Examination on avionics system fault prediction technology based on ashy neural network and fuzzy recognition

Abstract

The purpose of this paper is to accurately locate the fault prediction and diagnosis technology, to have a high degree of automation, and to handle it quickly, for the large aircraft avionics system failure presents the feature of multiple coupling, complex impact and rapid spread. At the same time, the fault prediction diagnosis technology is one of the most important contents of the avionics system equipment prediction, so how to quickly and effectively predict the failure of key system parts of avionics is the core essential to ensure the complete operation of the whole system. This paper through establishing the gray neural network model, combining the advantages of gray model to deal with poor information and the characteristics of artificial neural network processing nonlinear data, to realize the fault prediction of avionics system, At the same time, At the same time, through the fuzzy recognition method based on the deterioration degree, established the bridge between the two, in turn, to achieve the health prediction management of system. The method mainly includes: Firstly, by combining gray theory and artificial neural network algorithm with fuzzy recognition to establish a network model that contains gray neural network models and can reflect the excellent characteristics of fuzzy recognition and conduct experimental analysis; Second, on this basis, improve the weight update strategy of the gray neural network by using additional learning rate method which based on momentum and improve the accuracy of the algorithm. Therefore, it can be concluded that the predictions presented in this paper should not be directly imitated when the system disturbance factor is too large or the system is abnormally caused by a serious disturbance suddenly appearing at a certain point in time, but should properly processed the data firstly according to the actual situation. According to the time series of the actual situation, several models are established, and the data correction is explained from the model prediction effect, and the gray model and description are improved. The improved combination of gray neural network and gray neural network can not only improve the prediction accuracy, but also provide a feasible method for such time series prediction, which provides a practical and effective technical method for avionics system fault prediction.