10th International Congress on Information and Communication Technology

Authors - Ansh Soni, Krish Modi, Aneri Shah, Nishant Doshi
Abstract - A successful and efficient transportation system depends on the credibility of engines and machinery. With the help of NASA Turbofan Jet Engine dataset, this paper focuses on the predictive maintenance framework to boost transport efficiency by leveraging sensor data. With the help of machine learning algorithms, we predict the Remaining Useful Life (RUL) of engine components based on training the model with appropriate algorithms that prompt scheduled services and maintenance to reduce downtime. Feature engineering techniques and predictions of RUL, Health Index(HI), and degradation score- the proposed model provides a methodology for enhancing system dependability and minimizing maintenance costs. This study provides valuable insights into current transportation setbacks.