基于图卷积网络的非均衡数据船舶柴油机故障诊断

Fault diagnosis of marine diesel engines based on graph convolutional network under unbalanced datasets

[目的]船舶柴油机状态信息数据普遍存在类别不均衡的问题,非均衡数据集降低了基于数据驱动的故障诊断模型对柴油机健康状况自动识别的准确性。因此,提出基于样本间概率相似性的图卷积网络(GCN)模型,以解决非均衡数据集分类问题。[方法]首先,引入Kullback-Leibler散度来计算样本间的概率相似性,以挖掘样本间的非线性关系,将各个样本间的相似性用构造概率图的拓扑结构体现。然后,利用图学习对样本特征及邻近样本特征进行聚合和提取,为非均衡数据集的分类提供更多的信息。最后,通过构造多层图卷积层,对样本特征信息进行更深层次的挖掘。[结果]仿真及台架实验表明,所提出的图卷积网络能够有效地学习更多样本信息,通过聚合邻近样本信息来提高非均衡数据集分类的准确率。[结论]该模型的召回率和精确率均高于其他分类模型,具有一定的工程应用价值。

[Objectives]The class imbalance problems which are prevalent in the condition monitoring data of marine diesel engines significantly deteriorate the performance of data-driven models for the automatic and accurate identification of the health condition of engines. In this paper, a graph convolutional network(GCN)model based on probability similarity between samples is proposed to solve the classification problem of unbalanced datasets. [Methods]First, the Kullback-Leibler divergence is introduced to calculate the probability similarity between samples and mine the nonlinear relationship, and a probability topological graph is constructed to represent the similarity of samples. Graph learning is then introduced to learn and extract the correlations between adjacent samples in addition to their own features, providing more information for the imbalanced classification task. After multi-layer graph learning, the higher-level features of each node are extracted.[Results]The two cases of the simulation model and bench test clearly show that the proposed method efficiently extracts more information based on aggregating neighboring samples’ features, and improves the classification accuracy under imbalanced datasets. [Conclusions]The precision and recall rate of the proposed GCN model are higher than those of other state-of-the-art algorithms, giving it certain practical application value.