Finite Memory Observers for linear time-varying systems: Theory and diagnosis applications

作     者：Graton, Guillaume Kratz, Frederic Fantini, Jacques 

作者机构：Ecole Cent Marseille F-13451 Marseille 20 France Ecole Cent Marseille Lab Informat Sci & Syst F-13451 Marseille 20 France ENSI Bourges F-18020 Bourges France ENSI Bourges PRISME Lab F-18020 Bourges France Univ Orleans Polytech Orleans F-45067 Orleans France Polytech Orleans PRISME Lab F-45067 Orleans France 

出 版 物：《JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS》 (佛兰克林学会杂志)

年 卷 期：2014年第351卷第2期

页      面：785-810页

核心收录：

学科分类：0808[工学-电气工程] 07[理学] 08[工学] 0701[理学-数学] 0811[工学-控制科学与工程] 

主　　题：TIME-varying systems ROBUST control SENSITIVITY analysis FAULT diagnosis (Chemical engineering) PRODUCTION engineering ARTIFICIAL intelligence 

摘      要：Fault detection and diagnosis are important issues in process engineering. Hence, a considerable interest exists in this field now from industrial practitioners as well as academic researchers, as opposed to 30 years ago. The literature on process fault diagnosis, ranging from analytical methods to artificial intelligence and statistical approaches, is largely widespread. In this paper, the modeling of the real process is known, and the state-space representation is used The properties of the Finite Memory Observer (FM()) are studied from a global point of view for the class of linear time-varying (LTV) systems with stochastic noises. The FMO performances are framed by the study of their properties, and that of their influences on diagnosis results. Fundamentally, the generation of residuals is an essential procedure in diagnosis. So, the determination of the optimal window length of the observer is resolved, and the generation of residuals for diagnosis completed. In the first part, the design of the observer and the residual generation are shown. The second part is devoted to the study of the sensitivity and robustness of the observer and of residuals generated from the observer. (C) 2013 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.