| Autor: |
HU Qiguo, BAI Xiong, DU Chunchao |
| Jazyk: |
čínština |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Hangkong gongcheng jinzhan, Vol 13, Iss 3, Pp 157-163,170 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1674-8190 |
| DOI: |
10.16615/j.cnki.1674-8190.2022.03.18 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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