Abstract
The physical and operational properties of pipelines vary greatly. There is thus no universally applicable method, external or internal, which possesses all the features and the functionality required for a perfect leak detection performance. The authors of this paper know quite well that traditional methods, in a low uncertainty environment, overcome artificial intelligence methods of leak detection systems. If one considers the real world as a creator of uncertainties, neural networks and fuzzy systems emerge as important promising technologies for the development of leak detection systems. In this work, we propose a method for constructing ensembles of ANNs for pipeline leak detection. The results obtained in our experiments were satisfactory.Copyright © 2008 by ASME
Original document
The different versions of the original document can be found in:
- https://asmedigitalcollection.asme.org/IPC/proceedings/IPC2008/48579/739/336443,
- https://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1640428,
- http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1640428,
- https://www.addlabs.uff.br/Novo_Site_ADDLabs/images/documentos/publicacoes/publicacoes_pdf/trabalhos_anais_congresso/2008/20130809154919_2008%20-%20Artificial%20neural%20networks%20ensemble%20used%20for%20pipeline%20leak%20detection%20systems.pdf,
- http://www.addlabs.uff.br/Novo_Site_ADDLabs/images/documentos/publicacoes/publicacoes_pdf/trabalhos_anais_congresso/2008/20130809154919_2008%20-%20Artificial%20neural%20networks%20ensemble%20used%20for%20pipeline%20leak%20detection%20systems.pdf,
- https://academic.microsoft.com/#/detail/2114470142
Document information
Published on 01/01/2008
Volume 2008, 2008
DOI: 10.1115/ipc2008-64664
Licence: CC BY-NC-SA license
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