Research on Photoelectric Equipment Fault Diagnosis System Based on RS and Networks

被引：0

作者：

Chen Pei-bin ^{[1
]}

Ma Yan ^{[1
]}

Yang Hui ^{[1
]}

Li Xin-jian ^{[1
]}

Yan Debin ^{[1
]}

Hou Zhi-bin ^{[1
]}

He You-ming ^{[1
]}

Zahng Wu-rong ^{[1
]}

Lv Chaofang ^{[1
]}

机构：

[1] New Star Inst Appl Technol, He Fei, Peoples R China

来源：

ADVANCES IN APPLIED SCIENCE AND INDUSTRIAL TECHNOLOGY, PTS 1 AND 2 | 2013年 / 798-799卷

关键词：

Rough Sets (RS); networks; fault diagnosis;

D O I：

10.4028/www.scientific.net/AMR.798-799.415

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Rough Sets (RS) networks and expert system theory were applied to the photoelectric equipment fault diagnosis, and the intelligent diagnosis expert system was built. The accuracy and efficiency of photoelectric equipment fault diagnosis were significantly improved by using of the RS and networks. With combined RS theory with networks, photoelectric equipment fault implementation was optimized and simplified during the training of sample set.

引用

页码：415 / 418

页数：4

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