化工进展2026，Vol.45Issue(7)：3933-3939,7.DOI:10.16085/j.issn.1000-6613.2025-1295
基于潜空间协整自编码的化工过程特征提取与监测
Chemical process feature extraction and monitoring method based on latent cointegration autoencoder
邵蔚 1饶景之 1纪成 2王璟德 1孙巍1
作者信息
- 1. 北京化工大学化学工程学院,北京 100029
- 2. 北京化工大学化学工程学院,北京 100029||淮阴师范学院化学化工学院,江苏 淮安 223300
- 折叠
摘要
Abstract
Modern chemical processes often exhibit both significant nonlinearity and dynamic non-stationarity due to their complex internal mechanisms.Traditional single model approaches struggle to fully characterize these properties for effective process monitoring.To address this challenge,a process monitoring method that integrates convolutional autoencoder(CAE)and cointegration analysis(CA)was proposed.The method first utilized the nonlinear feature extracting capability of CAE to map high-dimensional raw data into a low-dimensional latent space,which represented the core process dynamics.Subsequently,CA was applied to these latent features to capture the long-term equilibrium relationships among variables.Finally,the feasibility of the method was verified by real industrial production data.The results showed that the proposed method achieved a fault detection rate(FDR)of 99.53％with a false alarm rate(FAR)of only 0.55％.Its overall performance significantly surpassed that of standalone methods such as CAE or CA,which also proved that the method could provide an effective solution with high sensitivity and high reliability for the safety monitoring of complex industrial processes.关键词
过程系统/协整分析/神经网络/控制/故障早期识别Key words
process systems/cointegration analysis/neural networks/control/early fault identification分类
信息技术与安全科学引用本文复制引用
邵蔚,饶景之,纪成,王璟德,孙巍..基于潜空间协整自编码的化工过程特征提取与监测[J].化工进展,2026,45(7):3933-3939,7.
