IoT and AI-Based Predictive Maintenance System Design for Express Auto Repair Shops
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Abstract
The purpose of this paper is to present a modular system that acquires data from various machines used in an express mechanical workshop. These data are classified and processed using artificial intelligence and IoT (internet of things), enabling the creation of predictive maintenance plans and operational schemes. This approach helps reduce operational costs, maintenance expenses, and energy consumption throughout the process
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La Revista Técnica "energía" está bajo licencia internacional Creative Commons Reconocimiento-NoComercial 4.0.
References
J. C. Cruz y A. M. Garcia, "Machine Learning for Predictive Maintenance to Enhance Energy Efficiency in Industrial Operations," Information Technology Engineering Journals (ITEJ), vol. 9, no. 1, pp. 15–22, 2024
F. Artkin, "Applications of Artificial Intelligence in Mechanical Engineering," European Journal of Science and Technology, vol. 45, pp. 159–163, 2022.
J. H. Rodríguez Ovalle y L. M. Dávila García, “Gestión del mantenimiento automotriz: Un acercamiento al mantenimiento preventivo 4.0 y los carros conectados,” Escuela Colombiana de Ingeniería Julio Garavito, 2020. Disponible en: https://catalogo.escuelaing.edu.co.
“CDPA: Centro de Desarrollo Productivo de Mantenimiento Automotriz Estrategia para la competitividad de la microempresa,” Revista Científica de la Universidad del Norte, 2020. Disponible en: https://rcientificas.uninorte.edu.co.
K. D. P. Mariano, F. L. N. Almada, y M. A. Dutra, "Smart Air Quality Monitoring for Automotive Workshop Environments," Institute of Informatics – Federal University of Goiás (UFG), Goiânia – GO, Brazil, 2024.
A. Petrov y I. Novak, "Optimization of Industrial Energy Efficiency Through the Application of Advanced Process Control, Monitoring Technologies, and Predictive Maintenance," Eigenpub Review of Science and Technology, vol. 6, no. 1, pp. 1–10, 2022.
M. Soori, F. K. Ghaleh Jough, R. Dastres, y B. Arezoo, "AI-Based Decision Support Systems in Industry 4.0, A Review," Journal of Economy and Technology, 2024.
T. Yamamoto, H. Hayama, T. Hayashi, y T. Mori, "Automatic Energy-Saving Operations System Using Robotic Process Automation," Energies, vol. 13, no. 2342, pp. 1–14, 2020.
S. Thomas, A. O. Philip, y N. Vishwanath, "ML Based Data Driven Energy Centered Predictive Maintenance," Proceedings of the Second International Conference on Edge Computing and Applications (ICECAA 2023), vol. 2, pp. 994–1003, 2023.
A. P., N. Chouhan, G. A. Chandhok, D. Sugumaran, U. Aswal, and S. A., “Empowering IoT Devices with Energy-Efficient AI and Machine Learning,” 2024 International Conference on Circuit Power and Computing Technologies (ICCPCT), DOI: 10.1109/ICCPCT61902.2024.10672916.
S. Gennitsaris et al., "Energy Efficiency Management in Small and Medium-Sized Enterprises: Current Situation, Case Studies and Best Practices," Sustainability, vol. 15, no. 3727, pp. 1–26, 2023. [11] J. O. Williams, “Narrow-band analyzer,” Ph.D. dissertation, Dept. Elect. Eng., Harvard Univ., Cambridge, MA, 1993.
