基于噪声和图卷积神经网络的电机故障诊断

文章针对旋转机械设备维护和噪声监测治理的需求，提出了一种基于电机噪声信号和图卷积神经网络的故障诊断算法。该算法对时域数据进行傅里叶变换，将变换后的频域数据转化为图数据，利用提出的新型图卷积神经网络结构对图数据进行训练并分类。搭建电机故障实验平台，完成了6种不同状态的电机噪声信号采集与实验验证。实验结果表明，图卷积神经网络能根据有限的电机噪声信号有效识别出电机故障，并具有一定的小样本学习能力，能够在样本量较少的情况下进行故障分类。对比分析表明，该算法分类准确率优于K最近邻-图算法、一维卷积神经网络、自动编码器和支持向量机等其他算法，为实际工程应用提供了参考。

Motor fault diagnosis based on noise and graph convolutional neural networks

A fault diagnosis algorithm based on motor noise signals and graph convolutional neural network (GCNN) is proposed for the needs of maintenance and noise monitoring and control of rotating machinery equipment. This algorithm transforms the time domain data into graph data by Fourier transform, and uses the proposed new graph convolutional neural network structure to train and classify the graph data. The experimental platform of motor fault is built, and the collection and experimental verification of motor noise signal in 6 different states are completed. The experimental results show that GCNN can effectively identify motor faults according to the limited motor noise signals, and has a certain small sample learning ability, and can classify faults under the condition of a small sample size. Comparative analysis shows that the accuracy of this algorithm is better than other algorithms such as K nearest neighbor graph algorithm, 1D convolutional neural network, automatic encoder and support vector machine, which provides a reference for practical engineering applications.