A New Fault Detection and Diagnosis Method for Oil Pipeline Based on Rough Set and Neural Network
A New Fault Detection and Diagnosis Method for Oil Pipeline Based on Rough Set and Neural Network
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DOI：
10.1007/978-3-540-72395-0_70
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发表时间：
2007-06
期刊：
影响因子：
--
通讯作者：
Jinhai Liu;Huaguang Zhang;Jian Feng;Heng Yue
中科院分区：
文献类型：
--
作者：
Jinhai Liu;Huaguang Zhang;Jian Feng;Heng Yue
提出了一种基于粗糙集（RS）和人工神经网络（ANN）相结合的故障检测方法--基于粗糙集和人工神经网络的混合故障检测方法（HFDMRSNN）。该方法不仅能检测出管道的稳态故障，而且能检测出管道的非稳态故障。通过在山东某长输成品油管道上的实验，评价了HFDMRSNN在真实的管道系统中的故障检测效率。结果表明，HFDMRSNN能够有效地识别复杂管道的状态。
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.