Pipeline Inspection Gauge’s Velocity Simulation Based on Pressure Differential Using Artificial Neural Networks

De Araújo, Renan Pires; De Freitas, Victor Carvalho Galvão; De Lima, Gustavo Fernandes; Salazar, Andrés Ortiz; Neto, Adrião Duarte Dória; Maitelli, André Laurindo

doi:10.3390/s18093072

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Pipeline Inspection Gauge’s Velocity Simulation Based on Pressure Differential Using Artificial Neural Networks

Renan Pires De Araújo

^1,*,

Victor Carvalho Galvão De Freitas

^2,*,

Gustavo Fernandes De Lima

Andrés Ortiz Salazar

¹,

Adrião Duarte Dória Neto

¹ and

André Laurindo Maitelli

Departamento de Engenharia de Computação e Automação, Universidade Federal do Rio Grande do Norte, Lagoa Nova, Natal, Caixa postal 1524 CEP 59078-970, RN, Brazil

Instituto Federal do Rio Grande do Norte, Rua Antônia de Lima Paiva, 155, Nova Esperança, Parnamirim CEP 59143-455, RN, Brazil

Authors to whom correspondence should be addressed.

Sensors 2018, 18(9), 3072; https://doi.org/10.3390/s18093072

Received: 13 July 2018 / Revised: 8 August 2018 / Accepted: 9 August 2018 / Published: 13 September 2018

(This article belongs to the Section Physical Sensors)

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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’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’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. View Full-Text

Keywords: PIG; neural network; velocity measurement; microcontroller; testing pipeline

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MDPI and ACS Style

De Araújo, R.P.; De Freitas, V.C.G.; De Lima, G.F.; Salazar, A.O.; Neto, A.D.D.; Maitelli, A.L. Pipeline Inspection Gauge’s Velocity Simulation Based on Pressure Differential Using Artificial Neural Networks. Sensors 2018, 18, 3072. https://doi.org/10.3390/s18093072

AMA Style

De Araújo RP, De Freitas VCG, De Lima GF, Salazar AO, Neto ADD, Maitelli AL. Pipeline Inspection Gauge’s Velocity Simulation Based on Pressure Differential Using Artificial Neural Networks. Sensors. 2018; 18(9):3072. https://doi.org/10.3390/s18093072

Chicago/Turabian Style

De Araújo, Renan P., Victor C.G. De Freitas, Gustavo F. De Lima, Andrés O. Salazar, Adrião D.D. Neto, and André L. Maitelli. 2018. "Pipeline Inspection Gauge’s Velocity Simulation Based on Pressure Differential Using Artificial Neural Networks" Sensors 18, no. 9: 3072. https://doi.org/10.3390/s18093072

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