title: 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.
authors.affiliation: School of Instrumentation Science and Opto-Electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China. jwwan@buaa.edu.cn
authors.forename: Jiangwen
authors.initials: J
authors.lastname: Wan
authors.forename: Yang
authors.initials: Y
authors.lastname: Yu
authors.forename: Yinfeng
authors.initials: Y
authors.lastname: Wu
authors.forename: Renjian
authors.initials: R
authors.lastname: Feng
authors.forename: Ning
authors.initials: N
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D003195:Computer Communication Networks / Q000295:instrumentation*; D003198:Computer Simulation; D003657:Decision Making; D004784:Environmental Monitoring / Q000295:instrumentation* / Q000379:methods*; D059206:Natural Gas / Q000032:analysis*; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine
chemical_list: D059206:Natural Gas