摘要
Abstract
For the problems of insufficient generalization ability of prognostics and health management(PHM)models in the integrated radio frequency system(IRFS)field,high data acquisition costs,and poor data balance,a design method for an air-ground cooperative system for PHM is proposed.This system achieves data sharing and model synchronization through the airborne IRFS in the air segment,the digital twin system and artificial intelligence(AI)control center on the ground segment,thereby enhancing the generalization ability of PHM models,reducing the cost of data acquisition,and improving data balance.Furthermore,the dataset of PHM is augmented by collecting various sensor characteristics from common units of different modules,and extreme data is generated using the K-means clustering algorithm and the generative adversarial network(GAN)to improve data balance.Finally,the reliability of the airborne IRFS is evaluated based on a convolutional neural network(CNN),resulting in a high fit between predicted and actual values,with mean squared error(MSE)of 0.000 2,mean absolute error of 0.008 9,and R2 Score of 0.945 2.This study provides new ideas and methods for the development of PHM technology in IRFS.关键词
机线综合射频系统/故障预测与健康管理(PHM)/卷积神经网络(CNN)/反向传播神经网络(BPNN)/生成对抗网络(GAN)Key words
airborne integrated RF system/prognostics and health management(PHM)/convolutional neural network(CNN)/back propagation neural network(BPNN)/generative adversarial network(GAN)分类
计算机与自动化
