Active thermal imaging of natural gas leakages

Jach, K.; Kopica, Miroslaw; Mierczyk, Zygmunt; Pregowski, Piotr; Strzelec, M.

doi:10.1109/cleopr.1999.811580

Cited by 3 publications

(2 citation statements)

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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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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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“…Recently, infrared imaging has been used in the oil and gas industry as a method to detect potential leakage in the pipeline, components and equipment [4]. Thermal imaging is the one of the accurate method of detection for the gas leakage [5]. This method is widely used for the gas leakage detection because the thermal image can detect the radiation of the gas pipeline by displaying the image on the thermal camera.…”

Section: Introductionmentioning

confidence: 99%

Gas Leakage Detection Using Thermal Imaging Technique

Jadin

Ghazali

2014

2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation

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Gas leakage is one of the hazards that can cause major incidents to human injuries, fires as well as high impact on economic. To avoid such situation, a preventive inspection is paramount important. Since gas leakage is unseen by naked eyes due to the energy that are emitted by gas are too small to simulate by eyes, thermal imaging technology is used to detect and evaluate the severity level of the leakage. This paper presents a method for detecting gas leak using infrared image analysis. Through an image filtering technique, the target region of interests is enhanced and segmented to extract the leaky regions. The image feature is extracted to identify the leakage. From the experimental result, it shows that this system is effective in detecting the presence of gas leakage in real industrial problem.

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Gas leakage detection using convolution neural networks

Kondapalli,

Penmetsa,

P. Krishna

2023

JCOM

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In many different industrial, residential, and commercial situations, gas leakage poses a serious hazard. Its discovery is crucial since it might have severe effects like explosions and fires. For the protection of persons and property, as well as to avert catastrophic tragedies, accurate and prompt gas leak detection is essential. Convolutional Neural Networks (CNNs), in particular, have demonstrated encouraging results in the detection of gas leaks in recent years. Here, a CNNbased method is provided for detecting gas leaks from image data. The suggested method employs a Softmax classifier for gas classification after extracting features from the image dataset using a combination of convolution, pooling, and fully connected layers. The usefulness of the suggested approach in accurately detecting gas leakage is shown by the experimental findings and the proposed approach is tested on a real-world gas leakage dataset. It can be added to gas detection systems to improve their functionality, lowering the likelihood of gas-related mishaps. The findings support current work to create accurate and effective machine learning-based gas leak detection systems.

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Active thermal imaging of natural gas leakages

Cited by 3 publications

References 0 publications

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

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

Gas Leakage Detection Using Thermal Imaging Technique

Gas leakage detection using convolution neural networks

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