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http://dx.doi.org/10.20910/JASE.2019.13.6.17

Neural Network based Aircraft Engine Health Management using C-MAPSS Data  

Yun, Yuri (Hyundai Construction Equipment)
Kim, Seokgoo (Department of Aerospace and Mechanical Engineering, Korea Aerospace University)
Cho, Seong Hee (Department of Aerospace and Mechanical Engineering, Korea Aerospace University)
Choi, Joo-Ho (School of Aerospace and Mechanical Engineering, Korea Aerospace University)
Publication Information
Journal of Aerospace System Engineering / v.13, no.6, 2019 , pp. 17-25 More about this Journal
Abstract
PHM (Prognostics and Health Management) of aircraft engines is applied to predict the remaining useful life before failure or the lifetime limit. There are two methods to establish a predictive model for this: The physics-based method and the data-driven method. The physics-based method is more accurate and requires less data, but its application is limited because there are few models available. In this study, the data-driven method is applied, in which a multi-layer perceptron based neural network algorithms is applied for the life prediction. The neural network is trained using the data sets virtually made by the C-MAPSS code developed by NASA. After training the model, it is applied to the test data sets, in which the confidence interval of the remaining useful life is predicted and validated by the actual value. The performance of proposed method is compared with previous studies, and the favorable accuracy is found.
Keywords
C-MAPSS dataset; Neural network; PHM;
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Times Cited By KSCI : 1  (Citation Analysis)
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