{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T21:14:55Z","timestamp":1768684495171,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,9,13]],"date-time":"2018-09-13T00:00:00Z","timestamp":1536796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s18093072","type":"journal-article","created":{"date-parts":[[2018,9,13]],"date-time":"2018-09-13T11:46:04Z","timestamp":1536839164000},"page":"3072","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Pipeline Inspection Gauge\u2019s Velocity Simulation Based on Pressure Differential Using Artificial Neural Networks"],"prefix":"10.3390","volume":"18","author":[{"given":"Renan Pires","family":"De Ara\u00fajo","sequence":"first","affiliation":[{"name":"Departamento de Engenharia de Computa\u00e7\u00e3o e Automa\u00e7\u00e3o, Universidade Federal do Rio Grande do Norte, Lagoa Nova, Natal, Caixa postal 1524 CEP 59078-970, RN, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0867-4080","authenticated-orcid":false,"given":"Victor Carvalho Galv\u00e3o","family":"De Freitas","sequence":"additional","affiliation":[{"name":"Instituto Federal do Rio Grande do Norte, Rua Ant\u00f4nia de Lima Paiva, 155, Nova Esperan\u00e7a, Parnamirim CEP 59143-455, RN, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8584-9868","authenticated-orcid":false,"given":"Gustavo Fernandes","family":"De Lima","sequence":"additional","affiliation":[{"name":"Instituto Federal do Rio Grande do Norte, Rua Ant\u00f4nia de Lima Paiva, 155, Nova Esperan\u00e7a, Parnamirim CEP 59143-455, RN, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9s Ortiz","family":"Salazar","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia de Computa\u00e7\u00e3o e Automa\u00e7\u00e3o, Universidade Federal do Rio Grande do Norte, Lagoa Nova, Natal, Caixa postal 1524 CEP 59078-970, RN, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adri\u00e3o Duarte D\u00f3ria","family":"Neto","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia de Computa\u00e7\u00e3o e Automa\u00e7\u00e3o, Universidade Federal do Rio Grande do Norte, Lagoa Nova, Natal, Caixa postal 1524 CEP 59078-970, RN, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9 Laurindo","family":"Maitelli","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia de Computa\u00e7\u00e3o e Automa\u00e7\u00e3o, Universidade Federal do Rio Grande do Norte, Lagoa Nova, Natal, Caixa postal 1524 CEP 59078-970, RN, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,13]]},"reference":[{"key":"ref_1","unstructured":"De Ara\u00fajo, R.P. 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Neural Netw."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/9\/3072\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:20:15Z","timestamp":1760196015000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/9\/3072"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,13]]},"references-count":27,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2018,9]]}},"alternative-id":["s18093072"],"URL":"https:\/\/doi.org\/10.3390\/s18093072","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,13]]}}}