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| Residual Useful Life Prediction Method of Aero Engine Based on VMD-LSTM |
| Received:March 14, 2025 Revised:March 25, 2025 |
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| DOI：10.7643/issn.1672-9242.2025.05.013 |
| KeyWord:variational mode decomposition aero engine residual life prediction long short-term memory network performance degradation |
| Author | Institution |
| QI Yang |
Naval Aviation University Qingdao Campus, Shandong Qingdao , China |
| ZHU Huayuan |
Naval Aviation University Qingdao Campus, Shandong Qingdao , China |
| WU Shibo |
Naval Aviation University Qingdao Campus, Shandong Qingdao , China |
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| Abstract: |
| The work aims to solve the problem of low accuracy of existing aero engine residual life prediction methods. A new hybrid prediction framework was constructed by deep fusion of variational mode decomposition (VMD) and long short-term memory network (LSTM). Through the technical path combining signal decomposition and deep learning, the scheme effectively improved the extraction capability of engine degradation features and the accuracy of life prediction. Firstly, Variational Mode Decomposition (VMD) was performed for each parameter in aero engine data set. The decomposed data was input into the Long Short-Term Memory (LSTM) network and trained with the corresponding remaining life label. Finally, parameter optimization was realized through parameter comparison and optimization to improve the performance of the model. The experimental results of C-MAPSS showed that the VMD-LSTM model proposed in this paper achieved higher prediction accuracy in RUL prediction tasks of turbofan engines compared with CNN, DCNN, RNN and GRU, demonstrating the powerful capability of the VMD-LSTM model in extracting time-frequency domain features and capturing long-term dependency relationships. The VMD-LSTM model improves the ability to capture complex dynamic changes, and realizes high-precision prediction of aero engine RUL. |
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