RESEARCH PAPER
Predictive modelling of turbofan engine components condition using machine and deep learning methods
1
Institute of Heat Engineering, Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, Nowowiejska 21/25, 00-665 Warsaw, Poland
2
General Electric Company Polska sp. z. o. o., Al. Krakowska 110/114, 02-256 Warsaw, Poland
Publication date: 2021-06-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(2):359-370
HIGHLIGHTS
- 0-10 condition rank of a turbofan life limiting component is predicted.
- Environmental and engine sensors data preceding the condition observation are used.
- Ensemble meta-model of neural networks shown the best performance.
- Support vector machines and gradient boosted models did not match neural nets.
- Linear model demonstrated the worst performance among considered models
KEYWORDS
reliabilityprognosticsdeep learningmachine learninggas turbineturbofan engineneural networkcondition-based maintenance
ABSTRACT
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.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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