在大型配水系统中定位泄漏是一个重要且始终存在的问题。由于水管网的复杂性,传感器太少,和嘈杂的测量,这是一个非常具有挑战性的问题。在这项工作中,我们提出了一种基于生成深度学习和贝叶斯推理的方法,用于不确定性量化的泄漏定位。生成模型,利用深度神经网络,作为替代完整方程的概率代理模型,同时也包含了此类模型固有的不确定性。通过将这个代理模型嵌入到贝叶斯推理方案中,通过将传感器观测值与近似可能泄漏位置的真实后验分布的模型输出相结合来定位泄漏。我们证明了我们的方法能够快速生产,准确,和值得信赖的结果。它在增加复杂性的三个问题上表现出令人信服的性能。对于一个简单的测试用例,河内网络,在传感器数量和测量噪声水平不同的情况下,预测泄漏位置和真实泄漏位置之间的平均拓扑距离(ATD)范围为0.3至3。对于两个更复杂的测试用例,ATD的范围分别为0.75至4和1.5至10。此外,准确率高达83%,72%,在三个测试案例中实现了42%,分别。计算时间从0.1到13s,取决于所采用的神经网络的大小。这项工作是数字孪生的一个例子,用于先进的数学和深度学习技术在泄漏检测领域的复杂应用。
Localizing leakages in large water distribution systems is an important and ever-present problem. Due to the complexity originating from water pipeline networks, too few sensors, and noisy measurements, this is a highly challenging problem to solve. In this work, we present a methodology based on generative deep learning and Bayesian inference for leak localization with uncertainty quantification. A generative model, utilizing deep neural networks, serves as a probabilistic surrogate model that replaces the full equations, while at the same time also incorporating the uncertainty inherent in such models. By embedding this surrogate model into a Bayesian inference scheme, leaks are located by combining sensor observations with a model output approximating the true posterior distribution for possible leak locations. We show that our methodology enables producing fast, accurate, and trustworthy results. It showed a convincing performance on three problems with increasing complexity. For a simple test case, the Hanoi network, the average topological distance (ATD) between the predicted and true leak location ranged from 0.3 to 3 with a varying number of sensors and level of measurement noise. For two more complex test cases, the ATD ranged from 0.75 to 4 and from 1.5 to 10, respectively. Furthermore, accuracies upwards of 83%, 72%, and 42% were achieved for the three test cases, respectively. The computation times ranged from 0.1 to 13 s, depending on the size of the neural network employed. This work serves as an example of a digital twin for a sophisticated application of advanced mathematical and deep learning techniques in the area of leak detection.