Scipediacontent: Scipediacontent moved page Draft Content 240636272 to Lima et al 2018a

2021-02-12T12:34:50Z

Scipediacontent moved page Draft Content 240636272 to Lima et al 2018a

Scipediacontent: Created page with " == Abstract == Industrial pipelines must be inspected to detect typical failures, such as obstructions and deformations, during their lifetime. In the petroleum industry, th..."

2021-02-12T12:34:48Z

Created page with " == Abstract == Industrial pipelines must be inspected to detect typical failures, such as obstructions and deformations, during their lifetime. In the petroleum industry, th..."

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== Abstract ==

Industrial pipelines must be inspected to detect typical failures, such as obstructions and deformations, during their lifetime. In the petroleum industry, the most used non-destructive technique to inspect buried pipelines is pigging. This technique consists of launching a Pipeline Inspection Gauge (PIG) inside the pipeline, which is driven by the pressure differential produced by fluid flow. The purpose of this work is to study the application of artificial neural networks to calculate the PIG&rsquo

s velocity based on the pressure differential. We launch a prototype PIG inside a testing pipeline, where this PIG gathers velocity data from an odometer-based system, while a supervisory system gathers pressure data from the testing pipeline. Then we train a Multilayer Perceptron (MLP) and a Nonlinear Autoregressive Network with eXogenous Inputs (NARX) network with the gathered data to predict velocity. The results suggest it is possible to use a neural network to model the PIG&rsquo

s velocity from pressure differential measurements. Our method is a new approach to the typical speed measurements based only on odometer, since the odometer is prone to fail and present poor results under some circumstances. Moreover, it can be used to provide redundancy, improving reliability of data obtained during the test.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_240636272-beopen185-6804-document.pdf</pdf>

== Original document ==

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

* [http://dx.doi.org/10.3390/s18093072 http://dx.doi.org/10.3390/s18093072] under the license https://creativecommons.org/licenses/by

* [http://europepmc.org/articles/PMC6165500 http://europepmc.org/articles/PMC6165500] under the license http://creativecommons.org/licenses/by/3.0/

* [https://dx.doi.org/10.3390/s18093072 https://dx.doi.org/10.3390/s18093072]

* [https://www.mdpi.com/1424-8220/18/9/3072/pdf https://www.mdpi.com/1424-8220/18/9/3072/pdf]

* [http://www.mdpi.com/1424-8220/18/9/3072 http://www.mdpi.com/1424-8220/18/9/3072],
: [https://doaj.org/toc/1424-8220 https://doaj.org/toc/1424-8220] under the license cc-by

* [http://www.mdpi.com/1424-8220/18/9/3072/pdf http://www.mdpi.com/1424-8220/18/9/3072/pdf],
: [http://dx.doi.org/10.3390/s18093072 http://dx.doi.org/10.3390/s18093072]

* [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165500 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165500],
: [https://dblp.uni-trier.de/db/journals/sensors/sensors18.html#AraujoFLSNM18 https://dblp.uni-trier.de/db/journals/sensors/sensors18.html#AraujoFLSNM18],
: [https://academic.microsoft.com/#/detail/2891849696 https://academic.microsoft.com/#/detail/2891849696] under the license https://creativecommons.org/licenses/by/4.0/

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Lima et al 2018a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 240636272 to Lima et al 2018a

Scipediacontent: Created page with " == Abstract == Industrial pipelines must be inspected to detect typical failures, such as obstructions and deformations, during their lifetime. In the petroleum industry, th..."