/Predictive-Maintenance-of-Aircraft-engine-using-LSTM-networks

LSTM network is used to predict the RUL of turbofan engines.

Primary LanguageJupyter Notebook

Predictive-Maintenance-of-Aircraft-engine-using-LSTM-networks

CS6301 - Machine Learning Course Project

Aspects related to the maintenance scheduling have become a crucial problem especially in those sectors where the fault of a component can compromise the operation of the entire system, or the life of a human being. Current systems have the ability to warn only when the failure has occurred causing, in the worst case, an offline period that can cost a lot in terms of money, time, and security. Recently, new ways to address the problem have been proposed thanks to the support of machine learning techniques, with the aim to predict the Remaining Useful Life (RUL) of a system by correlating the data coming from a set of sensors attached to several components

We have adopted a machine learning approach through the use of Long Short Term Memory (LSTM) networks to analyze sensor time series sequences to estimate the RUL of turbofan engines.

Screenshot 2023-03-22 at 12 59 28 AM

Reference (Base paper)

D. Bruneo and F. De Vita, "On the Use of LSTM Networks for Predictive Maintenance in Smart Industries," 2019 IEEE International Conference on Smart Computing (SMARTCOMP), Washington, DC, USA, 2019, pp. 241-248, doi: 10.1109/SMARTCOMP.2019.00059.