Medium and Long-Term Fault Prediction of Avionics Based on Echo State Network

Abstract

As one of the core equipment of aircraft, avionics provide a power source for flight. Avionics are complex and highly susceptible to environmental factors. Failure in the long flight process is also relatively large, affecting the stability and safety of aircraft operation. Therefore, it is of great significance to predict the typical faults of avionics. At present, a lot of research achievements have been made on the fault diagnosis of avionics, but the failure prediction of avionics is rarely involved, especially the middle and long-term fault prediction. Hence, this paper proposes a fault prediction method for avionics based on an echo state network. In particular, one-dimensional wavelet denoising filtering and z-score standardized preprocessing are carried out to obtain pure useable data first. Then, the set training data are input into the ESN model. When the model is well trained, the test data can be used to test the model. Finally, the experimental results demonstrate that the proposed ESN model can effectively improve the medium and long-term prediction accuracies of the faults in avionics equipment. Besides, the proposed model can not only identify the types of faults but also predict the specific time when the faults occur. It guarantees the safe and stable operation of the equipment and supports the stable development of the air transport industry, which has great theoretical and practical application value.