Remaining Useful Life Prediction of Aero-engine Multi-information Fusion Based on KPCA-BLSTM

Abstract

The inaccurate life prediction caused by multiple degradation information can be easily appeared during the operation of complex aero-engine,a multi-information fusion life prediction model based on kernel principal component analysis(KPCA)and bidirectional long short-term memory(BLSTM)neural network is proposed.Firstly,the kernel principal component method is used to perform dimensionality reduction and information fusion on the multi-dimensional degraded data set to obtain a low-dimensional feature data set that can characterize equipment degradation.Then,the BLSTM neural network is used to predict the remaining useful life(RUL)of aero-engine with multi-dimensional degradation information to obtain the mapping relationship between the monitoring data and the remaining life.Finally,the C-MAPSS aero-engine degradation data set is used to simulate and verify the proposed multi-information fusion life prediction model,and the results are compared with other three models.The results show that the proposed KPCA-BLSTM neural network can predict RUL under multi-dimensional degradation information accurately,the error and score of the proposed model are better than the other three models,and the model has higher prediction accuracy.

Keywords

• aero-engine
• remaining useful life
• multi-information fusion
• bidirectional long short-term memory
• kernel principal component analysis