摘要
针对滚动轴承工作环境多变和样本不足导致故障诊断效果不佳的问题,提出一种多模态注意力卷积神经网络。该网络采用多个并行卷积层构建,并结合注意力机制,有效地提取了丰富的故障特征。然后提出了两种有限数据条件下的数据增强方法,解决了数据样本不足的问题。另外,将采集到的滚动轴承时域信号通过小波变换转换为时频图谱作为网络输入来提高数据质量,利用多种转频下故障数据对所提方法进行实验分析。结果表明,该方法在变工况实验中准确率高,聚类效果明显,说明该方法能有效提高变工况下轴承故障诊断的精度,具有很好的应用价值。
To solve the problem of poor fault diagnosis caused by the variable working environment of rolling bearings and insufficient data samples, a method called multi-modal attention convolutional neural network is proposed. This network is constructed by multiple parallel convolutional layers, and combines the attention mechanism to effectively extract rich fault features. Then, two data enhancement methods under limited data conditions are proposed to solve the problem of insufficient data samples. In addition, wavelet transform is used to convert the collected time-domain signals of rolling bearings into time-frequency spectrums as network input to improve data quality. The proposed method is experimentally analyzed by using fault data under multiple frequency conversions. The experimental results show that the method has high accuracy and obvious clustering effect under variable working conditions, which indicates that the method can effectively improve the accuracy of bearing fault diagnosis under variable working conditions and has a good application value.
作者
张珂
王竞禹
石怀涛
张啸尘
付玲
ZHANG Ke;WANG Jing-yu;SHI Huai-tao;ZHANG Xiao-chen;FU Ling(School of Mechanical Engineering,Shenyang Jianzhu University,Shenyang 110168,China;Zoomlion Heavy Industry Science and Technology Co.,Ltd.,Changsha 410006,China)
出处
《控制工程》
CSCD
北大核心
2022年第2期254-262,共9页
Control Engineering of China
基金
国家自然科学基金资助项目(52175107)
住建部科技计划项目(2019-K-080,2019-K-088)
河北省重点研发计划项目(19211904D)
沈阳市重点创新研发计划项目(Y19-1-004)
沈阳市重点科技研究项目(20-202-4-40)。
关键词
变工况轴承故障诊断
卷积神经网络
注意力机制
数据增强
小波变换
Bearing fault diagnosis under variable conditions
convolutional neural network
attention mechanism
data enhancement
wavelet transform
作者简介
张珂(1969-),男,辽宁沈阳人,博士,教授,主要从事机电一体化技术、数控机床主轴系统等方面的教学与科研工作;通信作者:石怀涛(1982-),男,辽宁沈阳人,博士,教授,主要从事机械系统故障诊断与智能化控制、非线性欠驱动吊运机器人的动力学建模及防摆控制等方面的教学与科研工作(Email:sht@sjzu.edu.cn)。
