文献类型：期刊文献

英文题名：Sensor Fault Detection and Diagnosis Using Graph Convolutional Network Combining Process Knowledge and Process Data

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：2023

卷号：72

起止页码：1

外文期刊名：IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

收录：;EI(收录号：20234014841343);WOS:【SCI-EXPANDED(收录号:WOS:001111852400010)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62073141,Grant 62073140, and Grant 62103149; in part by the Shanghai Rising-Star Program under Grant 21QA1401800; in part by the Shanghai Chenguang Project under Grant 21CGA37; and in part by the National Key Research and Development Program of China under Grant 2020YFC1522502 and Grant 2020YFC1522505. The Associate Editor coordinating the review process wasDr. Loredana Cristaldi.

语种：英文

外文关键词：Graph neural networks (GNNs); process monitoring; sensor fault detection; sensor fault isolation

摘要：The condition of sensors is critical to ensure the safe operation and product quality of industrial processes, but fault detection and diagnosis techniques for sensors have received little attention. To alleviate this problem, we introduce a novel deep-learning (DL) framework that combines process knowledge and graph convolutional networks (KDGCNs) for process sensor fault detection and diagnosis. We inject process knowledge into a data-based modeling approach through graph neural networks (GNNs) and use attention mechanisms to model the dependencies between sensors. We implement sensor fault detection using residuals and determine the location of the faulty sensor using a directed graph. Finally, we set up several sensor faults based on the Tennessee Eastman simulation, and the KDGCN shows satisfactory performance in both detection rate and diagnosis results, indicating that the injected knowledge and graph structure help to achieve accurate sensor fault detection and diagnosis.