文献类型：期刊文献

英文题名：A knowledge-driven spatial-temporal graph neural network for quality-related fault detection

作者：Guo, Lei[1];Shi, Hongbo[1];Tan, Shuai[1];Song, Bing[1];Tao, Yang[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2024

卷号：184

起止页码：1512

外文期刊名：PROCESS SAFETY AND ENVIRONMENTAL PROTECTION

收录：;EI(收录号：20241315816288);WOS:【SCI-EXPANDED(收录号:WOS:001224597400001)】；

基金：The authors gratefully acknowledge support from the National Natural Science Foundation of China (No. 62073140, No. 62073141, No. 62103149, No. 62273147) and in part by the Shanghai Rising-Star Program (No. 21QA1401800) and the Shanghai Natural Science Foundation (No. 22ZR1417000)

语种：英文

外文关键词：Quality indicator; Process monitoring; Graph attention networks; Spatial-temporal characteristics

摘要：The majority of quality -related fault detection methods focused on process statistics, neglecting the spatialtemporal characteristics of variables and the physical information of the process. This research presents a Knowledge -driven Spatial -Temporal Graph Attention Neural Network (K-STGAT) to address the instability of quality -related fault detection due to the existing dynamic and spatial -temporal correlations. First, the measurable variables in the process are transformed into topological graphs based on prior knowledge. Second, construct a spatial -temporal adjacency matrix and spatial -temporal sequence. Third, the sign attention mechanism is introduced into the graph attention networks to capture the propagation patterns of variables in both temporal and spatial dimensions. Finally, the model is trained by reconstructing the variables, and the qualityrelated variables are selected to construct a quality regression model to obtain the quality -related space and construct the detection statistic. The effectiveness and soundness of the proposed approach are demonstrated through its application to the Tennessee Eastman process.