Chen Zhiwen, Xu Jiamin, Peng Tao, Yang Chunhua
IEEE Trans Cybern. 2022 Sep;52(9):9157-9169. doi: 10.1109/TCYB.2021.3059002. Epub 2022 Aug 18.
Deep-neural network-based fault diagnosis methods have been widely used according to the state of the art. However, a few of them consider the prior knowledge of the system of interest, which is beneficial for fault diagnosis. To this end, a new fault diagnosis method based on the graph convolutional network (GCN) using a hybrid of the available measurement and the prior knowledge is proposed. Specifically, this method first uses the structural analysis (SA) method to prediagnose the fault and then converts the prediagnosis results into the association graph. Then, the graph and measurements are sent into the GCN model, in which a weight coefficient is introduced to adjust the influence of measurements and the prior knowledge. In this method, the graph structure of GCN is used as a joint point to connect SA based on the model and GCN based on data. In order to verify the effectiveness of the proposed method, an experiment is carried out. The results show that the proposed method, which combines the advantages of both SA and GCN, has better diagnosis results than the existing methods based on common evaluation indicators.
根据目前的技术水平，基于深度神经网络的故障诊断方法已被广泛使用。然而，其中很少有方法考虑到感兴趣系统的先验知识，而这对故障诊断是有益的。为此，提出了一种基于图卷积网络（GCN）的新故障诊断方法，该方法使用可用测量值和先验知识的混合。具体来说，该方法首先使用结构分析（SA）方法对故障进行预诊断，然后将预诊断结果转换为关联图。然后，将图和测量值输入到GCN模型中，其中引入了一个权重系数来调整测量值和先验知识的影响。在该方法中，GCN的图结构用作连接基于模型的SA和基于数据的GCN的结合点。为了验证所提方法的有效性，进行了一项实验。结果表明，所提方法结合了SA和GCN的优点，在常用评估指标下比现有方法具有更好的诊断结果。
