Based on Wavelet Threshold Denoising-LDA and Bilstm Aircraft Engine Life Prediction

Abstract

Turbine engines operate in extreme environments and require regular maintenance and replacement. It leads to a lot of human and financial investment, and once the aircraft fails during the navigation process, it will cause irreparable losses. Therefore, it is very necessary to propose an engine life prediction model, which can intervene manually before the engine fails, thereby reducing the probability of an air accident. In order to solve the problem, this paper proposes a wavelet threshold denoising-LDA and Bilstm aero-engine life prediction method, and uses the C-MAPSS data set for simulation prediction. Based on this data set, this paper firstly carried out wavelet threshold denoising to the data set to remove noise, and then used LDA to reduce the dimension to screen out the main features. The processed features are input into the bidirectional LSTM network for prediction. By training the above preprocessed data, BIlstm has better adaptability than the convolutional neural network CNN and LSTM, and the prediction error is greatly reduced.