Abstract: This project develops a comprehensive vehicle predictive maintenance system that leverages IoT sensors, machine learning, and cloud computing to monitor critical vehicle components and predict potential failures before they occur. The system addresses the growing need for proactive maintenance strategies in the automotive industry by monitoring real-time parameters such as brake pad pressure, gearbox usage patterns, clutch stress cycles, and environmental humidity levels using dedicated sensors connected to an ESP32/Arduino microcontroller. Data is transmitted to cloud storage via Wi-Fi for advanced analytics and machine learning-based failure prediction. The system features a web dashboard for real-time monitoring, historical data visualization, and maintenance scheduling recommendations. Multiple alert mechanisms including local buzzers, LED/OLED displays, SMS, and email notifications ensure timely maintenance interventions.
Keywords: IoT, ESP32, Brake and Pressure Sensor, Temperature sensor, Vibration sensor, Gearbox Monitoring, Clutch Health, Firebase Cloud, Machine Learning, Predictive Maintenance.
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DOI:
10.17148/IJARCCE.2025.1412135
[1] Pruthviraj B H, Mrs Preeja Mary R, Hari Prasad M, Gowtham P U, Chethan M K, "Real-Time Advanced Vehicle Predictive Maintenance System," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.1412135
