Recognition and monitoring of gas leakage using infrared imaging technique with machine learning

Shirley, C. P.; Raja, J Immanuel John; Evangelin Sonia, S. V.; Titus, I.

doi:10.1007/s11042-023-17131-w

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…With the development of computer technology, AI is widely used in the leakage monitoring of petrochemical industry [17][18][19]. Machine learning, machine vision, deep learning, and pattern recognition technologies can realize realtime analysis and processing of large amounts of complex sensor data, achieving timely and accurate identification of leakage events.…”

Section: Introductionmentioning

confidence: 99%

Leakage monitoring and diagnosis of LNG storage tanks with temperature sensing network integration and artificial intelligence algorithm

Wu,

Yang,

Sun

et al. 2024

Meas. Sci. Technol.

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Temperature is the most important safety monitoring indicator for leakage diagnosis during the operation phase of liquefied natural gas (LNG) storage tanks. Timely monitoring and accurate identification of LNG leakage events are crucial for accident prevention, loss reduction, and facility safety maintenance. This study integrates artificial intelligence (AI) algorithms and temperature sensing data to achieve intelligent monitoring and diagnosis of leakage in LNG storage tanks. Firstly, a comprehensive temperature sensing network is constructed by combining numerical simulation of the temperature field and temperature sensing experiments using fiber Bragg grating sensors. Secondly, Python is used to perform linear grid interpolation and flattening on the sensing network, generating 2D temperature nephogram samples that are conducive to AI algorithm recognition. Finally, sample features are extracted using machine vision, and leakage location calculation, leakage diagnosis and leakage volume calculation are implemented with the help of machine learning algorithms, achieving satisfactory accuracy on the test set. In addition, the ConvLSTM framework is introduced for deep learning and recurrent neural network training, enabling spatiotemporal prediction of the leakage area.

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Section: Introductionmentioning

confidence: 99%

Leakage monitoring and diagnosis of LNG storage tanks with temperature sensing network integration and artificial intelligence algorithm

Wu,

Yang,

Sun

et al. 2024

Meas. Sci. Technol.

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Gas Imaging with Uncooled Thermal Imager

Zhang,

Chen,

Lin

et al. 2024

Sensors

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Gas imaging has become one of the research hotspots in the field of gas detection due to its significant advantages, such as high efficiency, large range, and dynamic visualization. It is widely used in industries such as natural gas transportation, chemical, and electric power industries. With the development of infrared detector technology, uncooled thermal imagers are undergoing a developmental stage of technological advancement and widespread application. This article introduces a gas imaging principle and radiation transfer model, focusing on passive imaging technology and active imaging technology. Combined with the actual analysis, the application scenarios using uncooled thermal imaging cameras for gas imaging measurement are analyzed. Finally, the limitations and challenges of the development of gas imaging technology are analyzed.

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Recognition and monitoring of gas leakage using infrared imaging technique with machine learning

Cited by 2 publications

References 7 publications

Leakage monitoring and diagnosis of LNG storage tanks with temperature sensing network integration and artificial intelligence algorithm

Leakage monitoring and diagnosis of LNG storage tanks with temperature sensing network integration and artificial intelligence algorithm

Gas Imaging with Uncooled Thermal Imager

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