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.
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- http://dx.doi.org/10.3390/en13030618 under the license https://creativecommons.org/licenses/by
- https://www.mdpi.com/1996-1073/13/3/618/pdf under the license https://creativecommons.org/licenses/by/4.0
- https://doaj.org/toc/1996-1073 under the license cc-by
- https://ideas.repec.org/a/gam/jeners/v13y2020i3p618-d315227.html,
- https://academic.microsoft.com/#/detail/3003668429
under the license https://creativecommons.org/licenses/by/4.0/
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Published on 01/01/2020
Volume 2020, 2020
DOI: 10.3390/en13030618
Licence: Other
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