文献类型：期刊文献

英文题名：Nonlinear process monitoring based on load weighted denoising autoencoder

作者：Zhu, Jiazhen[1];Shi, Hongbo[1];Song, Bing[1];Tao, Yang[1];Tan, Shuai[1];Zhang, Tianqing[2]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Shanghai Shixiang Technol Corp, Ind Big Data Res Ctr, Shanghai 200237, Peoples R China

年份：2021

卷号：171

外文期刊名：MEASUREMENT

收录：;EI(收录号：20205209680187);WOS:【SCI-EXPANDED(收录号:WOS:000614787100006)】；

基金：This research is supported by the National Natural Science Foundation of China (No. 61673173, 61703161); National Natural Science Foundation of Shanghai (No. 19ZR1473200).

语种：英文

外文关键词：Denoising autoencoder; Nonlinear process; Fault detection; Online weighting strategy

摘要：Traditional monitoring methods are trained with normal data and map the process variables into latent variables directly. However, for these methods, the process variables would become intertwined in the latent variables, which results in that the fluctuations of process variables would be submerged in noise or neutralized in latent variables space. In order to address the submergence and neutralization problems, a novel algorithm load weighted denoising autoencoder (LWDAE) is proposed. According to the direction and magnitude of online data, the loading matrix of LWDAE is weighted to highlight the useful information of both training data and online data in latent variables space. In addition, to reduce the effect of noise on weighting matrix, LWDAE modifies the loss function by adding two new regularizations and revises the calculation logic of weighting matrix to consider the successive samples. Case studies of continuous stirred tank reactor demonstrate the effectiveness of LWDAE.