Development of an Automated Testing System for Alternators with Real-Time Monitoring Based on IoT and Electrical Analysis
| International Journal of Electrical and Electronics Engineering |
| © 2025 by SSRG - IJEEE Journal |
| Volume 12 Issue 6 |
| Year of Publication : 2025 |
| Authors : Jeancarlos Gago Salcedo, Kevin Gliserio Maravi Obispo, Jezzy James Huaman Rojas |
10.14445/23488379/IJEEE-V12I6P102
How to Cite?
Jeancarlos Gago Salcedo, Kevin Gliserio Maravi Obispo, Jezzy James Huaman Rojas, "Development of an Automated Testing System for Alternators with Real-Time Monitoring Based on IoT and Electrical Analysis," SSRG International Journal of Electrical and Electronics Engineering, vol. 12, no. 6, pp. 14-24, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I6P102
Abstract:
This research focuses on designing an automated test bench for alternators, which incorporates real-time monitoring using Internet of Things (IoT) technologies, advanced electrical analysis, and intelligent diagnostics based on a Multilayer Perceptron (MLP) artificial intelligence model. The system allows the testing of automotive alternators under various operating conditions, measuring the RPM, voltage, and current generated and sending the data to a web platform via an ESP32 microcontroller. Multiple tests were performed during the experiment at various load levels and speeds, demonstrating a direct relationship between voltage and RPM. Additionally, the PZEM-003/017 achieved a measurement margin of error of less than 1%, and the AI model's fault detection accuracy exceeded 90%. Likewise, a finite element analysis (FEA) of the system's structural framework was performed, validating the rigidity and safety of the structure under specified rigid loads through simulations of tension, displacement, and safety factors. The developed system provides accurate, cost-effective, and scalable diagnostic tools for alternators in industrial maintenance, technical training, and testing environments. The modular architecture, incorporation of dynamic speed control, and real-time predictive analytics capabilities represent a significant improvement over traditional methods.
Keywords:
Alternator, Internet of things, Artificial intelligence, Test bench, Electrical diagnostics.
References:
[1] Elebi Bazán Villacorta, “Design of a Test Bench for the Electric Battery Charging System to Evaluate its Operation in Light Vehicles at the Company Servicios Eléctricos Diésel,” Mechanical Electrical Engineer Thesis, César Vallejo University, 2019.
[Google Scholar] [Publisher Link]
[2] Juan Manuel Galán Ramírez, and Santiago Cárdenas Méndez, “Design of a Test Bench for Electromechanical Parts of an Automobile for CESVI Colombia,” Degree Theses, University of America Foundation, 2017.
[Google Scholar] [Publisher Link]
[3] Jorge Ismael Herrera de la Cruz, “Implementation of a Test Bench for Visualizing the Nominal Values of Electrical Magnitudes of an Alternator,” Bachelor's Thesis, Ecuador: Latacunga: Technical University of Cotopaxi (UTC), 2019.
[Google Scholar] [Publisher Link]
[4] Gonzalo Guardia Palomera, “Design of a Switched Flux Alternator for Mini-Wind Generation,” Bachelor’s Thesis, Polytechnic University of Catalonia, 2016.
[Google Scholar] [Publisher Link]
[5] Victor Joel Ilaya Rodríguez, “Monitoring System for Preventive and Predictive Maintenance Applied to Rotating Machines in Industry Based on IIoT,” Doctoral Dissertation Thesis.
[Google Scholar]
[6] Darko Hercog et al., “Design and Implementation of ESP32-based IoT Devices,” Sensors, vol. 23, no. 15, pp. 1-20, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] T.T. Vu Hong et al., “Autonomous Electrical System Monitoring and Control Strategies to Avoid Oversized Storage Capacity,” IOP Conference Series: Earth and Environmental Science, 2020 6th International Conference on Environment and Renewable Energy 2020, Hanoi, Vietnam, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Emre Cebeci, and Yusuf Yasa, “Sensorless Control of Synchronous Reluctance Motor Based on Active Flux Vector and Extended Kalman Filter,” Journal of Electrical Engineering & Technology, vol. 17, pp. 1207-1215, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Syed Musarat Hussain et al., “A Comprehensive Study on Cracks in Multi-Span Simply Supported Beam Bridges through SolidWorks Analysis,” Conference on Sustainable Traffic and Transportation Engineering, Yinchuan, China, pp. 244-250, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Nermina Zaimović-Uzunović, Ernad Bešlagić, and Almir Porča, “Numerical Analysis of Material Fatigue Impact on Bicycle Frame Safety in Accordance with EN 14764,” International Conference “New Technologies, Development and Applications”, Sarajevo, Bosnia and Herzegovina, pp. 41-49, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Kacper Murat et al., “Security Analysis of Low-Budget IoT Smart Home Appliances Embedded Software and Connectivity,” Electronics, vol. 13, no. 12, pp. 1-27, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Aydın Boyar, Ersan Kabalcı, and Yasin Kabalcı, “Model Predictive Torque Control-Based Induction Motor Drive with Remote Control and Monitoring Interface for Electric Vehicles,” Electric Power Components and Systems, vol. 51, no. 18, pp. 2159-2170, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Cleber Lourenço Izidoro et al., “Development of an Industrial IoT Based Monitoring System for Voltage Regulators,” IEEE Latin America Transactions, vol. 19, no. 8, pp. 1410-1416, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Hussam J. Khasawneh et al., “Industrial IoT-Based Submetering Solution for Real-Time Energy Monitoring,” Discover Internet of Things, vol. 5, no. 1, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Ayman Laaroussi El Barouki, “Implementation of an Intelligent System for Photovoltaic Panels,” Master Thesis, University of Évora, 2024.
[Google Scholar] [Publisher Link]
[16] Xenia Azareth Ayon-Gómez et al., “Sensor for Real-Time Glucose Measurement in Aqueous Media Based on Nanomaterials Incorporating an Artificial Neural Network Algorithm on a System-On-Chip,” Revista Mexicana De Ingenieria Biomedica, vol. 44, no. 4, pp. 70-83, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Md. Ibne Joha et al., “A Secure IoT Environment that Integrates AI-Driven Real-Time Short-Term Active and Reactive Load Forecasting with Anomaly Detection: A Real-World Application,” Sensors, vol. 24, no. 23, pp. 1-33, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Ángeles Arteaga, “Proposal of a Theoretical Framework for Data Quality Assessment in Artificial Intelligence,” Journal Sociencytec, vol. 1, no. 2, pp. 35-50, 2023.
[Google Scholar]
[19] A. Jiménez et al., “High-Speed Machining: Advances in Measurement and Tool Position Control,” Metallurgy and Electricity, vol. 757, pp. 44-47, 2003.
[Google Scholar]
[20] N.A. Plieva, “Calculation of the Average Electromotive Force for a Rotating Convective Envelope,” Magnetohydrodynamics, vol. 23, no. 3, pp. 8-14, 1988.
[Google Scholar]
[21] Xiaobin Le, Fatigue Analysis by FEA Simulation, Simulation-Based Mechanical Design, Springer, Cham, pp. 295-315, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Rebecca Rogers, Edward Apeh, and Christopher J. Richardson, “Resilience of the Internet of Things (IoT) from an Information Security Perspective,” 2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA), Chengdu, China, pp. 110-115, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Rajesh Kumar Mohanty, Ramesh Chandra Mohanty, and Sukanta Kumar Sabut, “Finite Element Analysis and Experimental Validation of Polycentric Prosthetic Knee,” Materials Today: Proceedings, vol. 63, pp. 207-214, 2022.
[CrossRef] [Google Scholar] [Publisher Link]