自动化学报2025，Vol.51Issue(5)：1118-1130,13.DOI:10.16383/j.aas.c240627
基于质量关联虚拟变量的质量相关变量划分及故障检测
Quality-related Variable Division and Fault Detection Based on Quality-related Virtual Variable
摘要
Abstract
Quality-related fault detection,as an important research content in data-driven multivariate statistical process monitoring,is a key technology for ensuring the safe and efficient operation of complex equipments or indus-trial processes.Determining or dividing quality-related variables is a core aspect of this method.Existing quality-re-lated fault detection methods typically rely heavily on quality variables,and their effectiveness is severely chal-lenged when quality variables are unmeasurable.To address this challenge,a quality-related variable division meth-od based on quality-related virtual variable(QRV)is proposed.An independent component analysis(ICA)-based model for quality-related fault detection is established using this division method,and an application study on fault detection is conducted.First,a QRV is constructed to indirectly reflect the quality characteristics of systems;Second,based on this QRV,process variables are divided into quality-related and quality-unrelated variable groups by hypothesis testing;Subsequently,this division results are applied to the ICA-based quality-related fault detec-tion,utilizing exponentially weighted moving average(EWMA)to modify statistics and construct comprehensive detection indices;Finally,the feasibility and effectiveness of the proposed method are verified through numerical simulations and Tennessee-Eastman process(TEP)experiments.关键词
工业过程/质量相关变量/变量划分/质量关联虚拟变量/故障检测指标Key words
Industrial processes/quality-related variable/variable division/quality-related virtual variable(QRV)/fault detection indices引用本文复制引用
刘美枝,孔祥玉,胡昌华..基于质量关联虚拟变量的质量相关变量划分及故障检测[J].自动化学报,2025,51(5):1118-1130,13.基金项目
国家自然科学基金(62273354,62227814),山西省高等学校科技创新项目(2022L434)资助Supported by National Natural Science Foundation of China(62273354,62227814)and Scientific Innovation Foundation of the Higher Education Institutions of Shanxi Province(2022L434) （62273354,62227814）
