文献类型：期刊文献

英文题名：Multi-modal hybrid modeling strategy based on Gaussian Mixture Variational Autoencoder and spatial-temporal attention: Application to industrial process prediction

作者：Peng, Haifei[1];Long, Jian[1];Huang, Cheng[1];Wei, Shibo[1];Ye, Zhencheng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China

年份：2024

卷号：244

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20234615071693);WOS:【SCI-EXPANDED(收录号:WOS:001112458400001)】；

基金：This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101) , National Natural Science Fund for Distinguished Young Scholars (Grant No. 61925305) , National Natural Science Foundation of China (Grant No. 61973124, 62373155) , Major Science and Technology Projects of Longmen Laboratory (Grant No. LMZDXM202206) .

语种：英文

外文关键词：Multimode process; Data-driven modeling; Gaussian mixture variational autoencoder; Spatial-temporal attention

摘要：The industrial process is characterized by its multi-modal nature and complex spatial and temporal correlations. Despite the fact that several multi-modal methods have been proposed, few of them can effectively extract deep multi-modal representations and the highly intricate spatial and temporal relationships. In this paper, a novel multi-modal hybrid modeling strategy (GMVAE-STA) is proposed for industrial process prediction. This strategy combines the Gaussian Mixture Variational Autoencoder (GMVAE) and the spatial-temporal attention based Gated Recurrent Unit (STA-GRU). First, the GMVAE maps the raw data to the latent space, which follows a Gaussian Mixture distribution, and the data with the highest probability in each Gaussian are identified as a mode. Then, the STA-GRU captures the complex spatial and temporal relationships within each mode and makes predictions. Experimental results on the Tennessee Eastman process and a real-world fluid catalytic cracking process demonstrate the effectiveness of mode classification and prediction of the proposed method.