论文标题
具有可训练的邻接矩阵的图形神经网络，用于多元传感器数据的故障诊断
Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data
论文作者
论文摘要
及时检测到化学技术过程中的异常情况，以及对故障原因的最早发现，大大降低了工业工厂的生产成本。大量不同的传感器收到了有关技术过程状态和生产设备运行的数据。为了更好地预测过程和设备的行为，不仅有必要分别考虑每个传感器中信号的行为，而且还必须考虑到它们相关的相关性和隐藏的关系。基于图的数据表示有助于这一点。图节点可以表示为来自不同传感器的数据，边缘可以互相显示这些数据的影响。在这项工作中，研究了将图形神经网络应用于化学过程中故障诊断问题的可能性。提议在训练图神经网络期间构造图形。这允许训练模型在传感器之间的依赖项之间不知道的数据。在这项工作中，考虑了几种获得邻接矩阵的方法，并研究了它们的质量。还建议在一个模型中使用多个邻接矩阵。我们使用田纳西州伊士曼进程数据集展示了关于故障诊断任务的最新性能。所提出的图形神经网络的表现优于复发性神经网络的结果。
Timely detected anomalies in the chemical technological processes, as well as the earliest detection of the cause of the fault, significantly reduce the production cost in the industrial factories. Data on the state of the technological process and the operation of production equipment are received by a large number of different sensors. To better predict the behavior of the process and equipment, it is necessary not only to consider the behavior of the signals in each sensor separately, but also to take into account their correlation and hidden relationships with each other. Graph-based data representation helps with this. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. In this work, the possibility of applying graph neural networks to the problem of fault diagnosis in a chemical process is studied. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance. In this work, several methods for obtaining adjacency matrices were considered, as well as their quality was studied. It has also been proposed to use multiple adjacency matrices in one model. We showed state-of-the-art performance on the fault diagnosis task with the Tennessee Eastman Process dataset. The proposed graph neural networks outperformed the results of recurrent neural networks.
