机械与电子2024，Vol.42Issue(7)：3-9,7.
基于信息融合和SA-CNN的轴承故障诊断
Bearing Fault Diagnosis Method Based on Information Fusion and Self-attention Convolutional Neural Network
摘要
Abstract
Aiming at the problems of difficulty in bearing fault feature extraction,single input signal and low fault recognition rate,a bearing fault diagnosis method based on multi-head attention information fusion and self attention convolutional neural network(SA-CNN)was proposed.Firstly,the bearing fail-ure of metro traction motor was pre-made.The bearing test stand with variable working conditions was built and the experimental scheme was designed to collect the bearing vibration signal and sound emission signal.Next,the multi-head attention mechanism is employed to fuse the vibration fault signals and a-coustic emission signals of the bearings.Finally,the fused signals are put into a self-attentive mechanism convolutional neural network for fault diagnosis.The final results show that based on multi-head atten-tion information fusion and SA-CNN can effectively pay attention to bearing fault characteristic signals,and improve the accuracy of bearing fault diagnosis under varying working conditions.关键词
轴承故障诊断/多头注意力机制/信息融合/自注意力机制/CNNKey words
bearing fault diagnosis/multi-head attention mechanism/information fusion/self-atten-tion mechanism/CNN分类
机械制造引用本文复制引用
王云,徐彦伟,何可承,颉潭成,王军华,蔡海潮..基于信息融合和SA-CNN的轴承故障诊断[J].机械与电子,2024,42(7):3-9,7.基金项目
国家自然科学基金资助项目(51805151) （51805151）
河南省高等学校重点科研项目(21B460004) （21B460004）
