Title
Aircraft propulsion health status prognostics and prediction
Authors
[ 1 ] Instytut Napędów i Lotnictwa, Wydział Inżynierii Lądowej i Transportu, Politechnika Poznańska | [ 2 ] Instytut Transportu, Wydział Inżynierii Lądowej i Transportu, Politechnika Poznańska | [ 3 ] Wydział Inżynierii Lądowej i Transportu, Politechnika Poznańska | [ P ] employee | [ SzD ] doctoral school student
Scientific discipline (Law 2.0)
Year of publication
2025
Published in
Article type
scientific article
Publication language
english
Keywords
EN
- ircraft propulsion
- F100 airbreathing engine
- engine health status prediction
- turbofan engine performance data
- engine prognostic health monitoring (EPHM)
Abstract
EN Aircraft propulsion health monitoring and prognostics are critical to ensuring operational reliability, safety, and cost-effectiveness. This study explores innovative methodologies for assessing the health status of turbofan engines, with an emphasis on F-16 aircraft propulsion systems. The proposed approach incorporates trending algorithms and advanced data analysis techniques to identify degradation patterns and predict engine failures before they occur. Key contributions include a comprehensive framework for engine performance data trending and novel algorithms for automatic data analysis, enabling accurate detection of anomalies and performance shifts. Utilizing engine monitoring system (EMS) data, including parameters like turbine temperatures, rotor speeds, and pressures, the study demonstrates methods to process and trend performance data. Various trending scenarios, such as scattered data, step changes, and parameter thresholds, are analyzed using statistical and algorithmic models. Case studies highlight the effectiveness of predictive tools like Long Term Slope (LTS), Three Point Average (TPA), and Predicted Value (PV) for timely maintenance actions. Proposed methodologies were verified and confirmed for the engine nozzle crunch failure. This research underlines the potential of incorporating artificial intelligence and neural networks into prognostic models, offering insights into remaining useful life estimation and diagnostics. By applying the presented methodologies, aircraft operators can enhance maintenance strategies, mitigate in-flight failures, and extend engine lifecycle. The findings contribute to advancing prognostic health monitoring systems for contemporary and future aircraft propulsion technologies.
License type
CC BY (attribution alone)
Open Access Mode
open journal
Open Access Text Version
final author's version
Ministry points / journal
100
Impact Factor
1 [List 2023]
System created by Poznań University of Technology
and Poznan Supercomputing and Networking Center
Log in through eKonto to add to SIS