Data-Driven Engine Health Monitoring with AI

Artificial Intelligence (AI) can change how automotive engines are maintained and monitored in the future. This paper presents an Adaboost-based model to monitor engine health with real-world data. Key engine metrics such as temperature, pressure, and other operational data can be analysed to detect early signs of potential problems. Machine learning algorithms can be used to identify patterns and anomalies that might be missed by traditional methods, thus leading to more accurate predictions of engine issues. Maintenance schedules can be optimised, resulting in reduced downtime and costs. This work highlights the importance of data-driven methods in the future of automotive maintenance and efficiency. The application of AI in this context also enhances safety by preventing unexpected engine failures. Importantly, AI-driven solutions are designed to be scalable, making them applicable to a wide range of vehicles and engine types. The automotive industry can move towards more proactive and efficient maintenance practices by exploiting the available data.