基于VMD-CNN-BiTCN滚动轴承故障诊断  

Fault Diagnosis of Rolling Bearings Based on VMD-CNN-BiTCN

作　　者：徐志祥[1] 玄永伟 王洪洋 王壬杰 XU Zhixiang;XUAN Yongwei;WANG Hongyang;WANG Renjie(School of Mechanical Engineering,Dalian University of Technology,Dalian 116024,China)

机构地区：[1]大连理工大学机械工程学院,大连116024

出　　处：《微特电机》2025年第2期68-73,共6页Small & Special Electrical Machines

摘　　要：针对滚动轴承故障诊断中,传统卷积神经网络(CNN)特征提取感受野受限、无法有效提取数据时序特征的问题,提出了一种CNN结合双向时间卷积网络(BiTCN)的模型,该模型能够扩展感受野并有效捕获数据的时序特征。将原始振动信号通过变分模态(VMD)分解为K个本征模函数(IMF);将分解后的信号输入到CNN层中进行特征提取和信号压缩;将该信号送入BiTCN中,提取正反两个方向的时序特征,使用膨胀卷积最大化感受野;通过池化层和全连接层实现滚动轴承故障诊断。实验结果显示,该模型在特征提取能力和时序特征感知具有显著优势,能够在多个数据集中表现出良好的故障诊断性能和泛化能力。To address the limitations of traditional convolutional neural networks(CNN)in feature extraction due to their restricted receptive field and inability to capture temporal features,a model combining CNN and bidirectional temporal convolution network(BiTCN)was proposed.The proposed model can expand the receptive field and effectively capture temporal dependencies.The original vibration signals were decomposed into K intrinsic mode functions(IMFs)using variational mode decomposition(VMD).The decomposed signals were fed into CNN layers for feature extraction and signal compression.The processed signals were input into BiTCN to extract bidirectional temporal features.Dilated convolutions are applied to maximize the receptive field.The signals were passed through pooling layers and fully connected layers for fault diagnosis of rolling bearings.Experimental results demonstrate that the proposed model excels in feature extraction and temporal feature perception,and achieves superior fault diagnosis performance and generalization capability across multiple datasets.

关 键 词：滚动轴承 故障诊断 卷积神经网络 双向时间卷积网络 变分模态分解 

分 类 号：TM315[电气工程—电机]