Data Analytics Applied to Predictive Maintenance

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dc.contributor.authorAndres Leonardo Alfonso Diaz
dc.contributor.authorMarien Rocio Barrera Gómez
dc.contributor.authorIván David Alfonso Díaz
dc.contributor.authorJerley Andres Mejia Gallo
dc.coverage.spatialBolivia
dc.date.accessioned2026-03-22T18:25:53Z
dc.date.available2026-03-22T18:25:53Z
dc.date.issued2023
dc.description.abstractThis paper describes in a simple way the development of a web application for predictive maintenance of equipment implemented in the electronics laboratory of the Universidad Pedagógica y Tecnológica de Colombia Sogamoso. This application was developed in Python language to achieve the manipulation and processing of large amounts of data. We developed a machine learning algorithm to predict damages in the laboratory equipment and enable the stakeholder toschedule maintenance to these equipments to prevent them from getting damaged. We implemented and compared the results obtained for two models (Random Forest and MLPRegressor Neural Network), being Random Forest the most accurate model.
dc.identifier.doi10.69681/lajae.v6i1.28
dc.identifier.urihttps://doi.org/10.69681/lajae.v6i1.28
dc.identifier.urihttps://andeanlibrary.org/handle/123456789/70070
dc.language.isoen
dc.publisherUniversity of California Press
dc.relation.ispartofLatin american journal of applied engineering.
dc.sourcePedagogical and Technological University of Colombia
dc.subjectPredictive maintenance
dc.subjectDamages
dc.subjectPython (programming language)
dc.subjectPredictive analytics
dc.subjectRandom forest
dc.subjectComputer science
dc.subjectArtificial neural network
dc.subjectAnalytics
dc.subjectMachine learning
dc.subjectArtificial intelligence
dc.titleData Analytics Applied to Predictive Maintenance
dc.typearticle

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