北京信息科技大学学报（自然科学版）2026，Vol.41Issue(1)：48-57,10.DOI:10.16508/j.cnki.11-5866/n.2026.01.006
基于多路并行注意力融合网络的轴承故障诊断
Bearing fault diagnosis based on a multi-branch parallel attention fusion network
摘要
Abstract
To address the insufficient extraction of local features,limited multi-scale representation capability,and the tendency for key channel responses to be overwhelmed by noise in fault diagnosis of vibration signals from train axle-box bearings,a multi-branch parallel attention fusion network(MBPAFN)was proposed.In the network,raw one-dimensional(1D)vibration signals were taken as input,and three parallel branches at the raw,average-pooling-downsampled,and max-pooling-downsampled scales were constructed to capture global time-domain patterns,low-frequency trends,and local extrema features,respectively.In each branch,1D convolutional modules were employed to extract local temporal features,and an efficient channel attention mechanism was incorporated to adaptively recalibrate channel weights,while a Transformer encoder was introduced to model long-range temporal dependencies,enabling joint representation of local impact features and global evolution patterns.The three-way features were adaptively fused through a channel alignment and feature fusion module and then fed into a fully connected classifier to output fault types,forming an end-to-end diagnostic pipeline.Experimental results on the publicly available Case Western Reserve University(CWRU)bearing dataset and a self-built train axle-box bearing dataset show that,compared with several representative deep learning diagnostic models,MBPAFN achieves higher recognition accuracy and better robustness under strong noise and non-stationary operating conditions,demonstrating that the multi-branch parallel attention fusion architecture can effectively improve the fault diagnosis performance of rolling bearings.关键词
列车轴箱轴承/故障诊断/多路并行网络/注意力机制/特征融合Key words
train axle-box bearing/fault diagnosis/multi-branch network/attention mechanism/feature fusion分类
机械制造引用本文复制引用
罗怀超,黄民..基于多路并行注意力融合网络的轴承故障诊断[J].北京信息科技大学学报（自然科学版）,2026,41(1):48-57,10.基金项目
北京市科学技术概念验证项目(20220481077) （20220481077）
