Abstract
The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks.
Highlights
Water provides a material basis for human life and the survival of all living things, and it is an indispensable natural resource needed for the development of human society
This paper proposes a water pipeline monitoring system based on wireless sensor networks and a leakage identification method based on support vector machine (SVM)
To address the high networking power consumption that affects conventional wireless sensor networks, we propose a leakage triggered networking method able to network and perform data transmission from wireless sensor nodes in the vicinity of leakage points, which effectively reduces the network energy consumption and extends its life cycle
Summary
Water provides a material basis for human life and the survival of all living things, and it is an indispensable natural resource needed for the development of human society. In [35], PipeNet, a system based on wireless sensor networks, was proposed It aims to monitor water flow and detect leaks by attaching acoustic and vibration sensors to large bulk-water pipelines and pressure sensors to normal pipelines. This paper proposes a water pipeline monitoring system based on wireless sensor networks and a leakage identification method based on support vector machine (SVM). Based on the differences in the time-frequency features of leakage and non-leakage signals, we propose a leakage detection method that constructs feature matrices by employing the intrinsic mode function, approximate entropy, and principal component analysis (PCA) and that uses SVM as a classifier to identify the leaks. Experimental and simulation results demonstrate that the proposed methods can effectively detect the leakage and prolong the lifetime of the wireless sensor network.
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