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Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm.
- Source :
-
Aerospace Science & Technology . Jan2019, Vol. 84, p661-671. 11p. - Publication Year :
- 2019
-
Abstract
- Abstract Online sequential extreme learning machine (OS-ELM) learns data one-by-one or chunk-by-chunk, and the recursive least square (RLS) algorithm is commonly employed to train the topological parameters of OS-ELM. Since it is hard to guarantee the smallest estimation error of the state variable by the RLS, the regression performance of the OS-ELM easily fluctuates in practical applications. To address this gap, a new training approach of the OS-ELM using Kalman filter called KFOS-ELM is proposed, and state propagation is combined into extreme learning process to obtain the OS-ELM's topological parameters. Besides, an adaptive-weighted ensemble mechanism is developed and used to dynamically tune the weight coefficients of each KFOS-ELM in the learning network. The regression performance of the proposed methodology is evaluated using benchmark datasets. The simulation results show that proposed methods are superior to the OS-ELM and EOS-ELM in terms of the regression accuracy and stability without additional computational efforts. Furthermore, an enhanced multi-sensor prognostic model based on KFOS-ELM and logistic regression (LR) model is designed for remaining useful life (RUL) prediction of aircraft engine. The experimental results confirm our viewpoints. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 12709638
- Volume :
- 84
- Database :
- Academic Search Index
- Journal :
- Aerospace Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 133767014
- Full Text :
- https://doi.org/10.1016/j.ast.2018.09.044