文献类型：期刊文献

英文题名：Relevance variable selection variational auto-encoder network for quality-related nonlinear process monitoring

作者：Ma, Yao[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：154

外文期刊名：APPLIED SOFT COMPUTING

收录：;EI(收录号：20240815569049);WOS:【SCI-EXPANDED(收录号:WOS:001182859000001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62073140, Grant 62073141, Grant 62103149, and Grant 62273147; in part by the National Key Research and Development Program of China under Grant 2020YFC1522502 and Grant 2020YFC1522505.

语种：英文

外文关键词：Variable selection; Fault variable localization; Quality-related; Process monitoring; Nonlinear

摘要：Quality -related process monitoring is essential for revealing changes in product quality and ensuring industrial safety. Therefore, it is crucial to distinguish enough quality -related features within the data. To learn the nonlinear characteristics and obtain quality -related features in process data, a novel process monitoring method named relevance variable selection variational auto -encoder (RVS-VAE) is proposed. Firstly, to enhance the strong quality -related variables and learn the time correlation, the normalized original data is weighted by mutual information and augmented into data matrices. Secondly, the RVS strategy is proposed to select the most quality -related variables from the latent features. These quality -related features are further utilized to monitor the process. What is more, when a fault is detected, relevance variable relative contribution (RVRC) method is presented to locate the fault variables. Finally, performance of the proposed RVS-VAE method is evaluated through comparative experiments on tennessee eastman (TE) process.