| YANG Shuo， GAO Cheng.Remaining Useful Life Prediction of Aeroengine during Overhaul Based on Long Short-Term Memory Network and Light Gradient Boosting Machine[J].航空发动机,2024,50(3):87-92 |
| Remaining Useful Life Prediction of Aeroengine during Overhaul Based on Long Short-Term Memory Network and Light Gradient Boosting Machine |
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| Key Words:remaining useful life prediction combined model light gradient boosting machine long short-term memory network aeroengine |
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| Abstract:In response to the challenges of predicting the Remaining Useful Life （RUL） during the overhaul period of aeroengines，
characterized by numerous state variables and significant nonlinear features， and the limitations of single models in insufficient feature
extraction and inadequate prediction accuracy， a novel combined model approach integrating Long Short-Term Memory networks （LSTM）
and Light Gradient Boosting Machine （LightGBM） for RUL prediction was proposed. The proposed method utilizes LSTM for initial feature
extraction from raw data， where the features extracted from the output gate of LSTM are subsequently fed into the LightGBM model for
RUL prediction. Simulation experiments were conducted using real engine datasets provided by NASA to predict the RUL of individual
engines. The efficacy of the model was validated by comparing its predictions with those of six other models. The results show that the
combined LSTM and LightGBM model significantly reduces prediction errors， achieving Root Mean Square Error （RMSE） values of
12.45， 20.23， 12.58， and 21.75 across four datasets， thereby outperforming other models. |
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