“Ai Based Predictive Maintenance And Anomaly Detection For Fleet Vehicles”

Authors: Narasimha Nayaka M S, Nirmitha H R, Rohini B R, Suguna G H

Abstract: This project presents a low-cost AI and IoT-based predictive maintenance system for fleet vehicles using ESP32 and embedded machine learning techniques. The system continuously monitors important vehicle parameters such as temperature, vibration, speed, and GPS location using multiple sensors integrated with the ESP32 microcontroller. Sensor data is processed in real time and analyzed using a machine learning model developed with Edge Impulse Studio for anomaly detection and fault prediction. The system uses the Blynk IoT platform for cloud-based monitoring, live data visualization, and alert notifications. A Hall Effect sensor is used for speed detection, while the MPU6050 sensor measures abnormal vibrations in the vehicle system. The GPS module enables real-time vehicle tracking and location monitoring. During abnormal conditions, the system automatically activates buzzer and LED alerts and controls the motor through relay and MOSFET circuits for safety protection. The proposed system reduces unexpected breakdowns, minimizes maintenance costs, and improves operational efficiency and vehicle safety. The project is developed using affordable and easily available hardware components, making it suitable for smart transportation and industrial monitoring applications. The developed prototype demonstrates reliable real-time monitoring, intelligent fault detection, and remote fleet management capabilities.

DOI: https://doi.org/10.5281/zenodo.20178811