Recently, deep learning becomes increasingly popular in the industrial data analysis field due to its distinguished feature representation capability. As an emerging deep learning technology, transformer network has attracted extensive attention, but its application in industrial fault detection is still not sufficiently exploited. Furthermore, the traditional transformer network mainly focuses on the time series information of the data, while ignoring the spatial characteristic of the data. For this problem, an improved transformer network model with graph attention mechanism (GA-Tran) is proposed and used for complicated process fault detection. Considering the strong coupling property of process variables, the presented method performs joint spatial-temporal learning by mining the data information in views of both time-dimension and variable-dimension. On the one hand, a transformer encoder module equipped with a self-attention mechanism is utilized to capture long-term temporal dependencies, while multi-scale convolution is integrated to abstract short-term time dependencies. On the other hand, a graph attention network is introduced into the transformer network to meticulously analyze the spatial relationships among variables. Considering the topology of the industrial process variables, we use spectral clustering to infer prior topology information, and remove intelligently irrelevant spatial information. Different from the existing transformer models with the anomaly scores as the monitoring indicator, two monitoring statistics based on encoder features and reconstruction errors are constructed for process status monitoring. Studies on the Tennessee Eastman chemical process demonstrate the effectiveness of the proposed GA-Tran method.

中文翻译：

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基于具有图注意力机制的增强变压器网络的复杂过程故障检测

近年来，深度学习因其卓越的特征表示能力在工业数据分析领域越来越受欢迎。变压器网络作为一种新兴的深度学习技术受到了广泛的关注，但其在工业故障检测中的应用还没有得到充分的开发。此外，传统的变压器网络主要关注数据的时间序列信息，而忽略了数据的空间特征。针对该问题，提出了一种带有图注意机制的改进变压器网络模型（GA-Tran），并将其用于复杂过程故障检测。考虑到过程变量的强耦合性，该方法通过从时间维度和变量维度挖掘数据信息来进行时空联合学​​习。一方面，利用配备自注意力机制的变压器编码器模块来捕获长期时间依赖性，同时集成多尺度卷积来抽象短期时间依赖性。另一方面，变压器网络中引入了图注意力网络，以细致地分析变量之间的空间关系。考虑到工业过程变量的拓扑，我们使用谱聚类来推断先验拓扑信息，并智能地去除不相关的空间信息。与现有以异常分数作为监测指标的变压器模型不同，构建了基于编码器特征和重构误差的两种监测统计量来进行过程状态监测。对田纳西州伊士曼化学工艺的研究证明了所提出的 GA-Tran 方法的有效性。