STUDY of the Possibility of Implementing the Prediction of Wear of Car Parts Based on Quality and Use Patterns Through IoT Technologies

This paper proposes a method for predicting the wear and tear of automotive consumables and wear parts based on their quality, driving style and road conditions, integrating IoT sensors for real-time monitoring. The developed model aims to analyze historical and real-time data to estimate the components' lifetime and provide personalized maintenance recommendations. Using noise, vibration and temperature sensors, Machine Learning algorithms and statistical analysis, the model can optimize the predictive maintenance strategy, thus reducing unforeseen costs and extending the vehicle's lifespan. This method overcomes the limitations of standardized manufacturer warranties and offers a practical and adaptable solution for the automotive industry.