Remaining Useful Life Prediction of Aircraft Turbofan Engine Based on Random Forest Feature Selection and Multi-Layer Perceptron

The accurate prediction of the remaining useful life (RUL) of aircraft engines is crucial for improving engine safety and reducing maintenance costs. To tackle the complex issues of nonlinearity, high dimensionality, and difficult-to-model degradation processes in aircraft engine monitoring parameters, a new method for predicting the RUL of aircraft engines based on the random forest algorithm and a Bayes-optimized multilayer perceptron (MLP) was proposed here. First, the random forest algorithm was used to evaluate the importance of historical monitoring parameters of the engine, selecting the key features that significantly impact the engine's lifetime operation cycle. Then, the single exponent smoothing (SES) algorithm was introduced for smoothing the extracted features to reduce the interference of original noise. Next, an MLP-based RUL prediction model was established using a neural network. The Bayes' online parameter updating formula was used to solve the objective function and return the optimal parameters of the MLP training model and the minimum value of the evaluation index RMSE. Finally, the probability density function of the predicted RUL value of the aircraft engine was calculated to obtain the RUL prediction results.The effectiveness of the proposed method was verified and analyzed using the C-MAPSS dataset for turbofan engines. Experimental results show that, compared with several other methods, the RMSE of the proposed method in the FD001 test set decreases by 6.1%, demonstrating that the method can effectively improve the accuracy of RUL prediction for aircraft engines.

Automated summary

This study proposed a new method for predicting the remaining useful life (RUL) of aircraft engines based on the random forest algorithm and a Bayes-optimized multilayer perceptron (MLP). The method selected key features that significantly impact the engine's lifetime operation cycle and established an MLP-based RUL prediction model using a neural network. Experimental results showed that compared to other methods, the proposed method achieved a 6.1% reduction in RMSE, demonstrating its effectiveness in improving the accuracy of RUL prediction for aircraft engines.