LiConvFormer: A lightweight fault diagnosis framework using separable multiscale convolution and broadcast self-attention

• Published in: Expert systems with applications Vol. 237; p. 121338
• Main Authors: Yan, Shen, Shao, Haidong, Wang, Jie, Zheng, Xinyu, Liu, Bin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2024
• Subjects: Broadcast self-attention; Fault diagnosis; LiConvFormer; Lightweight framework; Separable multiscale convolution; Fault diagnosis; Separable multiscale convolution; Broadcast self-attention; Lightweight framework; LiConvFormer

In recent studies, Transformer collaborated with convolution neural network (CNN) have made certain progress in the field of intelligent fault diagnosis by leveraging their respective advantages of global and local feature extraction. However, the multihead self-attention block used by Transformer a...