Remaining Useful Life Prediction of Aeroengines Based on MLP Integrated Random Subspace Decision Trees
To predict the Remaining Useful Life(RUL)of aeroengines,a prediction model based on the integration of Multi-Layer Perceptron(MLP)with random subspace decision trees was constructed to address the issue of selecting a specific feature combination from numerous engine state parameters.This model randomly selects feature subspaces from sampled data to build decision regression trees.The MLP model structure and loss function were established,and the parameters of the MLP model were optimized using the Adaptive Moment Estimation(Adam)algorithm.By integrating the prediction results from multiple decision trees based on the MLP model,the RUL of the engine is obtained.Ablation experiments conducted on the C-MAPSS dataset demonstrate that the random feature subspaces,decision regression trees,and MLP integration modules in the prediction model all contribute to improving prediction metrics such as Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Penalty Score,Goodness of Fit and Accuracy.The results show that when the true RUL cycle is less than 30,the prediction accuracy is improved by 7.46%compared to the predictions from Recurrent Neural Networks(RNN).Compared with other prediction methods,this method exhibits better performance under comprehensive evaluations across multiple metrics,providing an effective solution for multi-parameter prediction of the RUL of aeroengines.
remaining useful life predictionaeroenginerandom subspacedecision treeintegrated methodmulti-layer perceptron
万方数据
维普
