标题：A survey on model-based fault diagnosis for linear discrete time-varying systems
作者：Zhong, Maiying; Xue, Ting; Ding, Steven X.
作者机构：[Zhong, Maiying] Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China.; [Xue, Ting; Ding, Steven X.] Univ Duisbur 更多
通讯作者地址：[Zhong, MY]Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China.
关键词：Fault diagnosis; Model-based; Linear discrete time-varying
摘要：To meet the rising demands for safety and reliability of modern industrial control systems, the modelbased fault diagnosis problem has attracted much attention in the past few decades both from the academic communities and in practical applications. Among the rich body of literature, the majority of the results are proposed for linear time invariant systems despite the fact that most practical processes are inherently time-varying by nature and discretized for online implementations. Recently, with the everincreasing system complexity and scale, the model-based fault diagnosis issue for linear discrete timevarying ( LDTV) systems has become a hot research topic of both theoretical importance and practical significance, and a great number of results have been reported with many open problems deserving further investigation. To reflect the latest progress in this emerging research branch, this survey aims to provide a timely reference to readers interested in this topic. More specifically, in this survey, the techniques for model-based fault diagnosis for LDTV systems are classified into observer-based methods, parity space-based approaches and parameter estimation schemes, which constitute the three main parts of this survey. The background and the latest progress for each class of these techniques are comparatively discussed with particular focuses on the fault detection and the fault estimation issues. Additionally, an overview on their practical applications to specific industrial plants is provided. Furthermore, the possible future research directions concerning the model-based fault diagnosis for LDTV processes are pointed out, followed by comprehensive concluding remarks. (C) 2018 Elsevier B.V. All rights reserved.