A Survey on Maintenance of Aircraft Engines Using LSTM

Abstract

What if a part of aircraft could let you know  when the aircraft component needed to be replaced or  repaired? It can be done with continuous data collection,  monitoring, and advanced analytics. In the aviation  industry, predictive maintenance promises increased  reliability as well as improved supply chain and  operational performance. The main goal is to ensure that the engines work correctly under all conditions and there  is no risk of failure. If an effective method for predicting  failures is applied, maintenance may be improved. The  main source of data regarding the health of the engines  is measured during the flights. Several variables are  calculated, including fan speed, core speed, quantity and  oil pressure and, environmental variables such as outside  temperature, aircraft speed, altitude, and so on.

 Sensor data obtained in real time can be used to model  component deterioration. To predict the maintenance of  an aircraft engine, LSTM networks is used in this paper.  A sequential input file is dealt with by the LSTM model.  The training of LSTM networks was carried out on a high performance large-scale processing engine. Machines,  data, ideas, and people must all be brought together to  understand the importance of predictive maintenance and  achieve business results that matter.