集成技术2026，Vol.15Issue(3)：64-81,18.DOI:10.12146/j.issn.2095-3135.20251020001
基于Transformer的复杂工业过程质量预测方法综述
Transformer-Based Quality Prediction Methods for Complex Industrial Processes:A Review
摘要
Abstract
Quality prediction modeling based on deep learning has become one of the mainstream methods in the field of soft sensor,it can more effectively address complex problems such as nonlinearity and high-dimensionality compared to traditional statistical analysis and shallow machine learning.Transformer,a deep learning model based on the self-attention mechanism,offers a new research avenue for industrial quality prediction due to its unique advantages in sequence modeling.This paper systematically reviews the technological evolution and applications of Transformer in the industrial process quality prediction.Initially,the limitations of classic deep learning models in dynamic industrial processes are discussed.By analyzing the ability of the Transformer model's self-attention mechanism to model dynamic global features of industrial processes,its fundamental application principles in industrial process soft sensor problems are highlighted.Subsequently,the main improvement directions of the classic Transformer method in existing literature,including Crossformer,iTransformer,Informer,PatchTST,and Autoformer,are summarized,along with their application cases in complex real-world industrial processes.Finally,current challenges in industrial quality prediction are discussed,and future research directions are prospected.关键词
工业过程/深度学习/Transformer/质量预测/软测量Key words
industrial processes/deep learning/Transformer/quality prediction/soft sensor分类
信息技术与安全科学引用本文复制引用
王隆燕,周文鑫,叶凌箭..基于Transformer的复杂工业过程质量预测方法综述[J].集成技术,2026,15(3):64-81,18.基金项目
国家自然科学基金项目(62373147) （62373147）
浙江省自然科学基金项目(LY24F030007) This work is supported by National Natural Science Foundation of China(62373147),Zhejiang Provincial Natural Science Foundation of China(LY24F030007) （LY24F030007）
