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Proceedings of the International Congress on Sound and Vibration (ICSV29), 1–6p. (2023) https://iiav.org/content/archives_icsv_last/2023_icsv29/indexdd69.html?va=viewpage&vaid=405&said=10&session_id=146
In order to prevent unexpected accident due to a pipe damage, it is necessary to quickly detect location of the damage. Damage in a pipeline can be classified into leak and rupture according to the size of the damage. In this research, we studied two pipeline damage localization methods, which employ the non-intrusive pipe pressure monitoring sensor: (1) a cross-correlation function-based leak localization method and (2) a rupture localization method based on Recurrence Plot(RP) and Convolutional Neural Network(CNN). Frist, to develop a cross-correlation function-based leak localization method, two non-intrusive pipe pressure monitoring sensors were installed on the pipeline testbed, and the fluid-pipe coupled vibration signals were measured when a pipe was leak. The time difference between the measured signals was calculated using the cross-correlation function, and it was confirmed that leak location can be predicted within about 7.8% error. Second, to develop a RP-CNN based rupture localization method, pressure change at various locations in a pipeline testbed were measured when rupture is occurred using non-intrusive pipe pressure monitoring sensors. After that, the pressure data were converted into images using RP, and the images were classified using CNN. It was found that pressure data measured close to the rupture are well classified with the pressure data measured away from the rupture.
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© 2024 SOME RIGHTS RESERVED
The content of this web site is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Germany License.
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