摘要
针对轴承故障诊断问题,提出基于VGG16卷积神经网络与迁移学习的故障诊断方法。首先将轴承原始振动信号数据利用信号转图像方法进行预处理,生成相应的目标数据集;然后将已经预训练过的VGG16模型在生成的目标数据集上训练并微调;最后将微调后的VGG16模型应用于故障诊断。将该方法分别在凯斯西储大学和辛辛那提大学的轴承数据集上进行验证,结果表明该方法能够取得接近100%的轴承故障诊断准确率,具有较好的应用前景。
In this paper,we propose a bearing fault diagnosis method based on the VGG16 convolutional neural network with transfer learning.Firstly,the original bearing vibration signal data are preprocessed by a signal-to-image conversion,to generate a target bearing data set.Then,the pretrained VGG16 model is deeply trained by the target data set,and the related parameters are fine-tuned.Finally,the iterated VGG16 model is applied for the fault diagnosis.The above method is verified by the bearing data sets obtained from Case Western Reserve University and University of Cincinnati,respectively.The experimental results show that a diagnostic accuracy of nearly 100%is achieved by our method,which indicates that this method has a good application prospect in the field of the bearing fault diagnosis.
作者
赵宇凯
徐高威
刘敏(指导)
ZHAO Yukai;XU Gaowei;LIU Min(School of Electronic and Information Engineering,Tongji University,Shanghai 201804,China)
出处
《航天器环境工程》
2020年第5期446-451,共6页
Spacecraft Environment Engineering
基金
国家自然科学基金项目(编号:61573257)
北京卫星环境工程研究所创新基金项目(编号:CAST-BISEE2019-022)。
