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dc.contributor.authorCastro Cros, Martí de
dc.contributor.authorVelasco García, Manel
dc.contributor.authorAngulo Bahón, Cecilio
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Intel·ligència Artificial
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2022-02-04T09:08:24Z
dc.date.available2022-02-04T09:08:24Z
dc.date.issued2021-12-15
dc.identifier.citationDe Castro, M.; Velasco, M.; Angulo, C. Machine-learning-based condition assessment of gas turbine: a review. "Energies", 15 Desembre 2021, vol. 14, núm. 24, p. 8468:1-8468:27.
dc.identifier.issn1996-1073
dc.identifier.otherhttps://www.researchgate.net/publication/357068356_Machine-Learning-Based_Condition_Assessment_of_Gas_Turbines-A_Review
dc.identifier.urihttp://hdl.handle.net/2117/361662
dc.description.abstractCondition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machinelearning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.
dc.description.sponsorshipThis research was funded by Siemens Energy.
dc.language.isoeng
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.lcshGas-turbines
dc.subject.otherArtificial intelligence
dc.subject.otherMachine learning
dc.subject.otherSoft sensor
dc.subject.otherCondition assessment
dc.subject.otherGas turbine
dc.titleMachine-learning-based condition assessment of gas turbine: a review
dc.typeArticle
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacTurbines de gas
dc.contributor.groupUniversitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement
dc.identifier.doi10.3390/en14248468
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.mdpi.com/1996-1073/14/24/8468
dc.rights.accessOpen Access
local.identifier.drac32548676
dc.description.versionPostprint (published version)
local.citation.authorDe Castro, M.; Velasco, M.; Angulo, C.
local.citation.publicationNameEnergies
local.citation.volume14
local.citation.number24
local.citation.startingPage8468:1
local.citation.endingPage8468:27


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