Machine Learning-Based Predictive Analysis of Car Braking System

Abstract

Predictive maintenance of vehicle braking systems using machine learning approaches is the focus of this research.   To foresee any issues before they happen, the study analyses data from vehicle braking systems' sensors using a number of machine learning techniques. Data on temperature, pressure, vibration patterns, and wear metrics were included in a comprehensive dataset that included readings from several vehicle kinds' brake systems.  Based on the research, the best predicted accuracy was achieved by Random Forest and Neural Network models, at 92.7% and 91.5%, respectively. This allowed for maintenance interventions to be performed about 320 hours before critical failure thresholds. Over the course of six months, our predictive technology reduced maintenance costs by 34% and uncovered brake problems by 78% in a fleet of fifty test vehicles.   Machine learning has enormous promise for enhancing car safety systems and reducing operational costs through proactive maintenance scheduling, as demonstrated in this study.