Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Chemical Engineering |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322014000100014 |
Resumo: | Considering the importance of monitoring pipeline systems, this work presents the development of a technique to detect gas leakage in pipelines, based on an acoustic method, and on-line prediction of leak magnitude using artificial neural networks. On-line audible noises generated by leakage were obtained with a microphone installed in a 60 m long pipeline. The sound noises were decomposed into sounds of different frequencies: 1 kHz, 5 kHz and 9 kHz. The dynamics of these noises in time were used as input to the neural model in order to determine the occurrence and the leak magnitude. The results indicated the great potential of the technique and of the developed neural network models. For all on-line tests, the models showed 100% accuracy in leak detection, except for a small orifice (1 mm) under 4 kgf/cm² of nominal pressure. Similarly, the neural network models could adequately predict the magnitude of the leakages. |
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Brazilian Journal of Chemical Engineering |
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Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processingPipeline networkLeak detectionNeural networksConsidering the importance of monitoring pipeline systems, this work presents the development of a technique to detect gas leakage in pipelines, based on an acoustic method, and on-line prediction of leak magnitude using artificial neural networks. On-line audible noises generated by leakage were obtained with a microphone installed in a 60 m long pipeline. The sound noises were decomposed into sounds of different frequencies: 1 kHz, 5 kHz and 9 kHz. The dynamics of these noises in time were used as input to the neural model in order to determine the occurrence and the leak magnitude. The results indicated the great potential of the technique and of the developed neural network models. For all on-line tests, the models showed 100% accuracy in leak detection, except for a small orifice (1 mm) under 4 kgf/cm² of nominal pressure. Similarly, the neural network models could adequately predict the magnitude of the leakages.Brazilian Society of Chemical Engineering2014-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322014000100014Brazilian Journal of Chemical Engineering v.31 n.1 2014reponame:Brazilian Journal of Chemical Engineeringinstname:Associação Brasileira de Engenharia Química (ABEQ)instacron:ABEQ10.1590/S0104-66322014000100014info:eu-repo/semantics/openAccessSantos,R. B.Sousa,E. O. deSilva,F. V. daCruz,S. L. daFileti,A. M. F.eng2014-03-20T00:00:00Zoai:scielo:S0104-66322014000100014Revistahttps://www.scielo.br/j/bjce/https://old.scielo.br/oai/scielo-oai.phprgiudici@usp.br||rgiudici@usp.br1678-43830104-6632opendoar:2014-03-20T00:00Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ)false |
dc.title.none.fl_str_mv |
Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing |
title |
Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing |
spellingShingle |
Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing Santos,R. B. Pipeline network Leak detection Neural networks |
title_short |
Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing |
title_full |
Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing |
title_fullStr |
Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing |
title_full_unstemmed |
Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing |
title_sort |
Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing |
author |
Santos,R. B. |
author_facet |
Santos,R. B. Sousa,E. O. de Silva,F. V. da Cruz,S. L. da Fileti,A. M. F. |
author_role |
author |
author2 |
Sousa,E. O. de Silva,F. V. da Cruz,S. L. da Fileti,A. M. F. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Santos,R. B. Sousa,E. O. de Silva,F. V. da Cruz,S. L. da Fileti,A. M. F. |
dc.subject.por.fl_str_mv |
Pipeline network Leak detection Neural networks |
topic |
Pipeline network Leak detection Neural networks |
description |
Considering the importance of monitoring pipeline systems, this work presents the development of a technique to detect gas leakage in pipelines, based on an acoustic method, and on-line prediction of leak magnitude using artificial neural networks. On-line audible noises generated by leakage were obtained with a microphone installed in a 60 m long pipeline. The sound noises were decomposed into sounds of different frequencies: 1 kHz, 5 kHz and 9 kHz. The dynamics of these noises in time were used as input to the neural model in order to determine the occurrence and the leak magnitude. The results indicated the great potential of the technique and of the developed neural network models. For all on-line tests, the models showed 100% accuracy in leak detection, except for a small orifice (1 mm) under 4 kgf/cm² of nominal pressure. Similarly, the neural network models could adequately predict the magnitude of the leakages. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-03-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322014000100014 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322014000100014 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0104-66322014000100014 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Brazilian Society of Chemical Engineering |
publisher.none.fl_str_mv |
Brazilian Society of Chemical Engineering |
dc.source.none.fl_str_mv |
Brazilian Journal of Chemical Engineering v.31 n.1 2014 reponame:Brazilian Journal of Chemical Engineering instname:Associação Brasileira de Engenharia Química (ABEQ) instacron:ABEQ |
instname_str |
Associação Brasileira de Engenharia Química (ABEQ) |
instacron_str |
ABEQ |
institution |
ABEQ |
reponame_str |
Brazilian Journal of Chemical Engineering |
collection |
Brazilian Journal of Chemical Engineering |
repository.name.fl_str_mv |
Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ) |
repository.mail.fl_str_mv |
rgiudici@usp.br||rgiudici@usp.br |
_version_ |
1754213174263939072 |