Accurate prediction of airborne equipment failure rate can provide correct repair and maintenance decisions and effectively establish a health management mechanism. This plays an important role in ensuring the safe use of the aircraft and flight safety. This paper proposes an optimal combination forecasting model, which mixes five single models (Multiple Linear Regression model (MLR), Gray model GM (1, N), Partial Least Squares model (PLS), Artificial Neural Network model (BP), and Support Vector Machine model (SVM)). The combined model and its single model are compared with the other three algorithms. Seven classic comparison functions are used for predictive performance evaluation indicators. The research results show that the combined model is superior to other models in terms of prediction accuracy. This paper provides a practical and effective method for predicting the airborne equipment failure rate.

The isothermal curing process of T700 carbon fiber/P450 polyimide prepreg is characterized by dynamic mechanical analysis method and the gelation points are analyzed by torsional braid analysis theory. The growth mechanisms of the mechanical property at different constant temperatures are investigated by the relative mechanical growth rate of storage modulus and the curing kinetics models are established. The theories of Flory and Hsich are used to calculate the activation energy during the curing reaction of prepreg. The results indicates that the gelation times which are detected by the method of torsional braid analysis at 300, 310, 330, and 350°C were 293.7, 213.8, 104.1, and 45.4 min, respectively. The activation energies of curing reaction are calculated by Flory theory and Hsich theory, they are 112.35 and 119.96 kJ/mol, respectively. The feasibly of curing system 300°C × 4 h + 330°C × 2 h + 350°C × 2 h + 370°C × 2 h is verified by the mechanical properties and the heat resistance of polyimide composite.

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.

Effective prediction of aircraft failure rate has important guiding significance for formulating reasonable maintenance plans, carrying out reliable maintenance activities, improving health management levels, and ensuring the safety of aircraft flight, etc. Firstly, combining the advantages of time series model in eliminating random accidental factors interference, grey model in dealing with poor information, and the characteristics of artificial neural network in dealing with nonlinear data, the failure rate of aircraft equipment is predicted by ARIMA model, grey Verhulst model, and BP neural network model, and secondly, based on the idea of variable weight, the method of sum of squares of errors is used to reciprocate. Shapley value method and IOWA operator method determine the weighting coefficient and establish three combined forecasting models for aircraft failure rate prediction, so as to improve the accuracy of the algorithm. Finally, taking the data of actual aircraft failure rate as the research object, the performance indexes of design prediction model are judged by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Index of Agreement (IA), Theil Inequality Coefficient (TIC), Equal Coefficient (EC), Nash-Sutcliffe Efficiency coefficient (NSE), Pearson test, and violin diagram of forecast error distribution. The experimental results show that: The forecasting precision of the combination model is better than that of the single model, and the evaluation index of combination forecasting model based on IOWA operator is better than that of other combination forecasting models, thus improving the forecasting accuracy and reliability. Compared with other typical prediction models simultaneously, it is verified that the proposed combined prediction model has strong applicability, high accuracy, and good stability, which provides a practical and effective technical method for aircraft fault prediction and has good application value.

Background: Post-traumatic tibial osteomyelitis is considered as complex clinical problem due to its unique characteristics such as prolonged course, multi-staged treatment and high recurrence rate. The purpose of this study is to identify and analyze the causes and risk factors associated with infection recurrence of tibial osteomyelitis treated with Ilizarov technique. Methods: From January 2011 to January 2019, a total of 149 patients with post-traumatic tibial osteomyelitis treated with Ilizarov bone transport technique were included in this study. Demographic and clinical data were collected and analyzed. Univariate analysis and logistic regression analysis were used to analyze the factors that may affect the recurrence or reinfection of post-traumatic tibial osteomyelitis after treated with Ilizarov bone transport technique.Results: All included patients were successfully followed up with an average of 37.5 month (18-78 month), among them, 17 patients (11.41%) occurred with recurrence or reinfection of tibial osteomyelitis in which 2 cases were in distraction area and 15 cases in docking site. Among them, 5 patients were treated successfully with sensitive intravenous antibiotic, the remaining 12 patients were intervened by surgical debridement or bone grafting after debridement. Univariate analysis showed that pseudomonas aeruginosa infection, bone exposure, number of previous operations (>3 times), blood transfusion during bone transport surgery, course of osteomyelitis >3 months, diabetes was associated with recurrence or reinfection of postoperative tibial osteomyelitis. According to the results of logistic regression analysis, pseudomonas aeruginosa infection, bone exposure, and the number of previous operations (>3 times) are risk factors for recurrence or reinfection of posttraumatic tibial osteomyelitis treated with Ilizarove bone transport technique, with odds ratios (OR) of 6.055, 7.413, and 1.753, respectively. Conclusion: The number of previous operations (>3 times), bone exposure, and pseudomonas aeruginosa infection are risk factors for infection recurrence of posttraumatic tibial osteomyelitis treated with Ilizarove bone transport technique.