Aircrafts are one of the most important means of transportation today. For aircrafts to be able to serve safely, their maintenance must be done in a timely and complete manner. In addition to regular maintenance, it may appear suddenly; there is also irregular maintenance performed in cases such as lightning strikes, bird strikes, and hard landings. Engine failures and maintenance has great importance in aircraft maintenance. Using the data recorded during the flight by flight data recorder, the engine health condition is monitored and the necessary maintenance procedures are carried out. In this study, the exhaust gas temperature was estimated using various data mining algorithms. Because exhaust gas temperature is one of the important parameters used to monitor the aircraft engine health condition. The obtained mining results show the Random Forest Algorithm has best estimation performance. With mining of exhaust gas temperature value, faults can be detected before costly maintenance and accidents. So preventive maintenance methods will be applied, aircraft engines will remain healthy, a significant reduction in the maintenance cost of the operator will be achieved, as well as flight safety and environmental protection.


This work was supported by Research Fund of the Erciyes University, Project no. FDK-2020-9981.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.