Trajectory-based operation monitoring of transition procedure in multimode process

作     者：Wang, Zhaojing Zheng, Ying Wong, David Shan-Hill 

作者机构：Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Natl Tsing Hua Univ Dept Chem Engn Hsinchu Taiwan 

出 版 物：《JOURNAL OF PROCESS CONTROL》 (工艺过程控制杂志)

年 卷 期：2020年第96卷

页      面：67-81页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 0817[工学-化学工程与技术] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0701[理学-数学] 071101[理学-系统理论] 

基　　金：National of Natural Science Foundation of China key Natural Science Foundation of Hubei, China [2019CFA047] 

主　　题：Transition monitoring Operating faults Transition identification Trajectory-based method 

摘      要：Many continuous industrial processes operate in different steady states with different grades or products. The switching between two steady states is called transition. Transition consists of a series of operation changes that should be carried out in proper order, within certain magnitudes and time region. Since faulty operation may lead to increase in inferior products or even hazard events, monitoring of the transition is desired. In this work, a transition identification and monitoring scheme is proposed based on slow feature analysis. Two monitoring statistics which represent the location of the trajectory and the speed of transition are proposed. Besides, operating faults are generated based on the guidewords of hazard and operability analysis (HAZOP). Using a numerical case and the mode 4-to-2 transition of the Tennessee-Eastman process in which catastrophic failures exist, the effectiveness of the proposed method is validated. In addition to missed detection rate and false alarm rate, two performance indexes known as detection time (DT) and rescue time (RT) are introduced. The advantages of proposed method are benchmarked against the stage-based sub principle component analysis(sub-PCA) and the global preserving statistics slow feature analysis(GSSFA). (C) 2020 Published by Elsevier Ltd.