Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/87797
Type: Artigo de periódico
Title: 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.
De Sousa E.O.
Da Silva F.V.
Da Cruz S.L.
Fileti A.M.F.
Abstract: 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.
Editor: Assoc. Brasiliera de Eng. Quimica / Braz. Soc. Chem. Eng.
Rights: aberto
Identifier DOI: 10.1590/S0104-66322014000100014
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-84899973672&partnerID=40&md5=7973e002b2dfa63f28614ac60e71784e
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

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