Predictive Maintenance for Nasa’s Turbofan Engine

Author(s)	KAMALKUMAAR V P, VEDA KEERTHI A, NAVANEETHA KANNAN A, YOGESH T, KIRUBAGARI B
Country	India
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.
Keywords	Predictive maintenance, Remaining useful life, Machine Learning, Streamlit, Web Application
Field	Engineering
Published In	Volume 5, Issue 3, May-June 2023
Published On	2023-05-18
DOI	https://doi.org/10.36948/ijfmr.2023.v05i03.3081
Short DOI	https://doi.org/gr9r46

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International Journal For Multidisciplinary Research