Abstract:

Deep learning methods are widely used in bearing fault diagnosis， but in actual engineering applications， real service fault data during bearing service are not easily collected and lack of data labels， which is difficult to train adequately. Focused on the difficulty of bearing service fault diagnosis， a transfer learning model based on Graph Convolutional Network （GCN） in bearing service fault diagnosis was proposed. In the model， the fault knowledge was learned from artificially simulated damage fault data with sufficient data and transferred to real service faults， so as to improve the diagnostic accuracy of service faults. Specifically， the original vibration signals of artificially simulated damage fault data and service fault data were converted into the time-frequency maps with both time and frequency information through wavelet transform， and the obtained maps were input into graph convolutional layers for learning， so as to effectively extract the fault feature representations in the source and target domains. Then the Wasserstein distance between the data distributions of source domain and target domain was calculated to measure the difference between two data distributions， and a fault diagnosis model that can diagnose bearing service faults was constructed by minimizing the difference in data distribution. A variety of different tasks were designed for experiments with different bearing failure data sets and different operating conditions. Experimental results show that the proposed model has the ability to diagnose bearing service faults and also can be transferred from one working condition to another， and perform fault diagnosis between different component types and different working conditions.

Key words: bearing fault diagnosis, deep learning, transfer learning, Graph Convolutional Network (GCN), wavelet transform

摘要：

深度学习方法被广泛应用于轴承故障诊断，但在实际工程应用中，轴承服役期间的真实服役故障数据不易收集，缺乏数据标签，难以进行充分的训练。针对轴承服役故障诊断困难的问题，提出了一种基于图卷积网络（GCN）的迁移学习轴承服役故障诊断模型。该模型从数据充足的人工模拟损伤故障数据中学习故障知识，并迁移到真实的服役故障上，以提高服役故障的诊断准确率。具体来说，通过将人工模拟损伤故障数据和服役故障数据的原始振动信号由小波变换转换为同时具有时间和频率信息的时频图，并将得到的时频图输入到图卷积层中进行学习，从而有效地提取源域和目标域的故障特征表示；然后计算源域和目标域的数据分布之间的Wasserstein距离来度量两个数据分布之间的差异，通过最小化数据分布差异，构建了一个能诊断轴承服役故障的故障诊断模型。在不同的轴承故障数据集和不同工作条件下设计了多种不同的任务进行实验，实验结果表明，该模型具有诊断轴承服役故障的能力，同时也能从一个工作条件迁移到另一工作条件，在不同组件类型和不同工作条件之间进行故障诊断。

关键词: 轴承故障诊断, 深度学习, 迁移学习, 图卷积网络, 小波变换