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.
关键词
航空发动机/注意力长短时记忆网络/多头自注意力机制/剩余寿命预测
Key words
aero-engine/attention long short term memory/multi-headed self-attention/remaining life prediction
维普
万方数据