A lightweight transformer based on feature fusion and global-local parallel stacked self-activation unit for bearing fault diagnosis

被引:6
|
作者
Hou, Yandong [1 ]
Li, Tianzhi [1 ]
Wang, Jinjin [1 ]
Ma, Jiulong [1 ]
Chen, Zhengquan [2 ]
机构
[1] Henan Univ, Sch Artificial Intelligence, Zhengzhou 450046, Peoples R China
[2] Henan Univ, Sch Comp & Informat Engn, Kaifeng 475004, Peoples R China
基金
中国国家自然科学基金;
关键词
Transformer; Fault diagnosis; Lightweight network; Feature fusion; Rolling bearing;
D O I
10.1016/j.measurement.2024.115068
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Due to the complex environment and limited hardware resources in the industrial practice diagnosis tasks, deploying deep learning -based models with large parameters is challenging. A novel lightweight bearing fault diagnosis method, global-local parallel transformer (GLP-Transformer), is proposed for balanced diagnostic performance within resource constraints. In this end -to -end framework, a multi -channel vibration feature fusion embedding block is designed, which extracts multi -position sensor signals to acquire richer original features. Furthermore, a multi -layer network structure with alternately stacked global-local parallel self -activation unit is presented for fault feature mapping processing at low costs. This unit integrates the parameter efficiency of convolutional operation and the global feature extraction expertise of transformer. Experimental verification is performed on publicly available and self -built data platforms. Compared with other methods, GLP-Transformer significantly reduces the requirements for storage and computational resources (Params: 48 . 28 K, FLOPs: 2 . 74 M ) while possessing advanced generalization ability and robustness.
引用
收藏
页数:12
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