Semi-supervised graph convolutional network and its application in intelligent fault diagnosis of rotating machinery
Semi-supervised graph convolutional network and its application in intelligent fault diagnosis of rotating machinery
复制标题
半监督图卷积网络及其在旋转机械智能故障诊断中的应用
DOI：
10.1016/j.measurement.2021.110084
复制
发表时间：
2021-09-09
期刊：
影响因子：
5.6
通讯作者：
Yu, Dejie
中科院分区：
文献类型：
--
作者：
Gao, Yiyuan;Chen, Mang;Yu, Dejie
针对小样本情况下机械故障诊断的困难，提出了一种基于半监督图卷积网络（SSGCN）的旋转机械智能故障诊断方法。SSGCN在分析自然形成的图形数据方面有很好的应用，但对于复杂的机械振动数据还没有研究。此外，SSGCN只适用于图形数据，但采集的振动信号是一维时间序列。为了更好地反映所有振动样本之间的局部几何性质，我们将所有振动样本构造成一个无向加权的k-最近邻图。对SSGCN进行了详细的参数分析。实验结果表明，该方法能够自适应地从原始振动信号中提取出有效的故障特征。即使每个齿轮或轴承条件的标号比仅为0.05，我们提出的方法仍然可以获得超过98%的平均精度。
Aiming at the difficulty of mechanical fault diagnosis with small samples, an intelligent fault diagnosis method for rotating machinery is proposed based on semi-supervised graph convolutional network (SSGCN). SSGCN has a good application in analyzing the naturally formed graph data, but it has not been researched for the complex mechanical vibration data. Besides, SSGCN is only applicable to graph data, but the collected vibration signals are one-dimensional time series. To well reflect the local geometry property between all vibration samples, we construct all vibration samples into an undirected and weighted k-nearest neighbor graph. The detailed parameter analysis is also carried out for SSGCN. Experimental results indicate that our proposed method can adaptively extract the available fault features from the raw vibration signals. Even if the label ratio is only 0.05 for each gear or bearing condition, our proposed method can still obtain an average accuracy of more than 98%.