Artificial Neural Network-based Fault Detection

被引:0
|
作者
Khelifi, Asma [1 ]
Ben Lakhal, Nadhir Mansour [2 ,3 ]
Gharsallaoui, Hajer [1 ]
Nasri, Othman [2 ]
机构
[1] Univ Tunis El Manar, Ecole Natl Ingenieurs Tunis, LARA Automat, BP 37, Tunis 1002, Tunisia
[2] Univ Sousse, Natl Engn Sch Sousse, LATIS Lab, BP 264, Sousse 4023, Erriadh, Tunisia
[3] Clermont Auvergne Univ, Engn Sci Sch Clermont Ferrand, Inst Pascal Lab, Clermont Ferrand, France
关键词
Fault detection; Artificial Neural-Network; Multi-Layer Architecture; residue generation; inverted pendulum; DIAGNOSIS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Undoubtedly, the fault diagnosis role is vital in monitoring technological processes. Regarding the modern technology ever-growing complexity, the research community has spent huge efforts to adapt diagnosis with today's systems requirements. From this sight, the present work develops an intelligent Artificial Neural-Network (ANN)-based diagnosis algorithm. Indeed, the ANN is a widespread technique in the "Artificial Intelligence" area. It is adjusted in this proposal to ensure fault-detection task. As a free-model technique, the suggested method present very promising perspectives and great convenience to a large scale of systems. Otherwise, since it is considered as a typical experimental mechanism, the inverted pendulum (IP) is selected to be our case of study. Instead of using a real IP, a model describing this system is built on Matlab/Simulink. The results of the established fault-detection method have proved its accuracy and high efficiency.
引用
收藏
页码:1017 / 1022
页数:6
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