Zhang Qianqian, Hao Caiyun, Lv Zhongwei, Fan Qiuxia
School of Automation and Software Engineering, Shanxi University, Taiyuan, P.R. China.
PLoS One. 2023 Oct 5;18(10):e0292381. doi: 10.1371/journal.pone.0292381. eCollection 2023.
Learning powerful discriminative features is the key for machine fault diagnosis. Most existing methods based on convolutional neural network (CNN) have achieved promising results. However, they primarily focus on global features derived from sample signals and fail to explicitly mine relationships between signals. In contrast, graph convolutional network (GCN) is able to efficiently mine data relationships by taking graph data with topological structure as input, making them highly effective for feature representation in non-Euclidean space. In this article, to make good use of the advantages of CNN and GCN, we propose a graph attentional convolutional neural network (GACNN) for effective intelligent fault diagnosis, which includes two subnetworks of fully CNN and GCN to extract the multilevel features information, and uses Efficient Channel Attention (ECA) attention mechanism to reduce information loss. Extensive experiments on three datasets show that our framework improves the representation ability of features and fault diagnosis performance, and achieves competitive accuracy against other approaches. And the results show that GACNN can achieve superior performance even under a strong background noise environment.
学习强大的判别特征是机器故障诊断的关键。大多数基于卷积神经网络（CNN）的现有方法都取得了不错的成果。然而，它们主要关注从样本信号中导出的全局特征，未能明确挖掘信号之间的关系。相比之下，图卷积网络（GCN）能够通过将具有拓扑结构的图数据作为输入来有效挖掘数据关系，使其在非欧几里得空间中的特征表示非常有效。在本文中，为了充分利用CNN和GCN的优势，我们提出了一种用于有效智能故障诊断的图注意力卷积神经网络（GACNN），它包括全CNN和GCN两个子网络来提取多级特征信息，并使用高效通道注意力（ECA）机制来减少信息损失。在三个数据集上进行的大量实验表明，我们的框架提高了特征的表示能力和故障诊断性能，并且与其他方法相比具有有竞争力的准确率。结果表明，即使在强背景噪声环境下，GACNN也能实现卓越的性能。
