文献类型：期刊文献

英文题名：Distributed monitoring of nonlinear plant-wide processes based on GA-regularized kernel canonical correlation analysis

作者：Jin, Wenhao[1];Wang, Wenjing[1];Wang, Yang[2];Cao, Zhixing[1];Jiang, Qingchao[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Shanghai Dianji Univ, Sch Elect Engn, Shanghai 200240, Peoples R China

年份：2024

卷号：252

外文期刊名：RELIABILITY ENGINEERING & SYSTEM SAFETY

收录：;EI(收录号：20243516943743);WOS:【SCI-EXPANDED(收录号:WOS:001307009800001)】；

语种：英文

外文关键词：System safety; Data-driven fault diagnosis; Nonlinear plant-wide processes; Kernel canonical correlation analysis

摘要：Fault detection and diagnosis is important for ensuring process safety and is gaining increasing attention in the system safety field. A regularized kernel canonical correlation analysis (RKCCA) approach is proposed for monitoring nonlinear plantwide processes. For each local unit, genetic algorithm (GA)-based regularization is performed to determine the communication variables from neighboring units, which preserves the maximum correlations and eliminates the irrelevant variables. Then variables from a local unit and the communication variables are mapped into high-dimensional feature spaces, and the feature space of the local unit is decomposed into three orthogonal subspaces, namely the residual subspace, the inner subspace, and the outer-related subspace. Monitoring statistics to identify both the process status and the characteristic of a detected fault are constructed. The proposed RKCCA-based monitoring method considers both the information of a local unit and the beneficial information of related units to facilitate fault detection, thereby exhibiting superior performance to some state-of-the-arts methods. Applications on the Tennessee Eastman benchmark process and an industrial tail gas treatment process demonstrate the superiority of RKCCA monitoring.