Assessment of the Condition of Pipelines Using Convolutional Neural Networks

Yuri Vankov, Aleksey Rumyantsev, Shamil Ziganshin, Tatyana Politova, Rinat Minyazev and Ayrat Zagretdinov
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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

Energies, 2020, vol. 13, issue 3, 1-12

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.

Keywords: pipelines; defect; diagnostics; convolutional neural network; binary classification; computational experiment (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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