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Article

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Title

Aircraft Engine Remaining Useful Life Prediction using neural networks and real-life engine operational data

Authors

[ 1 ] Instytut Napędów i Lotnictwa, Wydział Inżynierii Lądowej i Transportu, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.7] Civil engineering, geodesy and transport

Year of publication

2024

Published in

Advances in Engineering Software

Journal year: 2024 | Journal volume: vol. 192

Article type

scientific article

Publication language

english

Keywords
EN
  • prognostic health monitoring
  • engine remaining useful life
  • artificial neural network
  • aircraft turbofan engine
  • engine health status prediction
Abstract

EN Aircraft Engine Remaining Useful Life is a key factor which strongly affects flight operations safety and flight operators business decisions. In the article authors decided to present the concept of engine remaining useful life prediction. Proposed method was created as a result of the analysis of the real turbofan engine operational data collected for a few years which was used as an input data for the deep neural network, in order to train, validate and test machine learning algorithms. Two architectures of deep neural networks were created: multilayered deep convolutional neural networks and a long short-term memory network with regression output. Both neural networks were trained, validated and tested on the same engine data and with a various network training options. Results were compared with the neural network performance metrics and figures presenting network prediction convergence. To present how the real-life engine dataset differs the results from the simulated data, both datasets were validated on the same neural network architectures. The main purpose of this article was to present the idea and method of how the artificial neural networks could be used to predict aircraft remaining useful life indicator on the real-life engine operational data not the simulated one.

Pages (from - to)

103645-1 - 103645--11

DOI

10.1016/j.advengsoft.2024.103645

URL

https://www.sciencedirect.com/science/article/pii/S0965997824000528?dgcid=author

Ministry points / journal

140

Impact Factor

4 [List 2023]

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