Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

Abstract

In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.

The rise of low-cost sensing for managing air pollution in cities

“…This is because H 2 is highly explosive at concentrations above 4% in atmospheric air. Wan et al (2012) described the application of sensor networks for detecting leaks in natural gas pipelines, in order to overcome the problems of low-recognition efficiency, high false positive and negative rates and poor localisation accuracy. Therefore, sensor networks might be capable of identifying hazardous leaks in industrial and ambient environments in real-time, in order to offer comprehensive surveillance for the enhanced safety of workers and the general public.…”

A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids

“…In the natural gas field, Wan et al [26] have exploited a multi-classifier based on SVM in order to detect and localize leakages. The wavelet decomposition has been exploited to detect the leakage presence, whereas the difference of arrival have been adopted to estimate the leakage position.…”

“…To our knowledge, most of them [19,20,[22][23][24]26] cannot be applied to residential scenarios, where only aggregated flow measurement records are available, i.e., only one sensing point. Specifically, the input data in Nasir et al [20] have been generated by means of the EPANET software [21] to simulate four pressure sensors and two flow sensors.…”

“…Mounce et al [19] have correlated the anomalies in flow and pressure series with operational information and manual data interpretation. Wan et al [26] have adopted a high-pressure experimental circuit with five acoustic and pressure sensors.…”

Proof of Concept of Crack Localization Using Negative Pressure Waves in Closed Tubes for Later Application in Effective SHM System for Additive Manufactured Components

“…As explained in the paper of Wan et al [11], when a leak happens along the pipeline, the fluid density of the leak point declines immediately due to the fluid medium losses and the pressure drops. Then the pressure wave source spreads out from the leak point to both ends of the pipeline.…”