基于深度卷积测量网络的滚动轴承压缩域故障特征提取方法  

Rolling bearing fault feature extraction in the compressed domainwith deep convolutional measurement network

作　　者：林慧斌[1] 王洪畅 习慈羊 LIN Hui-bin;WANG Hong-chang;XI Ci-yang(School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510641,China)

机构地区：[1]华南理工大学机械与汽车工程学院,广东广州510641

出　　处：《振动工程学报》2024年第3期485-496,共12页Journal of Vibration Engineering

基　　金：国家自然科学基金资助项目(51875207);广东省自然科学基金资助项目(2020A1515010750,2022A1515011238)。

摘　　要：压缩感知可有效降低机械状态监测信号的数据存储和传输压力,而现有压缩感知方法在故障诊断的应用中存在压缩效率低下、信号重构过程缓慢等问题。本文利用自编码网络与压缩感知的对应关系,提出了一种基于深度卷积测量网络的滚动轴承压缩域故障特征提取方法。针对无噪声的故障信号样本难以获取的问题,提出一种利用故障机理构建数据集的方法,利用该仿真数据集训练得到的模型适用于不同工况下的实测轴承信号。构造网络层数由所需要的信号压缩率确定、隐含层与原信号的频率呈对应关系的深度卷积去噪自编码网络。截取训练完备的编码子网络(即深度卷积测量网络)代替传统的观测矩阵对滚动轴承振动信号进行压缩测量,实现压缩域的故障特征提取。仿真分析验证了所提数据集构造方法及压缩域特征提取方法的有效性。滚动轴承实验信号分析进一步验证了采用所提方法训练得到的深度卷积测量网络具有很好的泛化性,且能够在压缩率远低于传统压缩感知方法的情况下有效地提取轴承故障特征成分并进行故障诊断。Compressed sensing can effectively relieve the burden of data storage and transmission for mechanical condition monitor-ing.However,this method exists some problems such as low compression efficiency and slow signal reconstruction process in the application of fault diagnosis.In this paper,using the corresponding relationship between autoencoder and compressed sensing,a novel fault feature extraction method of the rolling bearing in the compressed domain based on the deep convolutional measurement network is proposed.For the problem that noise-free fault signal samples are difficult to obtain,a dataset construction method based on the fault mechanism is proposed.The model trained on this dataset is suitable for bearing signals under different working conditions A deep convolutional denoising autoencoder(DCDAE)is constructed,in which the number of layers is determined by the required signal compression rate and the frequency of the hidden layer corresponds to that of the original signal.The fully trained encoding sub-network of DCDAE,named deep convolutional measurement network(DCMN),is used to compress the rolling bearing vibration signal instead of the traditional measurement matrix,and then the fault features are directly extracted in the compressed domain.The effectiveness of the proposed dataset construction method and the compressed domain feature extraction method are analyzed through the simulations.The rolling bearing experimental signals further verify that the deep convolutional measurement network trained by the proposed method has good generalization and can effectively extract fault features for fault diagnosis in the compressed domain with a compression ratio far lower than that of the traditional compressed sensing method.

关 键 词：故障诊断 滚动轴承 故障特征提取 压缩感知 深度卷积测量网络 

分 类 号：TH165.3[机械工程—机械制造及自动化] TH133.33