dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorda Silva, H. V.
dc.creatorMorooka, C. K.
dc.creatorGuilherme, I. R.
dc.creatorda Fonseca, T. C.
dc.creatorMendes, JRP
dc.date2014-05-20T15:24:30Z
dc.date2016-10-25T17:58:44Z
dc.date2014-05-20T15:24:30Z
dc.date2016-10-25T17:58:44Z
dc.date2005-12-15
dc.date.accessioned2017-04-05T23:47:38Z
dc.date.available2017-04-05T23:47:38Z
dc.identifierJournal of Petroleum Science and Engineering. Amsterdam: Elsevier B.V., v. 49, n. 3-4, p. 223-238, 2005.
dc.identifier0920-4105
dc.identifierhttp://hdl.handle.net/11449/35097
dc.identifierhttp://acervodigital.unesp.br/handle/11449/35097
dc.identifier10.1016/j.petrol.2005.05.004
dc.identifierWOS:000234302300010
dc.identifierhttp://dx.doi.org/10.1016/j.petrol.2005.05.004
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/878895
dc.descriptionA methodology for pipeline leakage detection using a combination of clustering and classification tools for fault detection is presented here. A fuzzy system is used to classify the running mode and identify the operational and process transients. The relationship between these transients and the mass balance deviation are discussed. This strategy allows for better identification of the leakage because the thresholds are adjusted by the fuzzy system as a function of the running mode and the classified transient level. The fuzzy system is initially off-line trained with a modified data set including simulated leakages. The methodology is applied to a small-scale LPG pipeline monitoring case where portability, robustness and reliability are amongst the most important criteria for the detection system. The results are very encouraging with relatively low levels of false alarms, obtaining increased leakage detection with low computational costs. (c) 2005 Elsevier B.V. All rights reserved.
dc.languageeng
dc.publisherElsevier B.V.
dc.relationJournal of Petroleum Science and Engineering
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectpipeline leakage detection
dc.subjectpattern recognition
dc.subjectfuzzy systems
dc.titleLeak detection in petroleum pipelines using a fuzzy system
dc.typeOtro


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