Predictive Tire Wear Monitoring System

The Predictive Tire Wear Monitoring System introduces a modern approach to improving vehicle safety and efficient tire maintenance through real-time analysis of tire conditions. Unlike conventional methods, which often overlook dynamic factors such as road surfaces, driving styles, and vehicle loads, this system combines data from accelerometers, gyroscopes, vibration sensors, and GPS modules, all managed by an ESP32 microcontroller. Three machine learning algorithms—Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Deep Neural Network (DNN)—were tested to predict tire wear patterns. Among these, DNN demonstrated the highest accuracy and reliability, making it the algorithm of choice for the system. This proactive maintenance strategy reduces tire-related risks, optimizes usage, and lowers costs, while also supporting environmental sustainability. The system is designed to cater to both individual users and fleet operators, offering a scalable, reliable solution for predictive maintenance.