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.

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The non-dispersive infrared absorption spectrum method is one of the important sulfur dioxide concentration measurement method. If the soot particles attach on the protection windows surface, the original measurement light intensity is attenuated because of the absorption and scattering of the soot particle and the sulfur dioxide concentration measurement accuracy is decreased. The protection window pollution question caused by the monodispersion soot particles is discussed in the paper. The numerical simulation results show that the sulfur dioxide concentration measurement accuracy is related to the protection window pollution. The solution method is brought out and the measurement accuracy can be improved.