Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm - Lu, Feng; Wu, Jindong; Huang, Jinquan; Qiu, Xiaojie (Aerospace science and technology, 2019)
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.