In various fields, such as medical engi-neering or aerospace engineering, it is difficult to
apply the decisions of a machine learning (ML) or a deep learning (DL) model that do not …

Deep learning is quickly becoming essential to human ecosystem. However, the opacity of
certain deep learning models poses a legal barrier in its adoption for greater purposes …

In this paper, a study of prediction of the Remaining Useful Life (RUL) of an aircraft's
turbofan engine is explored by analyzing a data set of a real-life turbofan engine from the …

Abstract Remaining Useful Life (RUL) estimation is directly related with the application of
predictive maintenance. When RUL estimation is performed via data-driven methods and …

This paper takes an approach to the determination on the Remaining Useful Life (RUL) on a
real-life turbo engine model selected from a set of data provided within the public domain for …

Remaining Useful Life (RUL) is used to provide an early indication of failures that required
performing maintenance and/or replacement of the system in advance. Accurate RUL …

Abstract Aircraft Engine Remaining Useful Life is a key factor which strongly affects flight
operations safety and flight operators business decisions. In the article authors decided to …

In the recent years, artificial intelligence, machine learning and IoT technologies have
enabled a great number of industrial applications with profitable results. Predicting the …

Turbofan engines are instrumental in defining the performance of the aircraft and are subject
to intense wear and tear, necessitating the development of efficient maintenance techniques …

In this paper, the use of Deep Neural Net (DNN) to estimate Remaining Useful Life of using
a NASA C-MAPPS dataset training data is explored. The idea is to use DNN and …