Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm

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 RL...

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Vydáno v:Aerospace science and technology Ročník 84; s. 661 - 671
Hlavní autoři: Lu, Feng, Wu, Jindong, Huang, Jinquan, Qiu, Xiaojie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Masson SAS 01.01.2019
Témata:
Aircraft engine
Kalman filter
Online sequential learning
Prognostics
Remaining useful life
Online sequential learning
Aircraft engine
Remaining useful life
Kalman filter
Prognostics
ISSN:1270-9638, 1626-3219
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Popis
Shrnutí: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.
ISSN:1270-9638
1626-3219
DOI:10.1016/j.ast.2018.09.044