- Tytuł:
- Predictive modelling of turbofan engine components condition using machine and deep learning methods
- Autorzy:
-
Matuszczak, Michał
Żbikowski, Mateusz
Teodorczyk, Andrzej - Powiązania:
- https://bibliotekanauki.pl/articles/1841686.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
- Tematy:
-
reliability
prognostics
deep learning
machine learning
gas turbine
turbofan engine
neural network
condition-based maintenance - Opis:
- The article proposes an approach based on deep and machine learning models to predict a component failure as an enhancement of condition based maintenance scheme of a turbofan engine and reviews currently used prognostics approaches in the aviation industry. Component degradation scale representing its life consumption is proposed and such collected condition data are combined with engines sensors and environmental data. With use of data manipulation techniques, a framework for models training is created and models' hyperparameters obtained through Bayesian optimization. Models predict the continuous variable representing condition based on the input. Best performed model is identified by detemining its score on the holdout set. Deep learning models achieved 0.71 MSE score (ensemble meta-model of neural networks) and outperformed significantly machine learning models with their best score at 1.75. The deep learning models shown their feasibility to predict the component condition within less than 1 unit of the error in the rank scale.
- Źródło:
-
Eksploatacja i Niezawodność; 2021, 23, 2; 359-370
1507-2711 - Pojawia się w:
- Eksploatacja i Niezawodność
- Dostawca treści:
- Biblioteka Nauki
Artykuł