Bearing fault diagnosis for variable operating conditions based on KAN convolution and dual branch fusion attention

Bearing fault diagnosis for variable operating conditions based on KAN convolution and dual branch fusion attention

Abstract This paper proposes a bearing fault diagnosis method based on Kolmogorov–Arnold Convolutional Network: Adaptive Context-aware Graph Channel Attention with Squeeze-and-Excitation Networks (KANConv-ACGCA-SENet). Firstly, a new structure of KANs is applied to Convolutional Neural Networks (CNN...

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Bibliographic Details
Main Authors: Qibing Wang, Chuanjie Yin, Kun She, Qinfeng Tong, Guoxiong Lu, Hongbing Zhang, Jiawei Lu
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
ACGCA
Attention mechanism
Bearing fault diagnosis
Convolution
Kolmogorov–Arnold networks (KANs)
Online Access:https://doi.org/10.1038/s41598-025-04620-1
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