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Journal of Sensors
Volume 2016 (2016), Article ID 8363242, 7 pages
Research Article

Pipeline Bending Strain Measurement and Compensation Technology Based on Wavelet Neural Network

1School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
2PetroChina Pipeline Company, Langfang 065000, China
3Institute of Disaster Prevention, Langfang 065000, China

Received 4 January 2015; Accepted 16 June 2015

Academic Editor: Gyuhae Park

Copyright © 2016 Rui Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The bending strain of long distance oil and gas pipelines may lead to instability of the pipeline and failure of materials, which seriously deteriorates the transportation security of oil and gas. To locate the position of the bending strain for maintenance, an Inertial Measurement Unit (IMU) is usually adopted in a Pipeline Inspection Gauge (PIG). The attitude data of the IMU is usually acquired to calculate the bending strain in the pipe. However, because of the vibrations in the pipeline and other system noises, the resulting bending strain calculations may be incorrect. To improve the measurement precision, a method, based on wavelet neural network, was proposed. To test the proposed method experimentally, a PIG with the proposed method is used to detect a straight pipeline. It can be obtained that the proposed method has a better repeatability and convergence than the original method. Furthermore, the new method is more accurate than the original method and the accuracy of bending strain is raised by about 23% compared to original method. This paper provides a novel method for precisely inspecting bending strain of long distance oil and gas pipelines and lays a foundation for improving the precision of inspection of bending strain of long distance oil and gas pipelines.