于金广. 基于模糊神经网络的成品油管网泄漏检测方法[J]. 油气储运, 2012, 31(10): 733-736. DOI: 10.6047/j.issn.1000-8241.2012.10.003
引用本文: 于金广. 基于模糊神经网络的成品油管网泄漏检测方法[J]. 油气储运, 2012, 31(10): 733-736. DOI: 10.6047/j.issn.1000-8241.2012.10.003
Yu Jinguang. Fuzzy neural network–based product oil pipeline network leak detection method[J]. Oil & Gas Storage and Transportation, 2012, 31(10): 733-736. DOI: 10.6047/j.issn.1000-8241.2012.10.003
Citation: Yu Jinguang. Fuzzy neural network–based product oil pipeline network leak detection method[J]. Oil & Gas Storage and Transportation, 2012, 31(10): 733-736. DOI: 10.6047/j.issn.1000-8241.2012.10.003

基于模糊神经网络的成品油管网泄漏检测方法

Fuzzy neural network–based product oil pipeline network leak detection method

  • 摘要: 针对现行管道泄漏检测系统仅分析单段管道,以致误报率高且存在漏报的问题,从成品油管网全局出发,建立泄漏检测方法,通过引入现场操作信息,实现了辨识压力异常原因的功能。以模糊神经网络作为主要分类方法,以工况调整和负压波出现的位置、时间和变化量等为模糊神经网络的输入,对其进行训练,从而识别负压波出现的原因,以此屏蔽工况调整对管道泄漏检测系统的影响。通过在华北成品油管网鲁皖一期管道的试验应用,验证了该方法的可行性。

     

    Abstract: Current leak detection system of pipeline only works based on an analysis method on individual pipe sections, which often cause a higherfalse alarm rate and report failure. A leak detection method is established to realize the function in mistake identifying, say abnormalpressure, through introduction of site operation information from overall situation of product oil pipeline network. Fuzzy neural networkis taken as a major classification method and the position, time and variation of working condition adjustment, and negative pressure wavetaken as an input of the fuzzy neural network. It is trained to identify causes of negative pressure waves so as to shield impacts of theworking condition adjustment on the pipeline leak detection system. Through applied experiment in Phase 1 Shandong and Anhui Pipeline ofNorth China Product Oil Pipeline Network, the feasibility of the method is verified.

     

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