Abstract
The acoustic detection was novel method for natural gas pipeline leak. In order to improve accuracy and stability detection system, redundant structures of multiple sensor was necessary. The complex background noise and various working-condition adjective caused uncertainty, inadequacy and inconsistency of acoustic signal. In the process of multisource fusing identification, the high conflict among different sensor signal was inevitable. In this paper, the decision fusion model is built to identify natural gas pipeline leak. The decision fuse algorithm procedure includes signal preprocessing, feature extraction, basic relief assignment by BP neural network and decision fusion utilizing DSmT (Dezert–Smarandache Theory) and PCR5 rule. Experimental results show that the decision fusion model is effective and feasible. The information conflict of among different acoustic sensors is resolved perfectively. The fusion results for 150 group test samples indicate that the accuracy of leak detection reach 94.7 % under the given condition.
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Acknowledgments
The paper supported by Science Foundation of China University of Petroleum, Beijing (No. KYJJ2012-04-25). Supported by Science Foundation of China University of Petroleum, Beijing (No. 2462015YQ0414). Supported by National Science foundation of China (No. 51504274).
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Ye, Y., Wang, J. (2017). Study on Decision Fusion Identity Model of Natural Gas Pipeline Leak by DSmT. In: Balas, V., Jain, L., Zhao, X. (eds) Information Technology and Intelligent Transportation Systems. Advances in Intelligent Systems and Computing, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-319-38771-0_55
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DOI: https://doi.org/10.1007/978-3-319-38771-0_55
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