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Advanced transformer model for simultaneous leakage aperture recognition and localization in gas pipelines

Author

Listed:
  • Li, Pengyu
  • Wang, Xiufang
  • Jiang, Chunlei
  • Bi, Hongbo
  • Liu, Yongzhi
  • Yan, Wendi
  • Zhang, Cong
  • Dong, Taiji
  • Sun, Yu

Abstract

This study proposes a joint learning end-to-end dual-stream transformer structure based on enhanced external attention and improved feedforward mechanisms (EEA-JL) to address the problem of simultaneous leakage aperture recognition and localization in gas pipelines under noisy backgrounds. The EEA-JL structure can effectively improve the accuracy of leakage aperture recognition and localization in a single model. Traditional convolutional neural networks (CNNs) and recursive frameworks (RNNs) have limitations in extracting leakage features and analyzing positional information, making it difficult to capture the correlation coupling between different leakage apertures and positions. The EEA-JL structure incorporates a self-attention mechanism with two external linear neural memory units to analyze the correlation between samples and deepen the understanding of different leakage scenarios. Additionally, the multi-scale soft-threshold denoising module (MSSD) adaptively estimates the noise threshold of signals under different leakage conditions to achieve denoising. Through simulation experiments on a 169-meter oil and gas pipeline leakage detection system platform and comparison with other advanced methods, the EEA-JL model achieves a precision rate and R2score of 99.7% and 0.993, respectively, in aperture recognition and localization, with an average positioning error controlled within 1.26 m, demonstrating its guiding significance.

Suggested Citation

  • Li, Pengyu & Wang, Xiufang & Jiang, Chunlei & Bi, Hongbo & Liu, Yongzhi & Yan, Wendi & Zhang, Cong & Dong, Taiji & Sun, Yu, 2024. "Advanced transformer model for simultaneous leakage aperture recognition and localization in gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023005999
    DOI: 10.1016/j.ress.2023.109685
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    References listed on IDEAS

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    1. Su, Yue & Li, Jingfa & Yu, Bo & Zhao, Yanlin & Yao, Jun, 2021. "Fast and accurate prediction of failure pressure of oil and gas defective pipelines using the deep learning model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Liu, Cuiwei & Wang, Yazhen & Li, Xinhong & Li, Yuxing & Khan, Faisal & Cai, Baoping, 2021. "Quantitative assessment of leakage orifices within gas pipelines using a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    3. Juan Li & Wenjun Zheng & Changgang Lu, 2022. "An Accurate Leakage Localization Method for Water Supply Network Based on Deep Learning Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2309-2325, May.
    4. Zhou, Jie & Lin, Haifei & Li, Shugang & Jin, Hongwei & Zhao, Bo & Liu, Shihao, 2023. "Leakage diagnosis and localization of the gas extraction pipeline based on SA-PSO BP neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    5. Hassan, Shamsu & Wang, Jin & Kontovas, Christos & Bashir, Musa, 2022. "An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    6. Zhang, Y. & Weng, W.G., 2020. "Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    7. Juan Li & Ying Wu & Wenjun Zheng & Changgang Lu, 2021. "A Model-Based Bayesian Framework for Pipeline Leakage Enumeration and Location Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4381-4397, October.
    8. Chang, Yuanhong & Li, Fudong & Chen, Jinglong & Liu, Yulang & Li, Zipeng, 2022. "Efficient temporal flow Transformer accompanied with multi-head probsparse self-attention mechanism for remaining useful life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
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