改进小波阈值去噪和胶囊直连网络的轴承故障诊断  

Bearing Fault Diagnosis Based on Improved Wavelet Threshold Denoising and Capsule Direct Network

作　　者：杨婧媛 高文华[1] 董增寿[1] 曹俊琴[1] 康琳[1] YANG Jingyuan;GAO Wenhua;DONG Zengshou;CAO Junqin;KANG Lin(College of Electronic and Information Engineering,Taiyuan University of Science and Technology,Taiyuan Shanxi 030024,China)

机构地区：[1]太原科技大学电子信息工程学院,山西太原030024

出　　处：《机床与液压》2023年第8期200-204,共5页Machine Tool & Hydraulics

基　　金：山西省重点研发计划(201903D321012,201903D121023)。

摘　　要：作为轴承故障诊断依据,振动传感器采集的信号易受工作环境噪声干扰。为更加准确提取特征信息,采用改进传统小波阈值去噪方法,利用中间比例系数过渡方法,将传统硬阈值和软阈值结合,信号去噪更加平滑有效。去噪后的信号进行二维短时傅里叶变换,得到二维时频域数据结构。通过胶囊注意力方式改进ResNet网络直连结构,从而得到更好的分类模型Capsut-ResNet。通过对比去噪前后和不同注意力模型结构,证明了方法的有效性,能够实现更高的准确率。As a basis of bearing fault diagnosis,signal acquired by vibration sensor is easily affected by the work environment noise.In order to extract characteristic information accurately,an improved traditional wavelet threshold denoising method was adopted.The traditional hard threshold and soft threshold were combined using the method of intermediate proportional coefficient transition,and the denoising signal was smooth and effective.The denoising signal was operated by a two-dimensional short-time Fourier transform to obtain the two-dimensional time-frequency domain data structure.The capsule attention method was used to improve the direct structure of ResNet network,so as to acquire a better classification model.Comparing the structure of different attention models before and after denoising,the effectiveness of the method was proved.Higher accuracy can be achieved.

关 键 词：轴承故障诊断 小波阈值 系数过渡 胶囊注意力 直连结构 

分 类 号：TH133[机械工程—机械制造及自动化]