Latest revision as of 20:05, 1 February 2021

Abstract

Safety and reliability of hydrocarbon transportation pipelines represent a critical aspect for the Oil an Gas industry. Pipeline failures caused by corrosion, external agents, among others, can develop leaks or even rupture, which can negatively impact on population, natural environment, infrastructure and economy. It is imperative to have accurate inspection tools traveling through the pipeline to diagnose the integrity. In this way, over the last few years, different techniques under the concept of structural health monitoring (SHM) have continuously been in development.This work is based on a hybrid methodology that combines the Magnetic Flux Leakage (MFL) and Principal Components Analysis (PCA) approaches. The MFL technique induces a magnetic field in the pipeline's walls. The data are recorded by sensors measuring leakage magnetic field in segments with loss of metal, such as cracking, corrosion, among others. The data provide information of a pipeline with 15 years of operation approximately, which transports gas, has a diameter of 20 inches and a total length of 110 km (with several changes in the topography). On the other hand, PCA is a well-known technique that compresses the information and extracts the most relevant information facilitating the detection of damage in several structures. At this point, the goal of this work is to detect and localize critical loss of metal of a pipeline that are currently working.

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

• http://hdl.handle.net/2117/84881 under the license https://creativecommons.org/licenses/by

• http://iopscience.iop.org/article/10.1088/1742-6596/628/1/012027/pdf under the license http://creativecommons.org/licenses/by-nc-nd/3.0/es/

• http://stacks.iop.org/1742-6596/628/i=1/a=012027/pdf,

http://stacks.iop.org/1742-6596/628/i=1/a=012027?key=crossref.b49064e53b510291d3ffdae3b42606d4,

http://dx.doi.org/10.1088/1742-6596/628/1/012027 under the license cc-by

• https://iopscience.iop.org/article/10.1088/1742-6596/628/1/012027/pdf,

https://upcommons.upc.edu/handle/2117/84881,

https://core.ac.uk/display/41826716,

https://ui.adsabs.harvard.edu/abs/2015JPhCS.628a2027R/abstract,

http://iopscience.iop.org/article/10.1088/1742-6596/628/1/012027/pdf,

https://academic.microsoft.com/#/detail/2233603112 under the license http://iopscience.iop.org/info/page/text-and-data-mining