Intermittent Fault Detection for Nonlinear Stochastic Systems

被引:0
|
作者
Niu, Yichun [1 ]
Sheng, Li [1 ]
Gao, Ming [1 ]
Zhou, Donghua [2 ]
机构
[1] China Univ Petr East China, Coll Control Sci & Engn, Qingdao 266580, Shandong, Peoples R China
[2] Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China
来源
IFAC PAPERSONLINE | 2020年 / 53卷 / 02期
基金
中国国家自然科学基金;
关键词
Intermittent fault detection; Nonlinear stochastic systems; Moving horizon estimation; Dynamic weight matrices; Particle swarm optimization;
D O I
10.1016/j.ifacol.2020.12.817
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, the problem of intermittent fault detection is investigated for nonlinear stochastic systems. The moving horizon estimation with dynamic weight matrices is proposed, where the weight matrices are adjusted by an unreliability index of prior estimate to avoid the smearing effects of intermittent faults. Based on the particle swarm optimization algorithm, the nonlinear optimization problem is solved and the approximate estimate is derived. Finally, the feasibility and effectiveness of the proposed algorithm are validated by a numerical example. Copyright (C) 2020 The Authors.
引用
收藏
页码:694 / 698
页数:5
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