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Open AccessArticle

Assessment of the Condition of Pipelines Using Convolutional Neural Networks

1
Industrial heat power and heat supply systems, Kazan State Power Engineering University, Kazan 420066, Russia
2
Computer Systems, Kazan National Research Technical University named after A.N. Tupolev-KAI, Kazan 420111, Russia
*
Author to whom correspondence should be addressed.
Energies 2020, 13(3), 618; https://doi.org/10.3390/en13030618
Received: 31 December 2019 / Revised: 21 January 2020 / Accepted: 22 January 2020 / Published: 1 February 2020
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. View Full-Text
Keywords: pipelines; defect; diagnostics; convolutional neural network; binary classification; computational experiment pipelines; defect; diagnostics; convolutional neural network; binary classification; computational experiment
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MDPI and ACS Style

Vankov, Y.; Rumyantsev, A.; Ziganshin, S.; Politova, T.; Minyazev, R.; Zagretdinov, A. Assessment of the Condition of Pipelines Using Convolutional Neural Networks. Energies 2020, 13, 618.

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