文献类型：期刊文献

英文题名：Soft sensors for industrial fault detection using multi-scale fusion temporal convolutional autoencoders

作者：Sun, Huanqi[1];Xiong, Weili[1];Li, Zhongmei[2,3];Sun, Wenxin[4];Chen, Yiyang[5];Chen, Hongtian[4]

机构：[1]Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, State Key Lab Ind Control Technol, Shanghai 200237, Peoples R China;[4]Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China;[5]Soochow Univ, Sch Mech & Elect Engn, Suzhou 215137, Peoples R China

年份：2026

卷号：174

起止页码：276

外文期刊名：ISA TRANSACTIONS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001795262100003)】；

语种：英文

外文关键词：Soft sensors; Temporal convolutional networks; Fault detection; Filter response normalization; Autoencoders

摘要：With the widespread deployment of intelligent sensors and advances in data storage, large volumes of process data are continuously collected, providing a foundation for developing soft sensors for multi-scale monitoring in complex industrial processes. This paper proposes an enhanced autoencoder-based temporal convolutional soft sensor model for industrial process monitoring, aiming to effectively capture multi-scale features and the dynamic evolution of process data. The proposed filter temporal convolutional network incorporates adaptive filter-response normalization, thereby enhancing multi-scale feature extraction and improving model generalization. Then, a multi-layer filter temporal convolutional autoencoder is developed to enable efficient multi-scale feature extraction and accurate process data reconstruction. Moreover, a multi-scale feature fusion module with a channel attention mechanism is designed to adaptively integrate temporal features and significantly enhance model robustness. Finally, a statistical metric based on reconstruction errors is established, and the Kullback-Leibler divergence is employed to determine control limits for fault detection. The superiority and effectiveness of the proposed method are validated through applications to the wastewater treatment process and the multiphase flow process.