Model-based fault diagnosis methods for systems with stochastic process – A survey

被引：1

作者：

Zhao, Zhen ^{[1
]}

Liu, Peter Xiaoping ^{[1
,3
]}

Gao, Jinfeng ^{[2
]}

机构：

[1] School of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou,310018, China

[2] School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou,310018, China

[3] Department of Systems and Computer Engineering, Carleton University, Ottawa,ON,K1S 5B6, Canada

来源：

Neurocomputing | 2022年 / 513卷

基金：

中国国家自然科学基金;

关键词：

Failure analysis - Fault detection - Stochastic models - Stochastic systems;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Model-based methods are widely used for the fault diagnosis of stochastic dynamic systems by simply using the input–output relationship of the system. Despite encouraging results and progress over past decades, developing a reliable and effective diagnostic method for systems with stochastic processes remains a significant challenge. In this paper, a comprehensive review on this topic is performed. Representative main approaches and results are analyzed and compared, and applications in some specific industrial processes are briefly reviewed. The perspectives on current challenges and potential opportunities of future work are discussed, hoping to shed some light on this active topic. © 2022 Elsevier B.V.

引用

页码：137 / 152

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