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.
Highlights
In the present age, natural gas has become one of the most important energy resources in the World, which primarily depends on the pipeline transportation
The accuracy of spectral analysis mainly depends on the eigenvalue problem, which can be solved by a filter diagonalization method (FDM) [24,25]
In this paper we study the features of leak monitoring information and the structure of pipeline monitoring sensor networks, and present a type of pipeline leak detection and localization method based on a hierarchical model
Summary
Natural gas has become one of the most important energy resources in the World, which primarily depends on the pipeline transportation. Imad et al [12] presented a model of in-network information processing for pipeline monitoring based on WSNs, but did not give any specific algorithms. This is mainly because monitoring information shows uncertainty and diversity in expressive form, huge amounts in numbers and complicated relationships [11]. There has not been a kind of method which can effectively process monitoring information of natural gas pipelines This mainly reflects the following aspects [13,14]: in the first place, under urban area circumstances, sensors may be affected by background noise so that detection information has great ambiguity.
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