Abstract
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.
Highlights
Complex equipment plays an important role in military industry and civilian production
As a typical complex equipment, airplanes play an important role in the development of military and civil fields, and their airborne equipment plays a key role as an important part of the aircraft system
Fault prediction has more research value than post-fault diagnosis. It provides good repair and maintenance decisions and improves the health management level of airborne equipment. e failure prediction of airborne equipment is generally divided into three aspects: failure time prediction, remaining life prediction, and failure rate prediction. e failure rate is the main basis for the allocation of failure samples in airborne equipment maintenance and testing
Summary
Complex equipment plays an important role in military industry and civilian production. In order to overcome the limitations of the models in previous studies, the combined model uses five types of statistical and data-driven models, including multiple linear regression (MLR) model, grayGM (1,N) model, partial least squares (PLS) model, artificial neural network (BP) model, and support vector machine (SVM) model to predict the failure rate of the airborne equipment of a certain UAV flight control system. A single predicted failure rate value of airborne equipment is regarded as an independent input variable, and the predicted failure rate value is used as a dependent output variable of the combined model for research It verifies the validity and applicability of the proposed method in the failure prediction of airborne equipment, and provides an effective basis for its fault diagnosis and health maintenance.
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