Prediction of remaining useful life of aircraft engines based on decision tree feature extraction and RSM_LightGBM
Aiming at the low prediction accuracy of current aircraft engine remaining useful life(RUL)prediction models and the difficulty of extracting sensor monitoring parameters,an aircraft engine RUL prediction model was proposed based on decision tree feature extraction and random search algorithm optimized LightGBM.Firstly,the historical monitoring parameters of aircraft engines were analyzed.The decision tree algorithm was used to calculate the importance contribution degree of monitoring parameters to the engine life cycle,and important features were extracted.Then,the data was normalized to reduce the impact of dimensional differences on the prediction model.Secondly,based on the historical degradation characteristics of aircraft engines,threshold labels were set for the engines to represent their performance degradation characteristics.Finally,the random search algorithm was used to optimize the hyper parameters in LightGBM and obtain the minimum RMSE.Experimental verification was carried out on the CMAPSS dataset.The experimental results showed that,compared with the optimal values obtained by other models,the proposed method had better comprehensive performance in multiple evaluation indicators,and effectively improved the accuracy of aircraft engine RUL prediction.
NETL
NSTL
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