| Abstract: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. |
