Author: Dr. Ananya Sharma, Rahul Verma
Abstract: Predictive maintenance in the automotive industry leverages the capabilities of IoT sensors and big data analytics to anticipate potential failures before they occur, reducing downtime, costs, and safety risks. This paper explores the architecture, benefits, and challenges of integrating IoT-enabled predictive maintenance in modern vehicles. It discusses how real-time data collection, cloud computing, and machine learning algorithms can work together to monitor vehicle health, detect anomalies, and predict component failures with high accuracy. Furthermore, the paper highlights case studies from leading automotive companies and outlines the technological, operational, and regulatory considerations for successful implementation. The findings indicate that predictive maintenance systems can enhance vehicle performance, prolong lifespan, and reduce operational costs, paving the way for smarter, safer, and more sustainable transportation systems.
Keywords: IoT, Big Data, Predictive Maintenance, Automotive Systems, Machine Learning, Vehicle Health Monitoring.
Full Issue
| View or download the full issue | PDF 115-118 |