Aircraft Failure Rate Prediction Method Based on CEEMD and Combined Model

Abstract

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.

Scientific and Methodological Approaches for the Creation of Complex Diagnostic and Prognostics Systems of the Technical Condition of Units, Functional Systems and Power Structures of Helicopters

“…The database of the results of various studies and experiments of computational and theoretical methods in the field of diagnostics of the technical condition of units and systems, which are implemented in HUMS-type systems based on the accuracy and reliability of measuring signals with elements of forecasting the development of defects in power structures and functional units of the helicopter, also serves as confirmation of the feasibility and possibility of creating an integrated diagnostic and prognostic system for helicopter equipment, products made of various materials and other information sources in this field of research. As an example, the following works can be cited: research in the field of accuracy of flight data generation [2,3], prognostics of the remaining service life of units [4] and failure rate [5].…”

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

“…Finally, the predicted values of each component are superimposed and integrated to form the final predicted values. These decomposition ensemble methods include empirical mode decomposition and LS-SVM combination [ 34 ], correlation vector EMD and GMDH reconstruction combination [ 35 ], EMD and RVM-GM model [ 36 ], CEEMD and combinatorial model [ 37 ], and other prediction models. These methods can decompose the original aircraft failure rate data into many components with different characteristics, and then use the appropriate prediction model to predict each component.…”

“…Holt-winters seasonal model [28], neural network residual correction AR [29], artificial neural network Weibull regression [30], Weibull-based generalized renewal process (WGRP) [31], sparse direct support vector machine regression [32], generalized weighting least-squares combination [33] Integrated combination model based on decomposition Empirical mode decomposition (EMD) and LS-SVM combination [34], correlation vector EMD and GMDH combination [35], EMD and RVM-GM combination [36], CEEMD and combined model [37] the combination forecasting theory system in 1969, this method has been widely concerned by scholars at home and abroad. Effective combination of different prediction models can be regarded as an effective supplement to the generation process of infinitely approaching real data.…”