Multiple sensor fault diagnosis by evolving data-driven approach

被引：26
|
作者：
El-Koujok, M. [1 ]
Benammar, M. [1 ]
Meskin, N. [1 ]
Al-Naemi, M. [1 ]
Langari, R. [2 ]
机构：
[1] Qatar Univ, Dept Elect Engn, Doha, Qatar
[2] Texas A&M Univ, Dept Mech Engn, College Stn, TX 77843 USA
来源：
INFORMATION SCIENCES | 2014年 / 259卷
关键词：
Sensor fault diagnosis; Data-driven approach; Nonlinear system; FUZZY; IDENTIFICATION; SYSTEMS;
D O I：
10.1016/j.ins.2013.04.012
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Sensors are indispensable components of modern plants and processes and their reliability is vital to ensure reliable and safe operation of complex systems. In this paper, the problem of design and development of a data-driven Multiple Sensor Fault Detection and Isolation (MSFDI) algorithm for nonlinear processes is investigated. The proposed scheme is based on an evolving multi-Takagi Sugeno framework in which each sensor output is estimated using a model derived from the available input/output measurement data. Our proposed MSFDI algorithm is applied to Continuous-Flow Stirred-Tank Reactor (CFSTR). Simulation results demonstrate and validate the performance capabilities of our proposed MSFDI algorithm. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码：346 / 358
页数：13
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