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dc.contributor.authorEnislay, Ramentol
dc.contributor.authorTomas, Olsson
dc.contributor.authorShaibal, Barua
dc.date.accessioned2021-06-02T10:13:42Z
dc.date.available2021-06-02T10:13:42Z
dc.date.issued2021
dc.identifierONIX_20210602_10.5772/intechopen.93043_498
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/49384
dc.description.abstractMore and more industries are aspiring to achieve a successful production using the known artificial intelligence. Machine learning (ML) stands as a powerful tool for making very accurate predictions, concept classification, intelligent control, maintenance predictions, and even fault and anomaly detection in real time. The use of machine learning models in industry means an increase in efficiency: energy savings, human resources efficiency, increase in product quality, decrease in environmental pollution, and many other advantages. In this chapter, we will present two industrial applications of machine learning. In all cases we achieve interesting results that in practice can be translated as an increase in production efficiency. The solutions described cover areas such as prediction of production quality in an oil and gas refinery and predictive maintenance for micro gas turbines. The results of the experiments carried out show the viability of the solutions.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: generalen_US
dc.subject.othermachine learning, prediction, regression methods, maintenance, degradation prediction
dc.titleChapter Machine Learning Models for Industrial Applications
dc.typechapter
oapen.identifier.doi10.5772/intechopen.93043
oapen.relation.isPublishedBy09f6769d-48ed-467d-b150-4cf2680656a1
oapen.relation.isFundedByH2020-SPIRE-2016
oapen.grant.number723523
oapen.grant.acronymFUDIPO


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