摘要
针对常用的故障诊断深度学习方法需要较高的设备成本与较长的训练时间,提出一种基于Inception-ResNet模型的轴承故障分类方法。通过使用Inception网络的并行结构使网络学习到不同尺度的特征,引入了残差结构来减少因网络加深所导致的退化现象,并加入了三维卷积,使不同通道间的信息相互交融。为验证本文方法的性能,使用凯斯西储大学数据集与IMS数据集进行验证,并与传统的浅层学习方法和深度学习方法进行了对比实验。结果表明,相较于其他方法,所提方法不仅拥有优良的诊断能力,在资源占用与训练效率上也更加优秀。
In view of the high equipment cost and long training time required by common deep learning methods for fault diagnosis, this paper proposes a bearing fault classification method based on the Inception-ResNet model. By using the Inception network′s parallel structure, the network learns features of different scales, resizing structures are introduced to reduce degradation caused by network deepening, and three-dimensional convolution is added to allow information between different channels to blend. In order to verify the performance of this method, case Western Reserve University data set and IMS data set were used for verification, and compared with the traditional shallow learning method and deep learning method, experiments were conducted. The results show that, compared with other methods, the method presented not only has excellent diagnostic ability, but also is better in terms of resource utilization and training efficiency.
作者
孔子宇
王海瑞
Kong Ziyu;Wang Hairui(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China)
出处
《电子测量技术》
北大核心
2021年第5期55-62,共8页
Electronic Measurement Technology
基金
国家自然科学基金(61263023,61863016)项目资助。
