• JAYASHANKARA D M
• Gangadhar M L

Aircrafts are more important because they are accomplished of transferring things as well as from one side of the world to the other side of the world in considerably less time compare to other physical transport mediums. Aircrafts are extensively used in defense systems as well as in public transport. Therefore, problem related to safety and security are main concern in the aircraft. Including many facts of security issues, decided for providing and confirming exact operation over their lifetime of engines is the most crucial task. An aircraft engine is one of the most disparate engineering systems that have been developed. Aircraft engines are designed to be used for longer lifespan. Their maintenance is a challenging and costly task for security reasons. As a core element of the aircraft system, turbofan engine has complicated structure and high reliability desires which lead to significant maintenance costs. Because of turbofan engine failure occur, it affects overall functionality of aircraft system. Therefore rescue of turbofan engine is necessary. The intent is to make sure a neat operation of the engines in all conditions with probability of failure is zero. Deep learning techniques are used to predict the aircraft failure. Predictive analytics is the branch of the analytics used to make predictions about unknown upcoming events. It uses many techniques from data mining, modeling, statistics, artificial intelligence and machine learning, to analyze current input data to make predictions about future. Aircraft identifies analytics will measure key parameters like pressure, temperature, physical fan speed etc. to predict lifespan of an aircraft engine and therefore it will help to plan the maintenance accordingly, with very minimal impact to the operations. This helps to avoid the failure of aircraft before it occurs.

Machine learning, AI, Aircraft

"Deep Learning for Predicting Aircraft Failures", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.1118-1150, June 2019, Available :http://www.jetir.org/papers/JETIR1907300.pdf

"Deep Learning for Predicting Aircraft Failures", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp1118-1150, June 2019, Available at : http://www.jetir.org/papers/JETIR1907300.pdf