Digital twin for Electronic Centralized Aircraft Monitoring by machine learning algorithms

Ugur Kilic, Gorkem Yalin and Omer Cam

Energy, 2023, vol. 283, issue C

Abstract: Electronic Centralized Aircraft Monitoring (ECAM) parameters play a vital role in the operation of an aircraft to reduce the workload of the cockpit crew. A wide-body commercial aircraft with a triple-spool turbofan engine is examined within the scope of the study. This study is focused on the estimation of the ECAM primary engine parameters: Engine Pressure Ratio, Exhaust Gas Temperature, Fuel Flow, and Shaft Speeds without any additional measurement for data continuity. The recorded flight data obtained from a commercial aircraft is processed with machine learning methods, and the most suitable estimation method is tried to be determined. Correlation analysis is carried out for each data in the study to show strong predictor candidates. The modeling process is conducted by using MATLAB. Results indicate that the Fine Decision Tree is better at memorizing data, while the Wide Neural Network is better at generalizing data. Computational results show that the developed models are outstandingly precise and accurate to estimate aircraft's ECAM data to ensure flight safety for health and performance monitoring of an engine. Thus, when an unreliable situation occurs while performing the flight in practical conditions, the cockpit crew will be able to overcome this situation by Digital Twin.

Keywords: Machine learning; Estimation; Turbofan; Primary engine parameters; Digital twin (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223025124
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:283:y:2023:i:c:s0360544223025124

DOI: 10.1016/j.energy.2023.129118

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().