SMART MECHANICAL GOVERNOR

The conventional mechanical governor, while robust, suffers from limitations in dynamic response and optimal performance under varying operational conditions. This project presents the design and implementation of a Smart Mechanical Governor that leverages Machine Learning (ML) for automated, real-time performance tuning. By integrating sensors to monitor key operational parameters (such as speed, load, and fuel flow) and an actuation mechanism for adjustment, the system creates a closed-loop feedback environment. Supervised learning algorithms are trained on historical performance data to model thecomplex, non-linear relationship between governor settings and system output. This ML model subsequently predicts the optimal calibration settings to achieve target performance metrics, such as enhanced stability, reduced settling time, and improved fuel efficiency. The proposed system aims to overcome the static nature of traditional governors, enabling self-optimization that adapts to engine wear and changing environments. The results demonstrate that the ML-driven approach significantly outperforms static calibration, offering a transformative upgrade for internal combustion engines in automotive, aerospace, and industrial power generation applications.