Técnicas y aplicaciones de Machine Learning en el campo de la Mecatrónica: Una Revisión Sistemática de Literatura
DOI:
https://doi.org/10.23857/dc.v10i3.4024Palabras clave:
Aprendizaje Automático, Mecatrónica, Metodología PRISMA, Procesos Industriales, Revisión Sistemática de Literatura (SLR)Resumen
A nivel global existe la necesidad de mejorar los procesos industriales. Es por ello que, la combinación de tecnologías de la información, mecánica y electrónica, son pieza fundamental para afrontar desafíos que se presentan en empresas y organizaciones. Este nuevo campo se ha denominado Mecatrónica. Así también, la aplicación de técnicas de Machine Learning (ML) se ha tornado un eje primordial para el análisis de la información tanto a nivel descriptivo como predictivo. El problema radica en que no existe actualmente una visión consolidada y sistematizada a nivel científico sobre las técnicas y aplicaciones específicas de ML en el ámbito de la Mecatrónica. Esta investigación tiene por objetivo identificar cuáles son las técnicas, aplicaciones y desafíos de ML en el campo de la Mecatrónica mediante la revisión sistemática de literatura (SLR), considerando investigaciones y casos de estudio de alto impacto. Para ello se aplicó la metodología PRISMA, y se establece una cadena de búsqueda, con mecanismos sistemáticos de revisión y selección, hasta la obtención de los estudios primarios. Los resultados obtenidos indican que las técnicas de ML más relevantes en el ámbito de la Mecatrónica incluyen Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) y Decision Trees, aplicadas en tareas como control, detección, automatización, optimización, reconocimiento, diagnóstico, mantenimiento, pronóstico, entre otros. Los principales desafíos son la adaptabilidad e integración con otras tecnologías.
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Derechos de autor 2024 Adrián Ramiro Granja Rojas, Elba María Bodero Poveda
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