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January 1, 2026International Journal of Oil Gas and Coal Technology

Leakage detection method of acoustic emission pipeline based on deep convolutional neural network transfer learning

Xinying Wang Haojie Tian Honglei Che Haiqun Chen

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Key Points

The central aim is to develop a robust method for detecting pipeline leaks using deep convolutional neural networks with transfer learning.
Implemented a deep convolutional neural network architecture for leak detection.
Utilized transfer learning to enhance model performance with limited data.
Gathered acoustic emission data from pipelines as input for the model.
The proposed method shows improved accuracy in identifying leaks compared to traditional techniques.
Achieved significant reduction in false positive rates during leak detection.
Demonstrated the ability to detect leaks at early stages, which can prevent larger failures.

Implication

Demonstrates a leakage detection method using deep learning in pipelines, highlighting its effectiveness for monitoring systems.

Abstract

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Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/699fe36b95ddcd3a253e739d https://doi.org/10.1504/ijogct.2026.10076495

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