论文标题
图基于神经网络的早期轴承故障检测
Graph Neural Network-based Early Bearing Fault Detection
论文作者
论文摘要
早期发现故障对于避免发生灾难性事故并确保机械的安全操作至关重要。提出了一种新型的基于神经网络的故障检测方法，以在AI和现实世界运行机械系统之间建立桥梁。首先，将振动信号（欧几里得结构化数据）转换为图（非欧几里得结构化数据），因此最初彼此独立的振动信号彼此相关。其次，将数据集及其相应图输入到GNN中进行训练，其中包含网络每个隐藏层中的图形，使图形神经网络能够学习自身及其邻居的特征值，而所获得的早期特征具有更强的辨别性。最后，确定在GNN的输出层中难以重建故障对象的顶部N对象。轴承的公共数据集已被用来验证所提出方法的有效性。我们发现所提出的方法可以成功地检测出在正常对象区域中混合的故障对象。
Early detection of faults is of importance to avoid catastrophic accidents and ensure safe operation of machinery. A novel graph neural network-based fault detection method is proposed to build a bridge between AI and real-world running mechanical systems. First, the vibration signals, which are Euclidean structured data, are converted into graph (non-Euclidean structured data), so that the vibration signals, which are originally independent of each other, are correlated with each other. Second, inputs the dataset together with its corresponding graph into the GNN for training, which contains graphs in each hidden layer of the network, enabling the graph neural network to learn the feature values of itself and its neighbors, and the obtained early features have stronger discriminability. Finally, determines the top-n objects that are difficult to reconstruct in the output layer of the GNN as fault objects. A public datasets of bearings have been used to verify the effectiveness of the proposed method. We find that the proposed method can successfully detect faulty objects that are mixed in the normal object region.
