摘要
针对风电机组轴承故障诊断中经典一维卷积神经网络和二维卷积神经网络准确率低的问题,将一维原始振动信号和二维时频图相融合,构建基于CBAM-InceptionV2-双流CNN的滚动轴承故障诊断方法。首先,通过快速傅里叶变换和小波变换,将原始振动信号转化为一维数据和二维时频图;其次,构建基于CBAMInceptionV2-双流CNN模型;最后,将提取到的双层特征信息进行融合,并输入到Softmax完成故障分类。实验结果表明,所提模型能够显著地提升轴承故障诊断的准确性。
Targeting the problem of low accuracy of one-dimensional convolutional neural network and two-dimensional convolutional neural network in wind turbine bearing fault diagnosis,the paper combines the one-dimensional original vibration signal with two dimensional time-frequency map,and proposes a rolling bearing fault diagnosis method based on CBAM-InceptionV2-two-stream CNN.Firstly,the original vibration signal is converted into the one-dimensional data and two-dimensional time-frequency map with fast Fourier transformation(FFT)and wavelet transformation.Then a CBAM-InceptionV2-two-stream CNN model is established.Finally,the extracted double level feature information is fused and input to SoftMax to complete the fault classification.The experimental results show that the proposed model can significantly enhance the accuracy of the bearing fault diagnosis.
作者
李俊卿
马亚鹏
胡晓东
马志鹏
王罗
何玉灵
张承志
LI Junqing;MA Yapeng;HU Xiaodong;MA Zhipeng;WANG Luo;HE Yuling;ZHANG Chengzhi(Department of Electric Power Engineering,North China Electric Power University,Baoding 071000,China;Taiyuan Heavy Industry Co.,Ltd.,Taiyuan 030000,China;China Three Gorges Corporation,Wuhan 430010,China;Department of Mechanical Engineering,North China Electric Power University,Baoding 071000,China)
出处
《智慧电力》
北大核心
2023年第6期28-33,共6页
Smart Power
基金
国家自然科学基金资助项目(52177042)。
