Chen Caifeng, Yuan Yiping, Zhao Feiyang
School of Mechanical Engineering, Xinjiang University, Urumqi 830047, China.
Sensors (Basel). 2023 Oct 16;23(20):8489. doi: 10.3390/s23208489.
The high correlation between rolling bearing composite faults and single fault samples is prone to misclassification. Therefore, this paper proposes a rolling bearing composite fault diagnosis method based on a deep graph convolutional network. First, the acquired raw vibration signals are pre-processed and divided into sub-samples. Secondly, a number of sub-samples in different health states are constructed as graph-structured data, divided into a training set and a test set. Finally, the training set is used as input to a deep graph convolutional neural network (DGCN) model, which is trained to determine the optimal structure and parameters of the network. A test set verifies the feasibility and effectiveness of the network. The experimental result shows that the DGCN can effectively identify compound faults in rolling bearings, which provides a new approach for the identification of compound faults in bearings.
滚动轴承复合故障与单一故障样本之间的高相关性容易导致误分类。因此，本文提出了一种基于深度图卷积网络的滚动轴承复合故障诊断方法。首先，对采集到的原始振动信号进行预处理并划分为子样本。其次，将处于不同健康状态的多个子样本构建为图结构数据，分为训练集和测试集。最后，将训练集作为深度图卷积神经网络（DGCN）模型的输入，对该模型进行训练以确定网络的最优结构和参数。测试集验证了该网络的可行性和有效性。实验结果表明，DGCN能够有效识别滚动轴承中的复合故障，为轴承复合故障的识别提供了一种新方法。
