Predictive Maintenance for Vehicles Using IoT And ML

This paper presents an IoT and Machine Learning-based Predictive Maintenance System for vehicles to monitor vehicle health and identify potential faults at an early stage. The proposed system focuses on monitoring essential vehicle parameters, including temperature, vibration intensity, battery voltage, current, fuel level, and gas levels using sensor-based data acquisition. An ESP32 microcontroller collects the sensor readings and transmits the data through Wi-Fi to the ThingSpeak cloud platform for real-time monitoring and analysis. Machine learning techniques are employed to analyze the collected sensor data and classify vehicle conditions as normal or abnormal. Based on the detected variations, the system generates early alerts to indicate potential faults and support timely maintenance decisions. Additionally, a buzzer provides alerts whenever abnormal conditions are detected. The proposed approach aims to reduce unexpected vehicle breakdowns, improve operational reliability, minimize maintenance costs, and enhance vehicle health monitoring. The integration of IoT and Machine Learning provides an intelligent and efficient approach to predictive maintenance in automotive applications.