Gyroscope Dynamic Balance Counterweight Prediction Based on Multi-Head ResGAT Networks  

作　　者：Wuyang Fan Shisheng Zhong 

机构地区：[1]School of Mechanical Engineering,Harbin Institute of Technology,Harbin,150000,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第6期2525-2555,共31页工程与科学中的计算机建模（英文）

基　　金：supported by the NationalNatural Science Foundation of China(No.51705100);the Foundation of Research on Intelligent Design Method Based on Knowledge Space Reconstruction and Perceptual Push(No.52075120).

摘　　要：The dynamic balance assessment during the assembly of the coordinator gyroscope significantly impacts the guidance accuracy of precision-guided equipment.In dynamic balance debugging,reliance on rudimentary counterweight empirical formulas persists,resulting in suboptimal debugging accuracy and an increased repetition rate.To mitigate this challenge,we present a multi-head residual graph attention network(ResGAT)model,designed to predict dynamic balance counterweights with high precision.In this research,we employ graph neural networks for interaction feature extraction from assembly graph data.An SDAE-GPC model is designed for the assembly condition classification to derive graph data inputs for the ResGAT regression model,which is capable of predicting gyroscope counterweights under small-sample conditions.The results of our experiments demonstrate the effectiveness of the proposed approach in predicting dynamic gyroscope counterweight in its assembly process.Our approach surpasses current methods in mitigating repetition rates and enhancing the assembly efficiency of gyroscopes.

关 键 词：GYROSCOPE COUNTERWEIGHT ASSEMBLY small-sample ResGAT repetition rate 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]