Aircraft Failure Rate Prediction Method Based on CEEMD and Combined Model

Accurate prediction of aircraft failure rate can improve flight safety and spare parts supply efficiency and effectively provide good maintenance and maintenance decisions and health management guidance. In order to achieve accurate prediction of non-linear and non-stationary aircraft failure rate, an aircraft failure rate prediction method based on the fusion of complementary ensemble empirical mode decomposition (CEEMD) and combined model is proposed. Firstly, the complementary set empirical mode is used to decompose the failure rate into multiple components with different frequencies, then the integrated moving average autoregressive model (ARIMA) model and grey Verhulst model are selected to predict different components, the entropy weight method is used to solve the coefficients of the combined model, and finally the prediction results of each prediction model are multiplied by their respective weight coefficients to obtain the final prediction results. The experiment was carried out by taking the actual case application of the failure rate data of the aircraft fuel control system as an example. Seven evaluation functions are used as evaluation criteria to evaluate the performance of the combined model. Experimental results show that the developed combined model is better than other models such as sum of squared error (SSE) and mean absolute error (MAE), which can significantly improve the prediction accuracy of aircraft failure rate. It is proved that the model can improve the accuracy and effectiveness of aircraft failure rate prediction. At the same time, the stability of the model has certain advantages over other models and has a good application prospect.