Abstract Leakages in pipelines affect the reliability of fluid transport systems causing environmental damages, economic losses, and pressure reduction at the delivery points. Therefore, this paper presents a methodology to detect and locate water leaks in pipelines by using artificial neural networks (ANN) techniques and online measurements of pressure and flow rate. Contrary to reported works in the literature, the proposed method estimates the friction factor of the pipe and uses this information as an input to compute the leak position. Data generated from a validated numerical simulator was used to enrich the data-training set for the ANN. Various leak scenarios were considered to characterize pressure losses and their differentials in different sections of the pipeline. Finally, the algorithm was tested experimentally in a pilot plant. The results demonstrate good performance and the applicability of the proposed method.

中文翻译：

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使用组合人工神经网络方法进行管道泄漏诊断

摘要 管道泄漏影响流体输送系统的可靠性，造成环境破坏、经济损失和输送点压力降低。因此，本文提出了一种通过使用人工神经网络 (ANN) 技术和压力和流量的在线测量来检测和定位管道漏水的方法。与文献中报道的工作相反，所提出的方法估计管道的摩擦系数，并使用此信息作为输入来计算泄漏位置。从经过验证的数值模拟器生成的数据用于丰富 ANN 的数据训练集。考虑了各种泄漏情况来表征管道不同部分的压力损失及其差异。最后，该算法在中试工厂中进行了实验测试。