Data-driven pipeline leak detection method based on cloud-edge collaboration

被引：0
|
作者：
Ma D.-Z. [1 ]
Wang T.-B. [1 ]
Hu X.-G. [1 ]
Liu Y.-Y. [1 ]
Liu J.-H. [1 ]
机构：
[1] College of Information Science and Engineering, Northeastern University, Shenyang
来源：
Kongzhi yu Juece/Control and Decision | 2023年 / 38卷 / 08期
关键词：
cloud-edge collaboration; data acquisition; data compression; data-driven; genetic algorithm; pipeline leak detection;
D O I：
10.13195/j.kzyjc.2023.0344
中图分类号：
学科分类号：
摘要：
To enhance the pipeline leak detection system’s efficiency, this paper presents a data-driven approach based on a collaborative cloud-side system. The increasing complexity of the leak detection process and the growing scale of pipeline transportation create challenges for data acquisition, transmission, and processing. Our proposed approach addresses these challenges by introducing an adaptive data compression and acquisition algorithm, which effectively reduces data redundancy and enables efficient collection of large volumes of pressure data. Fine-grained task division is performed for each link in the cloud-side collaborative system, based on the requirements of the cloud-side collaborative scheduling strategy. We propose a task for the pipeline leak detection system under the cloud-side collaborative system, based on the computation delay and transmission delay of the divided subtasks. The topological model of the pipeline leak detection system under cloud edge collaboration is also presented, and the optimization objective is defined as the edge controller utilization under the task execution time constraint. Furthermore, we use a genetic algorithm to solve the optimal scheduling strategy under the time constraint. And then we verify the effectiveness of the pipeline cloud edge collaborative leak detection method, achieving a rapid pipeline leak time alarm. © 2023 Northeast University. All rights reserved.
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页码：2415 / 2424
页数：9
相关论文
共 28 条
  • [1] Zhou S H, Wang J, Liang Y., Development of China’s natural gas industry during the 14th Five-Year Plan in the background of carbon neutrality, Natural Gas Industry, 41, 2, pp. 171-182, (2021)
  • [2] Li Q Y, Zhao M H, Zhang B, Et al., Current construction status and development trend of global oil and gas pipelines in 2020, Oil & Gas Storage and Transportation, 40, 12, pp. 1330-1337, (2021)
  • [3] Gao P., New progress in China’s oil and gas pipeline construction in 2021, International Petroleum Economics, 30, 3, pp. 12-19, (2022)
  • [4] Ma D Z, Hu X G, Sun Q Y, Et al., Cyber-physical abnormity diagnosis method using data feature fusion for pipeline network, Acta Automatica Sinica, 45, 1, pp. 163-173, (2019)
  • [5] Aljuaid K G, Albuoderman M A, AlAhmadi E A, Et al., Comparative review of pipelines monitoring and leakage detection techniques, The 2nd International Conference on Computer and Information Sciences, pp. 1-6, (2020)
  • [6] Benyeogor M S, Olatunbosun A, Kumar S., Airborne system for pipeline surveillance using an unmanned aerial vehicle, European Journal of Engineering Research and Science, 5, 2, pp. 178-182, (2020)
  • [7] Hossain K, Villebro F, Forchhammer S., UAV image analysis for leakage detection in district heating systems using machine learning, Pattern Recognition Letters, 140, pp. 158-164, (2020)
  • [8] Willsky A S., A survey of design methods for failure detection in dynamic systems, Automatica, 12, 6, pp. 601-611, (1976)
  • [9] Feng J, Zhang HG., On-linecomputerdetectingsystemof pipeline leak and its algorithm, Control and Decision, 19, 4, pp. 377-382, (2004)
  • [10] Amini I, Jing Y, Chen T., A two stage deep learning based detection method for pipeline leakage and transient conditions, 2020 IEEE Electric Power and Energy Conference, pp. 1-5, (2020)
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