Aeroengine Exhausted Gas Temperature Prediction Using Process Extreme Learning Machine

Abstract

To solve the aeroengine health condition prediction problem, a process extreme learning machine (P-ELM) is proposed based on the process neural networks (PNN) and the extreme learning machine (ELM). The proposed P-ELM has an ability of processing time accumulation effects widely existing in practical systems. The proposed P-ELM has only one unknown parameter which can be calculated directly rather than in the iteration way, thus the training time can be significantly reduced. After being validated via the prediction of Mackey-Glass time series, the proposed P-ELM is utilized to predict the aeroengine exhausted gas temperature, and the test results is satisfied. It has shown by the contrast tests that the proposed P-ELM can outperform the ELM, but has equal performance with the PNN. However, with just one unknown parameter which can be calculated directly, the proposed P-ELM is much easier to use and it needs much less training time. Thus, the proposed P-ELM is more adaptable to the practical situation of aeroengine health condition prediction compared with the PNN.