Published date: Jul 24 2024

Journal Title: ESPOCH Congresses: The Ecuadorian Journal of S.T.E.A.M.

Issue title: Volume 3, Issue 3

Pages: 34–59

DOI: 10.18502/espoch.v3i3.16614

Hugo Meramerahugo1d@gmail.comInvestigador independiente, Escuela Superior Politécnica de Chimborazo (ESPOCH), 060155, Riobamba, Ecuador.

Félix Garcíamerahugo1d@gmail.com

Edisson CalderónInvestigador independiente, Escuela Superior Politécnica de Chimborazo (ESPOCH), 060155, Riobamba, Ecuador.

The application of artificial intelligence is a recent improvement in the industry, allowing preventive maintenance to be applied as a reliability method for detecting failures in hydraulic systems. This is achieved by using artificial neural networks (ANN) as classifiers to make automatic binary and categorical decisions. Since these systems have multiple conditions and sub-conditions that can be complex for normal analysis, the UCI repository database is relied upon to construct an intelligent algorithm of artificial neural networks with deep learning. This has proven to be a highly effective way of predicting failures, with an overall accuracy rate of 97% when applying the intelligent model to the system. As a result, it can be concluded that deep learning is much more efficient than classical machine learning.

Keywords: artificial intelligence, predictive maintenance, artificial neural networks, deep learning.

Resumen

La aplicación de la inteligencia artificial es la nueva mejora en la industria, permitiendo que el mantenimiento preventivo se aplique como método de confiabilidad para la detección de fallos en sistemas hidráulicos aplicando Redes neuronales artificiales (ANN), utilizándoles como clasificadores para obtener una toma de decisiones automáticas de manera binaria y categórica, ya que dichos sistemas poseen varias condiciones y subcondiciones que se vuelven complejas para un análisis normal, apoyándose en la base de datos del repositorio de la UCI, siendo analizados para la construcción de un algoritmo inteligente de redes neuronales artificiales con Deep Learning (aprendizaje profundo), demostrando así un alto desenvolvimiento en la predicción de fallos, obteniéndose un 97% de exactitud (accuracy) de manera general en la aplicación del modelo inteligente al sistema. Se concluye que la aplicación del aprendizaje profundo es mucho más eficiente comparado con el aprendizaje automático clásico.

Palabras Clave: Inteligencia artificial, mantenimiento predictivo, Redes Neuronales Artificiales, Aprendizaje profundo.

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