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Assessment of the Condition of Pipelines Using Convolutional Neural Networks

Author

Listed:
  • Yuri Vankov

    (Industrial heat power and heat supply systems, Kazan State Power Engineering University, Kazan 420066, Russia)

  • Aleksey Rumyantsev

    (Computer Systems, Kazan National Research Technical University named after A.N. Tupolev-KAI, Kazan 420111, Russia)

  • Shamil Ziganshin

    (Industrial heat power and heat supply systems, Kazan State Power Engineering University, Kazan 420066, Russia)

  • Tatyana Politova

    (Industrial heat power and heat supply systems, Kazan State Power Engineering University, Kazan 420066, Russia)

  • Rinat Minyazev

    (Computer Systems, Kazan National Research Technical University named after A.N. Tupolev-KAI, Kazan 420111, Russia)

  • Ayrat Zagretdinov

    (Industrial heat power and heat supply systems, Kazan State Power Engineering University, Kazan 420066, Russia)

Abstract

Pipelines are structural elements of many systems. For example, they are used in water supply and heat supply systems, in chemical production facilities, aircraft manufacturing, and in the oil and gas industry. Accidents in piping systems result in significant economic damage. An important factor for ensuring the reliability of energy transportation systems is the assessment of real technical conditions of pipelines. Methods for assessing the state of pipeline systems by their vibro-acoustic parameters are widely used today. Traditionally, the Fourier transform is used to process vibration signals. However, as a rule, the oscillations of the pipe-liquid system are non-linear and non-stationary. This reduces the reliability of devices based on the implementation of classical methods of analysis. The authors used neural network methods for the analysis of vibro-signals, which made it possible to increase the reliability of diagnosing pipeline systems. The present work considers a method of neural network analysis of amplitude-frequency measurements in pipelines to identify the presence of a defect and further clarify its variety.

Suggested Citation

  • Yuri Vankov & Aleksey Rumyantsev & Shamil Ziganshin & Tatyana Politova & Rinat Minyazev & Ayrat Zagretdinov, 2020. "Assessment of the Condition of Pipelines Using Convolutional Neural Networks," Energies, MDPI, vol. 13(3), pages 1-12, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:618-:d:315227
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    References listed on IDEAS

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    1. Yingjie Xia & Chuanqing Zhang & Hui Zhou & Chunsheng Zhang & Wangbing Hong, 2019. "Mechanical Anisotropy and Failure Characteristics of Columnar Jointed Rock Masses (CJRM) in Baihetan Hydropower Station: Structural Considerations Based on Digital Image Processing Technology," Energies, MDPI, vol. 12(19), pages 1-24, September.
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    4. Jianjun Chen & Weihao Hu & Di Cao & Bin Zhang & Qi Huang & Zhe Chen & Frede Blaabjerg, 2019. "An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach," Energies, MDPI, vol. 12(14), pages 1-15, July.
    5. Feng Gao & Xiaojiang Wu & Qiang Liu & Juncheng Liu & Xiyun Yang, 2019. "Fault Simulation and Online Diagnosis of Blade Damage of Large-Scale Wind Turbines," Energies, MDPI, vol. 12(3), pages 1-16, February.
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    Cited by:

    1. Wan Zhang & Ruihao Shen & Ning Xu & Haoran Zhang & Yongtu Liang, 2020. "Study on Optimization of Active Control Schemes for Considering Transient Processes in the Case of Pipeline Leakage," Energies, MDPI, vol. 13(7), pages 1-16, April.
    2. Dariusz Bęben & Teresa Steliga, 2023. "Monitoring and Preventing Failures of Transmission Pipelines at Oil and Natural Gas Plants," Energies, MDPI, vol. 16(18), pages 1-19, September.

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