34 / 2021-06-22 20:38:32

Remaining Useful Life Prediction of Aeroengine Based on Ghost Approach

Aero engine is an important part related to aircraft flight safety. Accurately predict the remaining service life of the engine, which is of great significance in real life. Aiming at the current problem of poor real-time RUL prediction, a RUL prediction model based on the Ghost method is proposed. The Ghost method can take advantage of the redundancy of feature maps in convolutional neural networks, and use simple operations to obtain ghost feature maps, instead of redundant feature maps in traditional convolutional neural networks, and fully reduce the internal trainable parameters of the model, and improve training speed. Validated using the C-MAPSS data set, the results show that, compared with the existing methods, the Ghost model has the smallest root mean square error and scoring function. While maintaining high-precision RUL prediction performance, the training speed of the model is faster than other methods.