Complex system fault diagnosis based on the improved neural network and multi-expert system

被引：0

作者：

Xiong Xiaolong ^{[1
]}

Wang Jie ^{[1
]}

Wu Wenzhou ^{[1
]}

Niu Tianlin ^{[1
]}

Huo Liang ^{[1
]}

机构：

[1] AFEU Missile Inst, Postgrad Team 2, Sanyuan 713800, Shanxi, Peoples R China

来源：

ISTM/2007: 7TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1-7, CONFERENCE PROCEEDINGS | 2007年

关键词：

intelligent fault diagnosis; artificial neural network; multi-expert system; complex system;

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：

In order to make a diagnosis and obviate the fault faster and better when complex system goes wrong, this paper analyzed the characteristic and limitation of the traditional artificial neural network and expert system, applied the way of associating a kind of improved artificial neural network and multi-expert system, and built fault diagnosis mechanism based on the neural network and expert system to make an intelligent diagnosis for the faults of complex system. This method overcame the insufficiency of traditional expert system such as lack of the mechanism of effective self-study and self-adapt, be difficult to resolve the nonlinear system fault problems. Therefore, it is a convenient way to resolve the multi-sign and multi-reason fault Problems of complex system.

引用

页码：2304 / 2307

页数：4