Earticle

초록

영어

For natural gas pipeline, it has a leak or not is critical. The most commonly problems in the pipeline leak detection methods are the difficulties to identify, inaccuracy to locate, thus, the natural gas pipeline detection is difficult to be applied, therefore, the use of neural network multi-sensor data fusion of the natural gas pipeline leak detection is particularly important. In this paper, the method is proposed based on RBF neural network and the data fusion of D-S evidence theory for detecting the pipeline leak. Extracting neural network's input parameters through wavelet denoising, then substitute them into neural network and calculate them by multi-sensor data fusion algorithm so as to acquire leaking information.

목차

Abstract
 1. Introduction
 2. Signal Preprocessing based on Wavelet Transform
 3. Leakage Signal Identification based on RBF Neural Network
  3.1. Artificial Neural Network
  3.2. Radial-Basis Function Network
  3.3. The Signal Identifying Type based on RBF Neural Network
 4. Leak Detection based on multi-sensor data fusion
  4.1. The Combination Rule of Evidence Theory
  4.2. The Recursive Target Identification Fusion of Incompatible Data Structure
  4.3 Experimental Simulation
 5. Conclusion
 References

키워드

저자

• Bingkun Gao [ School of Electrical Engineering & Information Northeast Petroleum University, DaQing, China ]
• Guojun Shi [ School of Electrical Engineering & Information Northeast Petroleum University, DaQing, China, School of Information Technology Heilongjiang BaYi Agricultural University, DaQing, China ]
• Qing Wang [ School of Electrical Engineering & Information Northeast Petroleum University, DaQing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605