A Gas Pipeline Leakage Diagnosis of Fusing BP Neural Network Basing on WSN and D-S Theory

Abstract

For reasons of low accuracy of artificial survey leakage, a gas pipeline leakage diagnosis method based on BP neural networks and D-S theory is presented by introducing WSN and information fusion theory. Two sub-neural networks are established at normal node to simplify network structure. The leakage characteristic parameters of negative pressure wave and acoustic emission signals are used as input eigenvector respectively for primary diagnosis. Through making preliminary fusion result s as the basic probability assignment of evidence, the impersonal valuations are realized. Finally, all evidences are aggregated at normal and sink node respectively by using the improved combination rules. The method makes full use of redundant and complementary leakage information. Numerical example shows that the proposed improves the leakage diagnosis accuracy and decreases the recognition uncertainty.

Analysis of Possibility of Phase Shift Model Usage for Identification of Gas Leakage from Pipeline with Corrective Elements

“…Można spośród nich wyróżnić metody akustyczne [2,23], obserwacje bezpośrednie (np. termowizyjne, wykorzystanie tresowanych psów) [21,23], obserwacje ciśnień i przepływów [3,4,5,8,11,12,18,20,22,23], wykorzystanie metod komputerowych (sieci neuronowe, filtry Kalmana) [1,9,10,17,23,25]. Wszystkie te metody mają swoje słabe i mocne strony.…”

“…Among them could be distinguished: acoustic methods [2,23], direct observations (e.g. thermovision, use of trained dogs) [21,23], pressure and flow observations [3,4,5,8,11,12,18,20,22,23], use of computer methods (neural networks, Kalman filters) [1,9,10,17,23,25].…”

Node Deployment Optimization for Wireless Sensor Networks Based on Virtual Force-Directed Particle Swarm Optimization Algorithm and Evidence Theory

“…The Zigbee network was used to monitor the environmental parameters of aquiculture water [ 28 ]. Because artificial surveys have low accuracy, a gas pipeline leakage diagnosis system based on BP neural networks and D-S theory was presented by introducing WSN and information fusion theory [ 29 ]. Zhao et al [ 30 ] proposed a novel fleet deployment approach based on route risk evaluation to fully use navigation resources while reducing risks.…”