基于迁移学习的燃气管网泄漏定位方法

doi:10.16265/j.cnki.issn1003-3033.2025.03.0957

中国安全科学学报 ›› 2025, Vol. 35 ›› Issue (3): 212-220.doi: 10.16265/j.cnki.issn1003-3033.2025.03.0957

基于迁移学习的燃气管网泄漏定位方法

陈岑¹(), 纪育博², 王欢², 聂荣山³^,⁴, 梁晓瑜¹^,³^,^**()

¹ 中国计量大学计量测试与仪器学院,浙江杭州 310018
² 宁波华润兴光燃气有限公司,浙江宁波 315010
³ 中国计量大学能源环境与安全工程学院,浙江杭州 310018
⁴ 中国安全生产科学研究院交通安全研究所,北京 100012

收稿日期:2024-10-19 修回日期:2024-12-20 出版日期:2025-03-28
通信作者:
** 梁晓瑜(1975—),男,安徽宿州人,博士,教授,博士生导师,主要从事燃气安全运维与智慧计量等方面的研究。E-mail:xyliang@cjlu.edu.cn。
作者简介:
陈岑 (2000—),女,江西井冈山人,硕士研究生,研究方向为燃气管网异常诊断。E-mail:1621179538@qq.com。
纪育博，高级工程师；
王欢，高级工程师；
基金资助:
国家市场监督管理总局科技计划项目(2023MK230); 国家自然科学基金面上项目资助(51871206)

Gas pipeline network leak localization method based on transfer learning

CHEN Cen¹(), JI Yubo², WANG Huan², NIE Rongshan³^,⁴, LIANG Xiaoyu¹^,³^,^**()

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

Received:2024-10-19 Revised:2024-12-20 Published:2025-03-28

摘要/Abstract

摘要：

为增强燃气管网运行的可靠性与安全性,提高燃气管网泄漏故障的诊断能力,解决真实燃气管网泄漏数据样本稀缺及工况差异影响问题,提出基于迁移学习的燃气管网泄漏定位方法。首先,采用随机森林特征重要性排序方法,选取出TGNET仿真管网的5个压力监测点;然后,将3种不同压力工况下的压力监测点数据分别作为源域和目标域,输入特征,改进迁移学习传统联合概率分布适应(JDA)方法,以减小源域与目标域特征距离;最后,采用布谷鸟搜索(CS)算法,优化改进迁移学习算法的参数(映射后维度d'和学习率λ),实现无标签目标域泄漏管段的诊断。结果表明:所提复杂燃气管网泄漏定位方法可以有效提高无标签燃气管网泄漏识别效果,相比传统联合概率分布适应有更高的准确率。

关键词: 迁移学习, 燃气管网, 泄漏定位, 随机森林, 布谷鸟搜索(CS)算法, 联合分布自适应(JDA)

Abstract:

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.

Key words: transfer learning, gas pipeline network, leak localization, random forest, cuckoo search(CS) algorithm, joint distribution adaptation(JDA)

中图分类号:

X928.03事故预防与预测

陈岑, 纪育博, 王欢, 聂荣山, 梁晓瑜. 基于迁移学习的燃气管网泄漏定位方法[J]. 中国安全科学学报, 2025, 35(3): 212-220.

CHEN Cen, JI Yubo, WANG Huan, NIE Rongshan, LIANG Xiaoyu. Gas pipeline network leak localization method based on transfer learning[J]. China Safety Science Journal, 2025, 35(3): 212-220.

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气源压力/MPa	泄漏标签	样本数
0.4	1-16	2 391
0.3	1-16	2 391
0.2	1-16	2 391

源域工况压力/MPa	目标域工况压力/MPa	准确率/%	平均准确率/%
0.4	0.3	99.85	97.79
0.4	0.2	95.73	97.79
0.3	0.2	99.97	99.97
0.3	0.4	99.96	99.97
0.2	0.3	99.92	93.08
0.2	0.4	86.24	93.08

任务	SVM	随机森林	K最近邻	文中方法
0.4 MPa→0.3 MPa	36.96	37.68	35.98	99.85
0.4 MPa→0.2 MPa	36.96	37.68	35.98	95.73
0.3 MPa→0.2 MPa	36.99	37.29	36.07	99.97
0.3 MPa→0.4 MPa	36.99	37.29	36.07	99.96
0.2 MPa→0.3 MPa	37.02	37.57	36.43	99.92
0.2 MPa→0.4 MPa	37.02	37.57	36.43	86.24

基于迁移学习的燃气管网泄漏定位方法

Gas pipeline network leak localization method based on transfer learning

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参数	下限	上限	最优解
d'	10	100	16
λ	0.001	1	0.597