Small Leak Location for Intelligent Pipeline System via Action-Dependent Heuristic Dynamic Programming

被引:41
作者
Hu, Xuguang [1 ]
Zhang, Huaguang [2 ]
Ma, Dazhong [1 ]
Wang, Rui [1 ,3 ]
Tu, Pengfei [4 ]
机构
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110004, Peoples R China
[2] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110004, Peoples R China
[3] Nanyang Technol Univ, Singapore 637141, Singapore
[4] Nanyang Technol Univ, Energy Res Inst, Singapore 637141, Singapore
基金
中国国家自然科学基金;
关键词
Pipelines; Mathematical models; Data models; Transportation; Temperature measurement; Oils; Monitoring; Action-dependent heuristic dynamic programming (ADHDP); acto-critic learning; data driven; leak location; neural network; pipeline system; LOCALIZATION; DIAGNOSIS; FUSION;
D O I
10.1109/TIE.2021.3127016
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the construction process of intelligent pipeline system, pipeline monitoring is an important content to improve the safety of pipeline operation. Small leak location, in particular, is the primary focus of pipeline monitoring due to the unclear pressure drop point. To solve this, in this article, a data driven-based method of small leak location is proposed. First, through the collected pipeline parameters, empirical flow variables, and historical pressure values in pipeline system, a pipeline model based on pressure along pipeline is presented by a three-layer neural network to close to the industrial scenarios. Then, on the basis of the analyzed propagation process of negative pressure wave, an action-dependent heuristic dynamic programming with pressure-distance physical constraints is proposed to obtain the small leak location result. The proposed method is suitable for the collection of only pressure data scenario, which expands the application range. Finally, different cases of small leak location results indicate that the proposed method can locate the leak point, and the field tests further show that the proposed method has satisfactory performances in pipeline leak analysis.
引用
收藏
页码:11723 / 11732
页数:10
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