Latest revision as of 14:58, 12 February 2021

Abstract

An ultrasonic sensors system is commonly used to measure the wall thickness of buried pipelines in the transportation of oil and gas. The key of the system is to precisely measure time-of-flight difference (TOFD) produced by the reflection of ultrasonic on the inner and outer surfaces of the pipelines. In this paper, based on deep learning, a novel method termed Wave-Transform Network is proposed to tackle the issues. The network consists of two parts: part 1 is designed to separate the potential overlapping ultrasonic echo signals generated from two surfaces, and part 2 is utilized to divide the sample points of each signal into two types corresponding to before and after the arrival time of ultrasonic echo, which can determine the time-of-flight (TOF) of each signal and calculate the thickness of pipelines. Numerical simulation and actual experiments are carried out, and the results show satisfactory performances.

Document type: Article

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The different versions of the original document can be found in:

• http://europepmc.org/articles/PMC7570909 under the license https://creativecommons.org/licenses/by

• https://www.mdpi.com/1424-8220/20/18/5140/pdf

• https://dblp.uni-trier.de/db/journals/sensors/sensors20.html#LiWZGYLS20,

https://www.mdpi.com/1424-8220/20/18/5140,

https://www.mdpi.com/1424-8220/20/18/5140/pdf,

https://doi.org/10.3390/s20185140,

https://academic.microsoft.com/#/detail/3083843682 under the license cc-by

• https://www.mdpi.com/1424-8220/20/18/5140,

https://doaj.org/toc/1424-8220

• https://www.mdpi.com/1424-8220/20/18/5140/pdf,

http://dx.doi.org/10.3390/s20185140

under the license https://creativecommons.org/licenses/by/4.0/