Abstract
NASA's turbofan engine is a vital equipment used in its aircraft fleet. This engine is designed to provide the required thrust for various missions, from scientific research to astronaut training. However, this engine requires regular maintenance to ensure its optimal performance and safe operation. In this paper, we will find the remaining useful life of the turbofan engine by applying data science techniques and machine learning algorithms for predicting more accurate maintenance requirements. We will examine the performance metrics of different machine learning models and tune the parameters of the best model using random search. We will be deployed as an application using Streamlit. The final result of the web application is that it provides the results of the predictions done by the model as a csv file along with the model loss and accuracy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal For Multidisciplinary Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
