Maximizing Performance and Efficiency: An Algorithm Approach to Engine Sensor Optimization using Machine Learning

This paper presents an algorithmic methodology developed to reduce the number of sensors required in automotive engines by leveraging machine learning techniques. The sensor data used in this technique was obtained from a standard engine, which exhibited redundancy in data. The high cost associated with sensors and their integration into engine systems necessitates an efficient approach to optimize sensor utilization while maintaining reliable engine performance. By utilizing advanced data analysis and predictive modeling, our algorithm aims to identify redundant or non-critical sensors, enabling a streamlined and cost-effective sensor configuration. We achieved this by developing a tailored dimensionality reduction algorithm based on functional dependency theory. This approach transforms data from a high-dimensional space into a lower-dimensional space, preserving essential features of the original data and ideally approximating its intrinsic dimension.