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

页码：561 / +

页数：3

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