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Gas pipeline network leak localization method based on transfer learning
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Cen CHEN1, Yubo JI2, Huan WANG2, Rongshan NIE3, 4, Xiaoyu LIANG1, 3, **
China Safety Science Journal | 2025, 35(3) : 212 - 220
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China Safety Science Journal | 2025, 35(3): 212-220
• Public safety •
Gas pipeline network leak localization method based on transfer learning
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Cen CHEN1, Yubo JI2, Huan WANG2, Rongshan NIE3, 4, Xiaoyu LIANG1, 3, **
Affiliations
  • 1 College of Metrology and Instrument，China Jiliang University，Hangzhou Zhejiang 310018，China
  • 2 Ningbo China Resources Xingguang Gas Co.，Ltd.，Ningbo Zhejiang 315010，China
  • 3 College of Energy Environment and Safety Engineering，China Jiliang University，Hangzhou Zhejiang 310018，China
  • 4 Traffic Safety Research Institute，China Academy of Safety Science and Technology，Beijng 100012，China
Published: 2025-03-28 doi: 10.16265/j.cnki.issn1003-3033.2025.03.0957
Outline
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In order to enhance the reliability and safety of gas network operations and improve the fault diagnosis capabilities for gas network leaks，while addressing issues such as the scarcity of real gas network leak data samples and variations in operating conditions，a gas network leak localization method based on transfer learning was proposed. Firstly，the Random Forest feature importance ranking method was used to select five pressure monitoring points in the TGNET simulation network. Subsequently，pressure monitoring point data under three different pressure conditions were respectively used as the source domain and target domain input features. The traditional JDA method of transfer learning was improved to reduce the feature distance between the source domain and the target domain. Furthermore，the CS algorithm was employed to optimize the dimensionality after mapping d' and the learning rate λ parameters of the improved transfer learning algorithm，ultimately achieving the diagnosis of unlabeled target domain leak segments. The results indicated that the proposed leak localization method for complex gas networks can effectively improve the localization accuracy of unlabeled gas network leaks，achieving higher accuracy compared to traditional.

transfer learning  /  gas pipeline network  /  leak localization  /  random forest  /  cuckoo search(CS) algorithm  /  joint distribution adaptation(JDA)
Cen CHEN, Yubo JI, Huan WANG, Rongshan NIE, Xiaoyu LIANG. Gas pipeline network leak localization method based on transfer learning[J]. China Safety Science Journal, 2025 , 35 (3) : 212 -220 . DOI: 10.16265/j.cnki.issn1003-3033.2025.03.0957
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Year 2025 volume 35 Issue 3
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.03.0957
  • Receive Date：2024-10-19
  • Online Date：2025-07-05
  • Published：2025-03-28
Article Data
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History
  • Received：2024-10-19
  • Revised：2024-12-20
Funding
Affiliations
    1 College of Metrology and Instrument，China Jiliang University，Hangzhou Zhejiang 310018，China
    2 Ningbo China Resources Xingguang Gas Co.，Ltd.，Ningbo Zhejiang 315010，China
    3 College of Energy Environment and Safety Engineering，China Jiliang University，Hangzhou Zhejiang 310018，China
    4 Traffic Safety Research Institute，China Academy of Safety Science and Technology，Beijng 100012，China
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https://castjournals.cast.org.cn/joweb/zgaqkxxb/EN/10.16265/j.cnki.issn1003-3033.2025.03.0957
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