Gas Leakage Detection Using Thermal Imaging Technique

Jadin, Mohd Shawal; Ghazali, Kamarul Hawari

doi:10.1109/uksim.2014.95

Cited by 38 publications

(20 citation statements)

References 5 publications

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“…Details of the origin and theory of IRT have been presented in [81]. IRT has been widely used in pipeline monitoring [82,83,84]. An innovative method of detecting pressure air and gas leakages using passive IR thermography was reported in [69].…”

Section: Exterior Based Leak Detection Methodsmentioning

confidence: 99%

“…This work concluded that cavitation erosion, piping clogging and steel tanks can be inspected using an infrared camera. In the study of [84], a method for gas leak detection based on a thermal imaging approach was proposed. The pipeline surroundings were inspected using an infrared camera followed by filtering processing where the targeted region of interest was enhanced and segmented to extract features suitable for identification of rupture areas in the pipelines.…”

Section: Exterior Based Leak Detection Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Adegboye

Fung

Karnik

2019

Sensors

291

144

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Pipelines are widely used for the transportation of hydrocarbon fluids over millions of miles all over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distribution depot. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses pipeline leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.

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Section: Exterior Based Leak Detection Methodsmentioning

confidence: 99%

Section: Exterior Based Leak Detection Methodsmentioning

confidence: 99%

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Adegboye

Fung

Karnik

2019

Sensors

291

144

View full text Add to dashboard Cite

show abstract

“…Other studies suggest the detection of leakage of combustible gases using image processing, however, the images of these works are thermal images acquired by highcost hardware, which are infrared cameras specifically for detecting the infrared spectrum of the methane molecule [11]- [14].…”

Section: Introductionmentioning

confidence: 99%

Applying Convolutional Neural Networks to Detect Natural Gas Leaks in Wellhead Images

2020

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Detecting natural gas leaks is one of the most important measures in the oil industry for preventing accidents. The literature provides different techniques for detecting natural gas leaks. However, except for previous studies by the authors on this topic, there remains a gap in the literature on leak detection of natural gas using digital images, without the need for sensors or special cameras calibrated for the spectrum of methane molecules. These previous studies used image-processing techniques associated with a novelty filter classifier to detect the presence or absence of visible cloud of hydrocarbon vapors, that is, a natural gas plume in Closed Circuit Television (CCTV) frames installed in onshore wellheads. In this paper, we present a new method for detecting natural gas leaks in oil facilities that enhances the results obtained previously, along with the Gradient-weighted Class Activation Mapping Algorithm (Grad-CAM) to identify natural gas leaks. In this new method, convolutional neural networks (CNN) are applied to classify images (CCTV frames) as belonging to classes with or without natural gas leaks in onshore wellheads. Experimental results showed that the best performance model presented an accuracy of 99.78% and false negative rate of 0.00%.

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“…Among them, the hardware-based method uses external equipment like an infrared thermal imager, and a negative pressure detector to detect gas leakage signals. In [7][8][9], some scholars proposed the use of thermal imaging technology to detect gas leakage, analyze the heat emitted by the gas, and filter the image to VOLUME XX, 2017 1…”

Section: Introductionmentioning

confidence: 99%

Analysis of Gas Leakage Early Warning System Based on Kalman Filter and Optimized BP Neural Network

2020

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This paper proposes a method for gas leakage early warning system based on Kalman filter and back-propagation (BP) neural network to address the issue of inaccurate gas leakage detection and incapability of predicting concentration change of gas. First, Kalman filter is adopted to filter the noise from the gas concentration that is measured by a sensor. Then, predictions about the change of gas concentration are made using the BP neural network that is optimized by genetic algorithm. Next, the gas leakage early warning system, based on the proposed method, is designed. Last, to verify the effectiveness of the method proposed by simulation, methane, the main component of gas is chosen as an example. Also introduced in this paper are the determinant coefficient, mean absolute error, correlation coefficient and root-mean-square error ─ the four evaluation indicators methods to demonstrate the effectiveness and feasibility of the algorithm this paper proposed by comparing with Support Vector Machine (SVM), Long Short-term Memory (LSTM) and general Back Propagation Neural Network (BPNN). The best validation performance of BP neural network through simulation experiments and is 0.013518, and the probability of the relative error between the predicted value and the actual value within 10% is 0.7692. The proposed method can effectively improve the accuracy of gas concentration prediction as comparison results show, and it has advantage in fitting degree and error fluctuations.

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Gas Leakage Detection Using Thermal Imaging Technique

Cited by 38 publications

References 5 publications

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Applying Convolutional Neural Networks to Detect Natural Gas Leaks in Wellhead Images

Analysis of Gas Leakage Early Warning System Based on Kalman Filter and Optimized BP Neural Network

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