基于综合灰关联序模型的残差门控循环神经网络位标器零部件选配

doi:10.3969/j.issn.1004-132X.2021.03.009

中国机械工程

基于综合灰关联序模型的残差门控循环神经网络位标器零部件选配

钟百鸿；王琳；钟诗胜

哈尔滨工业大学（威海）海洋工程学院，威海，264209

出版日期:2021-02-10 发布日期:2021-02-07
基金资助:
国家自然科学基金(51705100)；
中央高校基本科研业务费专项资金(HITNSRIF2019078)

Selective Assembly for Coordinator Parts by RNGRU Based on Comprehensive Grey Relational Order Model

ZHONG Baihong；WANG Lin；ZHONG Shisheng

School of Ocean Engineering,Harbin Institute of Technology at Weihai,Weihai,Shandong,264209

Online:2021-02-10 Published:2021-02-07

摘要/Abstract

摘要： 位标器漂移性能是决定精确制导武器跟踪能力与制导精度的关键因素之一，通过位标器零部件选配可改善位标器漂移性能。针对位标器零部件一次选配成功率低的问题，提出综合灰关联序（CGRO）模型并对影响位标器零部件选配的装配参数进行关联分析，得到了影响位标器零部件选配的关键装配参数；建立残差门控循环神经网络（RNGRU）模型，实现了位标器零部件的选配。以影响位标器漂移性能的陀螺转子与调漂螺钉装配为例，应用CGRO模型对陀螺转子的装配参数进行关联分析，得到了影响两者装配的关键装配参数；基于RNGRU模型实现了对调漂螺钉质量的回归预测。实验结果表明，所提方法能够实现位标器零部件的选配，其预测精度优于传统门控循环神经网络。

关键词: 位标器, 选配, 综合灰关联序, 残差门控循环神经网络

Abstract: The drift performance of coordinator, one of the key factors that determined the tracking ability and guidance accuracy of the precision-guided weapon directly, might be improved by the selective assembly for coordinator parts. In view of the low success rate of the first selective assembly for coordinator parts, a CGRO model was proposed to perform the relevancy on assembly parameters affecting the selective assembly for coordinator parts, and key assembly parameters affecting the selective assembly for coordinator parts were obtained. The RNGRU model was established to realize the selective assembly for coordinator parts. Taking the selective assembly between the gyro rotor and drift-adjusting screw which affected the drift performance of the coordinator as an example, CGRO was used to analyze the assembly parameters of the gyro rotor, and the key assembly parameters that affected the selective assembly of the two were obtained. Based on RNGRU, the regression prediction of the mass of the drift-adjusting screw was realized. The experimental results show that the proposed method may achieve the selective assembly for coordinator parts, and the prediction accuracy is superior to the traditional GRU network.

Key words: coordinator, selective assembly, comprehensive grey relational order(CGRO), residual network-gated recurrent unit(RNGRU)

中图分类号:

TP205

钟百鸿；王琳；钟诗胜. 基于综合灰关联序模型的残差门控循环神经网络位标器零部件选配[J]. 中国机械工程, DOI: 10.3969/j.issn.1004-132X.2021.03.009.

ZHONG Baihong；WANG Lin；ZHONG Shisheng. Selective Assembly for Coordinator Parts by RNGRU Based on Comprehensive Grey Relational Order Model[J]. China Mechanical Engineering, DOI: 10.3969/j.issn.1004-132X.2021.03.009.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.cmemo.org.cn/CN/10.3969/j.issn.1004-132X.2021.03.009

http://www.cmemo.org.cn/CN/Y2021/V32/I03/314

参考文献

［1］刘检华，孙清超，程晖，等. 产品装配技术的研究现状、技术内涵及发展趋势［J］. 机械工程学报，2018，54(11)：2-28.
LIU Jianhua, SUN Qingchao, CHENG Hui, et al. The State-of-the-art, Connotation and Developing Trends of the Products Assembly Technology［J］. Journal of Mechanical Engineering, 2018, 54(11):2-28.
［2］PANG Jihong, XUE Xiaobo, JIN Yini, et al. Research on Quality Evaluation and Forecast Control in Assembly Process for Complex Products［J］.Journal of Physics:Conference Series， 2017, 842(1):1-6.
［3］SHETH S, GEORGE P M. Experimental Investigation, Prediction and Optimization of Cylindricity and Perpendicularity during Drilling of WCB Material Using Grey Relational Analysis［J］. Precision Engineering, 2016, 45:33-43.
［4］刘世平，曹俊峰，孙涛，等. 基于BP神经网络的冗余机械臂逆运动学分析［J］. 中国机械工程，2019，30(24)：2974-2977.
LIU Shiping, CAO Junfeng, SUN Tao, et al. Inverse Kinematics Analysis of Redundant Manipulators Based on BP Neural Network［J］. China Mechanical Engineering,2019, 30(24):2974-2977.
［5］贾振元，马建伟，刘巍，等. 多几何要素影响下液压阀件特性的混合神经网络预测模型［J］. 机械工程学报，2010，46(2)：126-131.
JIA Zhenyuan, MA Jianwei, LIU Wei, et al. Hybrid Neural Network Prediction Model of Hydraulic Valve Characteristics under the Affection of Multiple Geometric Factors Effected［J］. Journal of Mechanical Engineering, 2010, 46(2):126-131.
［6］LI Xueyi, LI Jialin, QU Yongzhi, et al. Gear Pitting Fault Diagnosis Using Integrated CNN and GRU Network with Both Vibration and Acoustic Emission Signals［J］. Applied Sciences, 2019, 9(4):1-15.
［7］谭学瑞，邓聚龙. 灰色关联分析：多因素统计分析新方法［J］. 统计研究，1995 (3)：46-48.
TAN Xuerui, DENG Julong. Grey Relation Analysis:a New Method of Multivariate Statistical Analysis［J］. Statistical Research, 1995 (3):46-48.
［8］水乃翔，董太亨，沙震. 关于灰关联度的一些理论问题［J］. 系统工程，1992，10(6)：23-26.
SHUI Naixiang, DONG Taiheng, SHA Zhen. On Several Theoretical Problems of Grey Correlation Degree［J］. Systems Engineering, 1992,10(6)：23-26.
［9］雷昊，杨海容，朱牧，等. 应用于红外制导武器的动力随动陀螺误差分析［J］. 上海航天，2017，34(1)：86-91.
LEI Hao, YANG Hairong, ZHU Mu， et al. Errors Analysis of Dynamical Servo Gyro in IR Guided Weapons［J］. Aerospace Shanghai, 2017, 34(1):86-91.
［10］ZHAO R, WANG D, YAN R, et al. Machine Health Monitoring Using Local Feature-based Gated Recurrent Unit Networks［J］. IEEE Transactions on Industrial Electronics, 2017, 65(2):1539-1548.
［11］徐任超，阎威武，王国良，等. 基于周期性建模的时间序列预测方法及电价预测研究［J］. 自动化学报，2019，45：1-9.
XU Renchao, YAN Weiwu, WANG Guoliang, et al. Time Series Forecasting Based on Seasonality Modeling and Its Application to Electricity Price Forecasting［J］. Acta Automatica Sinica, 2019, 45:1-9.
［12］HE K, ZHANG X, REN S, et al. Identity Mappings in Deep Residual Networks［C］∥European Conference on Computer Vision. Amsterdam, 2016:630-645.
［13］HE K, ZHANG X, REN S, et al. Deep Residual Learning for Image Recognition［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas，2016:770-778.

基于综合灰关联序模型的残差门控循环神经网络位标器零部件选配

Selective Assembly for Coordinator Parts by RNGRU Based on Comprehensive Grey Relational Order Model

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics