李俊花, 孙昭晨, 崔 莉. 基于BP神经网络原理的长输管道泄漏点定位及其实验研究[J]. 工程力学, 2010, 27(8): 169-173.
引用本文: 李俊花, 孙昭晨, 崔 莉. 基于BP神经网络原理的长输管道泄漏点定位及其实验研究[J]. 工程力学, 2010, 27(8): 169-173.
LI Jun-hua, SUN Zhao-chen, CUI Li. LEAKAGE LOCALIZATION FOR PIPELINES BASED ON BP NEURAL NETWORK AND EXPERIMENTAL VERIFICATION[J]. Engineering Mechanics, 2010, 27(8): 169-173.
Citation: LI Jun-hua, SUN Zhao-chen, CUI Li. LEAKAGE LOCALIZATION FOR PIPELINES BASED ON BP NEURAL NETWORK AND EXPERIMENTAL VERIFICATION[J]. Engineering Mechanics, 2010, 27(8): 169-173.

基于BP神经网络原理的长输管道泄漏点定位及其实验研究

LEAKAGE LOCALIZATION FOR PIPELINES BASED ON BP NEURAL NETWORK AND EXPERIMENTAL VERIFICATION

  • 摘要: 通过对压力梯度法的分析,发现管道泄漏点的定位精度取决于摩阻系数。而传统的摩阻系数确定方法并不适用于实际运行的泄漏管道。鉴于此,该文提出基于BP神经网络预测摩阻系数的泄漏点定位方法。该方法以泄漏点前后的平均流量分别作为BP网路的输入单元预测泄漏点前后的摩阻系数,之后利用压力梯度法求解泄漏点位置。通过管道泄漏的水力模型实验验证了BP神经网络预测摩阻系数的有效性以及该方法应用于管道泄漏点定位的合理性。

     

    Abstract: Research work based on pressure gradient method shows that the location of leakage depends on the friction factor. And the traditional methods using the friction coefficient are not suitable for determining the leakage location of running pipelines. The method to locate leak point is put forward based on BP neural network forecasting friction coefficient. The BP neural network forecasting friction factor is determined from the average flow rate before and after leakage occurrence. Experimental results confirm that this method can effectively forecast the friction coefficient and reasonably locate the leak point of long-distance pipeline.

     

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