Remaining Using Life Prediction of Aero-Engine Based on ALSTM-MHA
In order to improve the accuracy of the remaining life prediction task,a remaining life prediction model was proposed based on attention long and short term memory network and multi-headed self-attention mechanism(ALSTM-MHA),which could ex-tract the importance of feature dimensions and correlation information of time dimensions under the condition of using data temporality.The model was experimentally validated using the C-MAPSS dataset and analyzed in comparison with other methods.The results show that the ALSTM-MHA model can effectively extract the attention information in feature and time dimensions,and the root mean square error and asymmetric evaluation indexes are reduced by at least 0.3％and 20.48％,respectively,compared with other methods,which verifies the feasibility and effectiveness of the model.
aero-engineattention long short term memorymulti-headed self-attentionremaining life prediction
万方数据
维普
