基于GADF与引入迁移学习的ResNet34对变速轴承的故障诊断

针对传统分析方法对于轴承在变速情况下的故障诊断较为困难的问题，提出一种基于格拉姆角差场(Gramian angular difference field，GADF)与引入迁移学习的ResNet34模型对变速轴承的故障诊断方法.首先利用GADF对一维时序振动信号进行编码，转换成二维图像，产生相应的故障图，再将这些故障图输入引用迁移学习的残差网络(ResNet)自动进行故障特征提取及分类.为了验证该方法的有效性，综合对比其他方法，本文方法在西储大学轴承数据集上表现更好.最后对加拿大渥太华大学的变速轴承数据集进行诊断，检验其在变速情况下的分类性能.结果表明，在变速情况下，所提方法可达到较高的诊断精度.

Fault Diagnosis of Variable Speed Bearings Based on GADF and ResNet34 Introduced Transfer Learning

Aiming at the problem that traditional analysis methods are difficult for fault diagnosis of bearings under variable speeds， a fault diagnosis method was proposed for variable speed bearings based on GADF(Gramian angular difference field) and ResNet34 model introduced transfer learning. Firstly， a one-dimensional time-series vibration signal was encoded by using GADF and converted into a two-dimensional image to generate the corresponding fault maps， which were then input into a residual network (ResNet) using transfer learning to automatically extract and classify fault features. To verify the effectiveness of the method， the results of comprehensive comparison with other methods showed that the proposed method performs better on the Western Reserve University bearing dataset. Finally， the variable speed bearing dataset from the University of Ottawa in Canada was diagnosed to examine its classification performance in the variable speed case. The results showed that a high diagnostic accuracy can be achieved in the variable speed case.