Abstract
Both long-distance oil and gas pipelines often pass through areas with unstable geological conditions or natural disasters. As a result, they are prone to bending, displacement, and deformation due to the action of an external environmental loading, which poses a threat to the safe operation of pipelines. The in-line inspection method that is based on the implementation of high-precision inertial measurement units (IMU) has become the main means of pipeline bending stress-strain detection technique. However, to address the problems of the inconsistent identification, low identification efficiency, and high misjudgment rate during the application of the traditional manual identification methods, a feature identification approach for the in-line inspected pipeline bending strain based on the employment of an optimized deep belief network (DBN) model is proposed in this work. In addition, our model can automatically learn features from the pipeline bending strain signals and complete classification and identification. On top of that, after the network model was trained and tested by using the actual pipeline bending strain inspection data, the extracted results showed that the model after the implementation of the training process could accurately identify and classify various pipeline features, with an identification accuracy and efficiency of 97.8% and 0.02 min/km, respectively. The high efficiency, elevated accuracy, and strong robustness of our method can effectively improve the in-line inspection procedure of pipelines during the enforcement of a bending strain load.
Highlights
Long-distance oil and gas pipelines are the main transportation means of oil, natural gas, and other energy sources at home and abroad, while their safe, efficient, and reliable operation is an important guarantee for the constant national economic development [1,2].Besides, long-distance oil and gas pipelines pass through a variety of complex environments, where they are often faced with landslides, water damage, ground subsidence, frost heaving and thawing settlement, and other geological disaster effects, resulting in the manifestation of a high strain/stress load for the pipeline [3,4,5]
The repeated inertial measurement unit (IMU) in-line inspection can monitor both the changes and change rate of the pipeline displacement, and timely report the defect points with large changes in pipeline displacement and points with rapid modifications in pipeline displacement to carry out effective monitoring and early warning of the imposed pipeline strain
Thereby, the training efficiency of the deep belief network (DBN) model was significantly improved and the global optimization of the model parameters was realized
Summary
Academic Editors: José Correia, Xin Ma, Xiaoben Liu, Pei Du and Jingwei Cheng and Beilei Ji 4. College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100021, China
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