文献类型：期刊文献

英文题名：Uncorrelated discriminant graph embedding for fault classification

作者：Hu, Zhengwei[1];Peng, Jingchao[1];Zhao, Haitao[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai, Peoples R China

年份：2021

卷号：99

期号：S1

起止页码：S245

外文期刊名：CANADIAN JOURNAL OF CHEMICAL ENGINEERING

收录：;EI(收录号：20211210123184);WOS:【SCI-EXPANDED(收录号:WOS:000630878400001)】；

基金：This work is supported by National Natural Science Foundation of China (61375007) and Basic Research Programs of Science and Technology Commission Foundation of Shanghai (15JC1400600).

语种：英文

外文关键词：discriminant analysis; fault classification; feature extraction; process monitoring

摘要：Recently, graph embedding methods have been successfully used in process monitoring. To improve the discriminant power, a novel supervised graph embedding method, called uncorrelated discriminant graph embedding (UDGE), is proposed. Different from the unsupervised design of locality preserving projection (LPP), UDGE utilizes both the local geometrical structure and label information to construct the similarity between different data points. The "local geometrical structure" means that each data point can be represented as a combination of its neighbours. Due to add the uncorrelated constraint, the extracted features of UDGE are statistically uncorrelated. Uncorrelated attributes are essential for dimension reduction since they contain minimum redundancy. The application of UDGE is evaluated on the Tennessee Eastman process (TEP) benchmark. Experimental results show UDGE can better separate different types of faults and provide more promising fault diagnosis performance. The code of UDGE is released in .