摘要
提出一种基于连续小波变换(CWT)和坐标注意机制残差网络(CooAtten-Resnet)的弧齿锥齿轮箱智能故障诊断方法。首先将振动信号重叠采样获得大量信号样本,将这些样本通过连续小波变换将振动信号转化为时频图,并以此构建不同故障下的时频数据集,同时通过人为添加噪声样本以验证噪声对此类诊断方法的影响;然后将时频图数据集用于CooAtten-Resnet训练;最后对故障进行分类并输出诊断结果。结果表明,该方法可以准确的识别弧齿锥齿轮箱故障,无人为添加噪声的情况诊断准确率可达100%,添加噪声后在无降噪处理的情况下准确率仍在93%以上。相较于其他方法,该方法的准确率更高,抗噪能力更强,网络收敛速度更快,诊断结果更稳定。
An intelligent fault diagnosis method for spiral bevel gear box based on continuous wavelet transform(CWT)and coordinate attention mechanism residual network(CooAtten:-Resnet)is proposed.Firstly,a large number of signal samples are obtained by overlapping sampling of vibration signals.These samples are converted into time-frequency maps by continuous wavelet transform,and time-frequency data sets under different faults are constructed.At the same time,noise samples are added manually to verify the impact of noise on such diagnostic methods;Then the time-frequency map data set is used for CooAtten-Resnet training;Finally,the fault is classified and the diagnosis results are output.The results show that this method can accurately identify the fault of spiral bevel gear box,and the accuracy rate of diagnosis can reach 100%when no one adds noise,and the accuracy rate is still above 93%when no noise reduction is conducted after adding noise.Compared with other methods,this method has higher accuracy,stronger anti-noise abilty,faster network convergence and more stable diagnosis results.
作者
张旭
许昕
潘宏侠
徐轟钊
原涛涛
王同
Zhang Xu;Xu Xin;Pan Hongxia;Xu Hongzhao;Yuan Taotao;WangTong(School of Mechanical Engineering,North University of China,Taiyuan 030051,China;System Identification and Diagnosis Technology Research Institute,North University of China,Taiyuan 030051,China)
出处
《电子测量技术》
北大核心
2023年第3期182-189,共8页
Electronic Measurement Technology
关键词
小波时频图
弧齿锥齿轮
智能故障诊断
残差网络
注意力机制
坐标注意力机制残差网络
wavelet time-frequency diagram
spiral bevel gear
intelligent fault diagnosis
residual network
attention mechanism
coordinate attention
作者简介
张旭,硕士,主要研究方向为过程装备运行状态监测与故障诊断。E-mail:zx15332774210@163.com
