A new fault detection and diagnosis method for oil pipeline based on rough set and neural network

被引：0
|
作者：
Liu Jinhai [1 ]
Zhang Huaguang [1 ]
Feng Jian [1 ]
Yue Heng [1 ]
机构：
[1] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2007, PT 3, PROCEEDINGS | 2007年 / 4493卷
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposed a new fault-detection method based on the combination of Rough Set (RS) and Artificial Neural Network (ANN), called hybrid fault-detection method based on RS and ANN (HFDMRSNN), which uses RS to reduce parameters of a pipeline system and then uses ANN (three-layer neural network) to form a detection model. This method could detect fault of pipeline not only in stationary status but also in non-stationary status. The efficiency of the HFDMRSNN in detecting fault in real pipeline system is evaluated by an experiment in a long product oil pipeline in Shandong China. From the results, it is observed that the proposed HFDMRSNN is able to identify the status of complex pipeline effectively.
引用
收藏
页码：561 / +
页数：3
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