Mass transfer characteristics of pipeline leak-before-break in a nuclear power station

Yin, Songtao; Weng, Yu Hsuan; Song, Zichen; Cheng, Bo; Gu, Hongfang; Wang, Haijun; Yao, Jianan

doi:10.1016/j.applthermaleng.2018.06.077

Cited by 20 publications

(5 citation statements)

References 23 publications

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“…Regarding oil leakage detection on land, one type of oil leakage detection is for oil pipelines. Over the decades, researchers have proposed methods for pipeline leakage detection based on different operating principles, [25][26][27][28][29][30][31][32][33][34][35] including ultrasonic, acoustic emission, pressure point analysis, negative pressure waves, fiber optic sensors, dynamic modeling, infrared thermography, flow balancing, and smart ball leakage detection techniques. The recent method [28] achieved an accuracy of 94.9% on the test set.…”

Section: Oil Leakage Detectionmentioning

confidence: 99%

Crude Oil Leakage Detection Based on DA‐SR Framework

Gong

Gao

Yuan

et al. 2022

Advcd Theory and Sims

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Crude oil leakage is a security issue that needs to be avoided in many production areas such as oil fields and substations. However, crude oil leakage image data is often difficult to obtain due to security and privacy issues in the working area. And shadow interference is also a challenge for oil leakage detection tasks. This paper proposes a crude oil leakage detection method based on the DA‐SR framework. The framework consists of two parts: the data augmentation module and shadow removal module. High‐quality synthetic oil leakage images are generated using the cycle‐consistent adversarial networks (CycleGAN), and further process the synthetic images by a T‐CutMix sample processing method. To solve the problem of shadow interference, this paper uses the FlocalLoss function to calculate the confidence loss based on the YOLOv4 detection network and a hard sample retraining (HSR) algorithm to enhance the images with shadows. The experiments demonstrate that the combination of original and synthetic images when training the model can improve the performance of oil leakage detection. Finally, it is also shown that the detector built from the framework can effectively reduce the false detection of shadows.

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Section: Oil Leakage Detectionmentioning

confidence: 99%

Crude Oil Leakage Detection Based on DA‐SR Framework

Gong

Gao

Yuan

et al. 2022

Advcd Theory and Sims

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“…Where, R L , V C , and Vout are constants, m and c calculated already. The gas concentration in parts per million (ppm) is obtained from (9). The software is implemented by applying these equations.…”

Section: System Implementation and Operationmentioning

confidence: 99%

“…This system however suffers from high cost, long delays, and inflexibility to protocol changes attributes. The mass-volume balance method [8], [9], the negative pressure monitoring method [10], [11], acoustic monitoring method [12], [13] are limited by their dependence on the extent of the leak, leak flow rate and noise [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Robot for Gas Pipeline Inspection and Leak Detection

Okoli¹,

Ubochi²

2022

IJCDS

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“…[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] These approaches are categorized using different frameworks basically into two categories: hardware and software. [31][32][33][34] The methods are further extended into three main areas: internal, nontechnical or non-continuous, and external methods [35][36][37][38][39][40][41][42] ; more study has been done to classify available leak detection systems. [2][3][4] The present study will classify the following categories: external, visible or biological, internal or computing.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive study of techniques utilized for structural health monitoring of oil and gas pipelines

Sharma,

Tewari,

Biswas

et al. 2023

Structural Health Monitoring

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An extensive network of pipelines is extensively employed worldwide to carry oil and gas fluids over millions of kilometers. The pipeline channel was constructed to resist environmental dynamic loading conditions to provide safe and reliable oil and gas fluids transportation from the production well sites to depot stations installed at sea coastlines. However, pipeline infrastructure damages such as fractures, cracks, leakages, etc., are significant sources of economic losses in pipeline operations. Moreover, pipeline failures can cause considerable ecological catastrophes, human deaths, and financial loss. Important research initiatives have been committed to establishing pipeline breach detection and localization using various techniques to avoid these threats and maintain a secure and dependable pipeline network. This paper reviews different state-of-the-art damage detection methods and their recent advancement with a case study explaining the application of light detection and ranging for pipeline damage detection. The pros and cons of diverse damage detection methods in pipeline networks are also discussed. Research gaps for pipeline damage detection systems are also provided for better understanding and future research.

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Mass transfer characteristics of pipeline leak-before-break in a nuclear power station

Cited by 20 publications

References 23 publications

Crude Oil Leakage Detection Based on DA‐SR Framework

Crude Oil Leakage Detection Based on DA‐SR Framework

Autonomous Robot for Gas Pipeline Inspection and Leak Detection

A comprehensive study of techniques utilized for structural health monitoring of oil and gas pipelines

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