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Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning
Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, United States.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. NASA Ames Research Center, Moffett Field, United States.
Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, United States.
2019 (English)In: Journal of engineering for gas turbines and power, ISSN 0742-4795, E-ISSN 1528-8919, Vol. 41, no 4, article id 041008Article in journal (Refereed) Published
Abstract [en]

Degradation modeling and prediction of remaining useful life (RUL) are crucial to prognostics and health management of aircraft engines. While model-based methods have been introduced to predict the RUL of aircraft engines, little research has been reported on estimating the RUL of aircraft engines using novel data-driven predictive modeling methods. The objective of this study is to introduce an ensemble learning-based prognostic approach to modeling an exponential degradation process due to wear as well as predicting the RUL of aircraft engines. The ensemble learning algorithm combines multiple base learners, including random forests (RFs), classification and regression tree (CART), recurrent neural networks (RNN), autoregressive (AR) model, adaptive network-based fuzzy inference system (ANFIS), relevance vector machine (RVM), and elastic net (EN), to achieve better predictive performance. The particle swarm optimization (PSO) and sequential quadratic optimization (SQP) methods are used to determine optimum weights that are assigned to the base learners. The predictive model trained by the ensemble learning algorithm is demonstrated on the data generated by the commercial modular aero-propulsion system simulation (C-MAPSS) tool. Experimental results have shown that the ensemble learning algorithm predicts the RUL of the aircraft engines with considerable robustness as well as outperforms other prognostic methods reported in the literature. 

Place, publisher, year, edition, pages
ASME Press, 2019. Vol. 41, no 4, article id 041008
Keywords [en]
remaining useful life prediction, prognostics and health management (PHM), degradation modeling, aircraft engines, ensemble learning
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-71864DOI: 10.1115/1.4041674ISI: 000462020200008Scopus ID: 2-s2.0-85056854179OAI: oai:DiVA.org:ltu-71864DiVA, id: diva2:1267489
Note

Validerad;2018;Nivå 2;2018-12-03 (svasva)

Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2019-04-05Bibliographically approved

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Goebel, Kai

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