文献类型：期刊文献

英文题名：Process fault root cause diagnosis through state evolution mapping based on temporal unit shapelets

作者：Yu, Zhenhua[1];Wang, Guan[2];Jiang, Qingchao[1];Yan, Xuefeng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China

年份：2025

卷号：84

起止页码：96

外文期刊名：CHINESE JOURNAL OF CHEMICAL ENGINEERING

收录：;EI(收录号：20253218949081);WOS:【SCI-EXPANDED(收录号:WOS:001558649000005)】；

基金：The authors gratefully acknowledge the support from the following foundations: the National Natural Science Foundation of China (62322309, 62433004) , Shanghai Science and Technology Innovation Action Plan (23S41900500) , and Shanghai Pilot Program for Basic Research (22TQ1400100-16) .

语种：英文

外文关键词：Root cause diagnosis; Neural networks; Shapelet; Fermentation; Bioprocess

摘要：Accurate fault root cause diagnosis is essential for ensuring stable industrial production. Traditional methods, which typically rely on the entire time series and overlook critical local features, can lead to biased inferences about causal relationships, thus hindering the accurate identification of root cause variables. This study proposed a shapelet-based state evolution graph for fault root cause diagnosis (SEGRCD), which enables causal inference through the analysis of the important local features. First, the regularized autoencoder and fault contribution plot are used to identify the fault onset time and candidate root cause variables, respectively. Then, the most representative shapelets were extracted to construct a state evolution graph. Finally, the propagation path was extracted based on fault unit shapelets to pinpoint the fault root cause variable. The SEG-RCD can reduce the interference of noncausal information, enhancing the accuracy and interpretability of fault root cause diagnosis. The superiority of the proposed SEG-RCD was verified through experiments on a simulated penicillin fermentation process and an actual one. (c) 2025 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.