Research on Technology of Gas Pipeline Leakage Detection Based on Infrasonic Wave

Wang, Yong Qing; Wan, Shun; Zhou, Ying; Yang, Hong

doi:10.4028/www.scientific.net/amm.401-403.1106

Cited by 8 publications

(4 citation statements)

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“…According to [31], the leak hole diameter and the leak flow rate have a linear fitting relationship, where U is the internal flow velocity, D is the leak hole diameter, L 1 and L 2 are empirical coefficients,…”

Section: Theory Of Pressure Fluctuation and Leakage Aperturementioning

confidence: 99%

Application of pipeline leakage detection based on distributed optical fiber acoustic sensor system and convolutional neural network

Duan,

Liang,

Tong

et al. 2023

J. Phys. D: Appl. Phys.

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Underwater pipelines are exposed to harsh environments, including high salinity, multi-modal vortex corrosion, and severe wave interference. Their safety is essential for the development and transportation of marine energy. Therefore, real-time safety monitoring of long-distance energy pipelines is of great strategic importance for ensuring the safety of life and property and energy security. With the rapid development of energy development, the corrosion and leakage mechanisms of natural gas pipelines, as well as their identification and early warning, have become the focus of attention. Optical fiber sensing technology has been applied to various energy safety monitoring fields. However, the mechanism of sound source fluctuations in pipeline leakage and the mutual coupling mechanism between distributed optical fiber sensing technology and leakage sound waves are not yet clear. This paper establishes a model based on sound wave propagation and leakage noise response, derives a quadratic fitting relationship between pipeline pressure fluctuations and leakage orifices and a relationship between leakage noise source standard deviation and orifices, and proposes a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) permutation entropy underwater natural gas pipeline leakage signal recognition method based on distributed optical fiber acoustic sensing technology. The results of theoretical analysis are verified by experiments. It shows that the signal processing method of CEEMDAN permutation entropy is superior to traditional noise reduction methods, which can better preserve the features of the original signal; the radial basis function (RBF) neural network model can accurately identify four different leakage features with an accuracy of 88.15%, and its recognition stability and generalization ability are superior to convolutional neural network, backpropagation, and random forest. Therefore, the research results of this paper provide a new method for safety monitoring in the application of energy pipeline transportation engineering, and expand the potential application scenarios of distributed acoustic sensing sensor systems and RBF neural networks.

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Section: Theory Of Pressure Fluctuation and Leakage Aperturementioning

confidence: 99%

Application of pipeline leakage detection based on distributed optical fiber acoustic sensor system and convolutional neural network

Duan,

Liang,

Tong

et al. 2023

J. Phys. D: Appl. Phys.

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“…Nearly 80% of crude oil and 90% of natural gas in China rely on pipeline transportation. Many pipelines have a service life of more than 20 years, and leakage and perforation are frequent due to third-party damage, internal corrosion, soil corrosion, stray current and other reasons [1][2] . On November 22, 2013, Sinopec Donghuang oil pipeline leaked and exploded, resulting in 62 deaths and 136 injuries.…”

Section: Introductionmentioning

confidence: 99%

Pipeline leakage location technology based on SA-PSO algorithm

Zhang,

Shan

2023

Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023)

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In order to solve the problem of pipeline leakage location, a fluid dynamics model was established, the parameters of the pipeline were calculated iteratively by Runge Kutta method, the problem of pipeline leakage location was transformed into an optimization problem, and the optimization problem was solved by simulated annealing-particle swarm optimization algorithm (SA-PSO), and compared with other algorithms. The results show that the SA-PSO algorithm can jump out of the limitation of the local optimal solution, under the premise of guaranteeing the number of iterations, the global optimal solution can be obtained. The calculation time of the algorithm is the shortest, the average is 1.5 s, the positioning error under different working conditions is between 1.3~2.9 m, and the algorithm has good adaptability to small leakage. The change of flow rate and leakage rate has no influence on the positioning accuracy. The research results can provide theoretical basis and practical reference for the integrity of oil and gas pipelines.

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“…Natural gas has a low explosion limit, and the leaking gas is flammable and explosive when it reaches a certain concentration, which can easily cause major safety accidents that seriously threaten the lives and properties of the general public [14,15]. The current mainstream gas pipeline leak identification method is outside the pipeline detection, but outside the pipeline, detection excavation cost is high and can only identify the size of the larger leak points [16][17][18][19] and cannot meet the requirement of 1 mm minimum identifiable leak size for DN100 buried gas pipeline. Therefore, the design of a microleakage image recognition method applicable to the detection inside small, buried gas pipelines is of great significance to ensure people's safety and enhance economic efficiency.…”

Section: Introductionmentioning

confidence: 99%

Micro-Leakage Image Recognition Method for Internal Detection in Small, Buried Gas Pipelines

Zhao

2023

Sensors

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In order to resolve the problem that the sample of image for internal detection of DN100 buried gas pipeline microleakage is single and difficult to identify, a recognition method of microleakage image of the pipeline internal detection robot is proposed. First, nongenerative data augmentation is used to expand the microleakage images of gas pipelines. Secondly, a generative data augmentation network, Deep Convolutional Wasserstein Generative Adversarial Networks (DCWGANs), is designed to generate microleakage images with different features for detection in the pipeline of gas pipelines to achieve sample diversity of microleakage images of gas pipelines. Then, a bi-directional feature pyramid network (BiFPN) is introduced into You Only Look Once (YOLOv5) to retain more deep feature information by adding cross-scale connecting lines in the feature fusion structure; finally, a small target detection layer is constructed in YOLOv5 so that more shallow feature information can be retained to achieve small-scale leak point recognition. The experimental results show that the precision of this method for microleak identification is 95.04%, the recall rate is 94.86%, the mAP value is 96.31%, and the minimum size of identifiable leaks is 1 mm.

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Research on Technology of Gas Pipeline Leakage Detection Based on Infrasonic Wave

Cited by 8 publications

References 0 publications

Application of pipeline leakage detection based on distributed optical fiber acoustic sensor system and convolutional neural network

Application of pipeline leakage detection based on distributed optical fiber acoustic sensor system and convolutional neural network

Pipeline leakage location technology based on SA-PSO algorithm

Micro-Leakage Image Recognition Method for Internal Detection in Small, Buried Gas Pipelines

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