning models in recognizing degradation states of CNC manufacturing equipment, such as high computational requirements, which hinder their deployment on lightweight edge computing devices in industrial settings, this paper proposes a method for degradation state recognition of servo turret power heads based on a dynamic multi-scale attention mechanism and model lightweighting. To address the limited feature extraction ability of existing models, we use convolutional layers with different kernel sizes to capture multi-scale information. Multi-level feature fusion, combining attention mechanisms, dynamic convolutions, and fully connected layers, enhances the model’s robustness and accuracy. To achieve lightweighting, a minimum differentiation-assisted training strategy is introduced, significantly reducing parameter size while maintaining predictive performance. Six sets of experiments were conducted to verify the proposed method using data from the reliability testing system of the servo turret power head. Experimental results show that the proposed method achieves an average accuracy of 99.66% before lightweighting, and can still maintain an accuracy rate of 94.09% after reducing the parameter size by 95.44% (from about 520 000 to about 20 000 parameters). This provides a reference for lightweight monitoring of the degradation status in CNC machine tools.

Key words: degradation state recognition, dynamic multi-scale convolution, attention mechanism, lightweight, servo turret power head

CLC Number:

TH17

XU Weiyang, YANG Zhaojun, LIU Zhifeng, HE Jialong, LI Guofa, CHEN Chuanhai, LIU Yan. Lightweight Recognition of Degradation States for the Servo Turret Power Head Based on Dynamic Multi-scale Attention Mechanism[J]. Journal of Mechanical Engineering, 2026, 62(10): 348-361.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260503

http://www.cjmenet.com.cn/EN/Y2026/V62/I10/348

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