Plant-wide troubleshooting and diagnosis using dynamic emb e dde d latent feature analysis

作     者：Qin, S. Joe Liu, Yingxiang Dong, Yining 

作者机构：City Univ Hong Kong Sch Data Sci Kowloon Tat Chee Ave Hong Kong Peoples R China City Univ Hong Kong Hong Kong Inst Data Sci Ctr Syst Informat Engn Kowloon Tat Chee Ave Hong Kong Peoples R China Univ Southern Calif Ming Hsieh Dept Elect & Comp Engn Los Angeles CA 90089 USA 

出 版 物：《COMPUTERS & CHEMICAL ENGINEERING》 (计算机与化工)

年 卷 期：2021年第152卷

页      面：107392-107392页

核心收录：

学科分类：0817[工学-化学工程与技术] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Natural Science Foundation of China [U20A201398] City University of Hong Kong 

主　　题：Latent feature learning Dynamic latent variable modeling Reduced dimensional time series Latent variable composite loadings Plant-wide troubleshooting 

摘      要：Plant-wide process data are usually high dimensional with dynamics residing in a reduced dimensional latent space. In this paper, we propose a novel procedure for diagnosing and troubleshooting plant-wide process anomalies using dynamic embedded latent feature analysis (DELFA). To remove the impact of ex-ternal disturbances or exogenous variables, a dynamic inner canonical correlation analysis algorithm with exogenous variables is proposed. Composite loadings and composite weights are derived and applied for diagnosing a feature that is contained in several latent variables. The dynamic embedded latent features are usually related to poor control performance or malfunctioning control instrumentation. The proposed DELFA procedure with dynamic latent scores and composite loadings is applied to two industrial datasets of a chemical plant before and after a troubled control valve was fixed. The case study demonstrates convincingly that latent dynamic features are powerful for troubleshooting of process anomalies and di-agnosing their causes in a plant-wide setting. (c) 2021 Elsevier Ltd. All rights reserved.