Pipeline leak identification method using transient frequency response (TFR) has been researched in the past two decades. To extend this method to a more general water pipeline system with hydraulic uncertainties, this work (1) introduces deep learning (DL) into the TFR-based leak identification framework and (2) develops extended TFR equations in matrix form for DL learning set generation. In this framework, TFR equations are firstly solved in a pre-calibrated hydraulic model of the system to extract frequency response function (FRF) for the training set preparation. Then the simulated FRFs are fed to train fully linear DenseNet (FL-DenseNet) for feature recognition. Finally, the measured FRF of the system is fed to the trained FL-DenseNet to identify a leak to a pipe in the suspected leak area. A study on a hypothetical small system shows that the proposed framework has robustness against uncertainties of friction coefficient, wave speed, and leak flow. A significant advantage is also observed over the existing method with an inaccurate model. Then the framework is applied to a larger network. Over 90% of the synthetic leaks are identified in 5 of the 149 pipes. These results presented in the paper indicate the potential of applying this framework to a water pipeline system.

使用瞬态频率响应（TFR）的管道泄漏识别方法在过去的二十年中得到了研究。为了将这种方法扩展到具有水力不确定性的更一般的水管系统，这项工作 (1) 将深度学习 (DL) 引入基于 TFR 的泄漏识别框架和 (2) 以矩阵形式开发扩展 TFR 方程以生成 DL 学习集. 在该框架中，TFR 方程首先在系统的预校准水力模型中求解，以提取频率响应函数 (FRF) 以准备训练集。然后将模拟的 FRF 输入到训练全线性 DenseNet (FL-DenseNet) 以进行特征识别。最后，系统的测量 FRF 被馈送到经过训练的 FL-DenseNet，以识别可疑泄漏区域中管道的泄漏。对假设小系统的研究表明，所提出的框架对摩擦系数、波速和泄漏流的不确定性具有鲁棒性。与具有不准确模型的现有方法相比，还观察到了显着优势。然后将该框架应用于更大的网络。在 149 根管道中的 5 根中发现了超过 90% 的合成泄漏。论文中提出的这些结果表明了将该框架应用于供水管道系统的潜力。