基于空洞卷积和增强型多尺度特征自适应融合的滚动轴承故障诊断

Rolling bearing fault diagnosis based on dilated convolution and enhanced multi-scale feature adaptive fusion

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中文摘要：传统卷积神经网络(CNN)在识别故障类型时存在从原始振动信号中提取特征不足以及提取特征过程中需要更大的感受野以充分捕获信号的时间相关性的局限.针对轴承振动信号固有的多尺度特征,提出基于空洞卷积和增强型多尺度自适应特征融合的模型(DC-MAFFM).利用空洞卷积的大感受野提取信号特征,同时引入残差连接来减少卷积层上的信息损失,从而有效过滤信号中的噪声;设计改进的多尺度特征提取模块,在不同尺度上捕获互补的诊断特征,同时在各层都进行不同尺度特征融合,充分学习信号的高频和低频特征;利用提出的特征自适应融合模块对不同尺度的特征自适应赋予权重,增强判别特征学习的能力.在2个轴承数据集上进行验证,结果表明所提模型在噪声和变工况下有较强的诊断能力.在强噪声情况下,故障诊断准确率分别达到88.08％和75.56％,与其他方法相比有显著优势.

外文摘要：The traditional convolutional neural network(CNN)has the limitations of insufficiently extracting features from the original vibration signals and requiring a larger sensory field to fully capture the temporal correlation of the signals in the process of extracting the features when recognizing the fault types.A dilated convolution and enhanced multi-scale adaptive feature fusion model(DC-MAFFM)was proposed considering the inherent multi-scale characteristics of bearing vibration signals.The signal features were extracted using the large receptive field of the dilated convolution,and the residual connection was introduced to reduce the information loss on the convolution layer,so as to effectively filter the noise in the signal.An improved multi-scale feature extraction module was designed to capture complementary diagnostic features at different scales,meanwhile,the different-scale feature fusion was performed at each layer to fully learn the high-frequency and low-frequency features of the signal.The proposed feature adaptive fusion module was used to adaptively assign weights to the features at different scales to enhance the ability of discriminative feature learning.Verification was carried out on two bearing datasets,and results showed that the proposed model had strong diagnostic ability under noise and variable working conditions.In the case of strong noise,the fault diagnosis accuracy reached 88.08％and 75.56％,respectively,which demonstrated that the DC-MAFFM had a significant advantage over other methods.

外文关键词：

fault diagnosisdilated convolutionresidual connectionmulti-scale feature extractionadaptive fusion

作者：

韩康、战洪飞、余军合、王瑞

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作者单位：

宁波大学机械工程与力学学院,浙江宁波 315211

关键词：

故障诊断空洞卷积残差连接多尺度特征提取自适应融合

基金：

国家自然科学基金国家重点研发计划国家重点研发计划浙江省省属高校基本科研业务费资助项目浙江省公益技术应用研究项目

项目编号：

716710972019YFB17071012019YFB1707103SJLZ2023001LGG20E050010

出版年：

2024

DOI：

10.3785/j.issn.1008-973X.2024.06.018

浙江大学学报(工学版)

浙江大学

浙江大学学报(工学版)

CSTPCD北大核心

影响因子：0.625

ISSN：1008-973X

年,卷(期)：2024.58(6)