An indirect method for measuring frequency domain response of satellite internal structure based on convolutional neural network
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摘要: 针对生产线卫星脉动式快速生产的要求,为解决力学试验步骤烦琐且内部结构响应测量困难的问题,提出基于卷积神经网络(CNN)的卫星内部结构响应深度学习间接测量方法。分别对生产线I型卫星与成熟的II型卫星进行正弦扫频试验,提取结构加速度频域响应信息,并利用基于CNN建立内部结构响应的间接测量模型验证所提出方法的可行性。结果表明该方法对两种型号卫星内部结构响应的间接测量总体精度分别达到了95.8%与96.9%,具有较强的工程应用潜力。Abstract: In view of the requirement of pulsating rapid production for production line satellite, in order to solve the problems of tedious mechanical test steps and difficulty to measure the internal structural response, a deep-learning -based indirect measurement method based on convolutional neural network (CNN) was proposed. A sine sweep test was conducted on a production line satellite I and a mature satellite II, respectively, to extract the frequency domain response of the structural acceleration. Then an indirect model based on CNN for measuring the internal response was established to verify the feasibility of the proposed method. The results show that the indirect measurement accuracy with the method reaches 95.8% and 96.9% for the two types of satellites, respectively, which indicates that the proposed method may have a strong potential for engineering applications.
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