文献类型：期刊文献

英文题名：Adaptive slow feature analysis-sparse autoencoder based fault detection for time-varying processes

作者：Tan, Shuai[1];Zhou, Xinjin[1];Shi, Hongbo[1];Song, Bing[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：142

外文期刊名：JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS

收录：;EI(收录号：20225113263044);WOS:【SCI-EXPANDED(收录号:WOS:000903982000002)】；

基金：This research is sponsored by the National Natural Science Founda- tion of China (62273147) , Shanghai Natural Science Foundation (22ZR1417000) , National Key Research and Development Program of China (2020YFC1522502, 2020YFC1522505) .

语种：英文

外文关键词：Adaptive process monitoring; Slow feature analysis; Sparse autoencoder; Time-varying process

摘要：Background: Fault detection and diagnosis technology is of great significance for practical industrial processes. Industrial process characteristics change with time due to various reasons such as changing working conditions. This will cause false alarm or missing alarm of process monitoring. Methods: In this paper, an adaptive slow feature analysis (SFA) - sparse autoencoder (SAE) algorithm is proposed to establish an adaptive model for time-varying process monitoring. Model update index is built based on timevarying characteristics extracted using SFA model. Process monitoring index is built based on sparse characteristics extracted using SAE model. Through online adaptive update strategy, updated monitoring model is realized to adapt to the time-varying characteristics of the process. Significant findings: The proposed algorithm has good performance on penicillin fermentation process data set and can realize the task of adaptive process monitoring.