This paper presents an acoustic leak detection system for distribution water mains using machine learning methods. The problem is formulated as a binary classifier to identify leak and no-leak cases using acoustic signals. A supervised learning methodology has been employed using several detection features extracted from acoustic signals, such as power spectral density and time-series data. The training and validation data sets have been collected over several months from multiple cities across North America. The proposed solution includes a multi-strategy ensemble learning (MEL) using a gradient boosting tree (GBT) classification model, which has performed better in maximizing detection rate and minimizing false positives as compared with other classification models such as KNN, ANN, and rule-based techniques. Further improvements have been achieved using a multitude of GBT classifiers combined in a parallel ensemble method called bagging algorithm. The proposed MEL approach demonstrates a significant improvement in performance, resulting in a reduction of false positives reports by an order of magnitude.

本文提出了一种使用机器学习方法的用于配水总管的声泄漏检测系统。该问题被公式化为使用声音信号识别泄漏和无泄漏情况的二进制分类器。使用了一种监督学习方法，该方法使用了从声音信号中提取的几种检测特征，例如功率谱密度和时间序列数据。培训和验证数据集是在过去几个月中从北美多个城市收集的。提出的解决方案包括使用梯度提升树（GBT）分类模型的多策略集成学习（MEL），与其他分类模型（例如KNN，ANN和规则）相比，该方法在最大化检测率和最小化误报方面表现更好。基础的技术。使用在称为装袋算法的并行集成方法中组合的多个GBT分类器，可以实现进一步的改进。拟议的MEL方法证明了性能的显着提高，从而将误报报告减少了一个数量级。