Internet of Things for Vehicle Health Monitoring and Predictive Maintenance in Automotive Engineering
Keywords:
Internet of Things, Vehicle Health Monitoring, Predictive Maintenance, Automotive Engineering, Mechanical Systems, Sensors, Data Analytics, Machine Learning, Reliability, Cost Optimization, Automotive Sensors, Real-Time MonitoringAbstract
The integration of the Internet of Things (IoT) in automotive engineering has significantly transformed the maintenance practices for vehicle health monitoring and predictive maintenance. Traditional reactive maintenance strategies, often leading to unexpected failures and high operational costs, are being replaced by data-driven, proactive approaches. This paper explores the application of IoT technologies in monitoring critical mechanical components in vehicles, such as engines, brakes, and transmission systems. By utilizing sensors and real-time data analytics, IoT enables the early detection of anomalies, predicting potential failures before they occur. A predictive maintenance model, built upon machine learning algorithms, processes sensor data to forecast the remaining useful life of mechanical components. The results demonstrate that IoT-based monitoring systems enhance vehicle reliability, reduce downtime, and optimize maintenance schedules, leading to significant cost savings and improved safety. The paper further discusses the challenges of implementing such systems, including sensor calibration, data accuracy, and communication protocols. This research highlights the promising future of IoT in transforming maintenance practices in the automotive industry, offering a more efficient and sustainable approach to vehicle maintenance.
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