Considering the fault characteristics of UAV sensors of multiple and complex fault types, we modeled and predicted the time delay by using grey model and Elman neural network, and used the principle of minimum variance to determine the best combination of model weighting coefficients.The method was applied to the fault diagnosis of UAV sensors.The simulation result showed that the combined prediction model has a high precision in predicting the fault diagnosis delay, and proved the validity of the proposed diagnostic method.