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
王云 1徐彦伟 2何可承 1颉潭成 2王军华 2蔡海潮1
作者信息
- 1. 河南科技大学机电工程学院,河南洛阳 471003
- 2. 河南科技大学机电工程学院,河南洛阳 471003;智能数控装备河南省工程实验室,河南洛阳 471003
- 折叠
摘要
针对轴承故障特征提取困难、输入信号单一及故障识别率低等问题,提出基于多头注意力机制信息融合和自注意力机制卷积神经网络的轴承故障诊断方法.首先,预制地铁牵引电机轴承故障,搭建变工况轴承实验台并设计实验方案,采集轴承振动信号和声发射信号;其次,利用多头注意力机制将轴承的振动信号和声发射信号进行融合;最后,将融合后的信号输入自注意力机制卷积神经网络中进行故障诊断.实验结果表明,基于多头注意力机制信息融合和SA-CNN的轴承故障智能诊断方法,可以有效关注到轴承故障特征信号,提升变工况下轴承故障诊断的准确率.
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引用本文复制引用
基金项目
国家自然科学基金资助项目(51805151)
河南省高等学校重点科研项目(21B460004)
出版年
2024
国家科技期刊平台
NSTL
万方数据