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A deep learning approach for predicting the antenna pointing error caused by transmission faults with simulation data

文献类型：期刊论文

作者Chen, Lihui2,4; Xue, Song2,3,4; Lian, Peiyuan2,3,4; Xu, Qian1; Wang, Meng5; Wang, Congsi3
刊名SCIENTIFIC REPORTS
出版日期2024-12-30
卷号14期号:1页码:23
关键词Large antennas Transmission faults Pointing accuracy Axis error Intelligent prediction
ISSN号2045-2322
DOI10.1038/s41598-024-83103-1
产权排序4
英文摘要Reflector antenna has been widely used in deep space exploration, radar warning, and other fields, all of which requires high pointing accuracy. The antenna elevation bearings are the key component that guarantees its pointing accuracy, while any degradation or fault can seriously affect the antenna's performance, leading to deviations in antenna pointing and instability during operation. However, the relationship between the antenna elevation bearing fault and its pointing accuracy remains unclear because there is insufficient experimental faulty transmission data and pointing error collected from the test-rig simultaneously. Therefore, this paper aims to establish a deep learning model-based relationship to reveal the underlying relationship between the antenna transmission faults and its pointing accuracy. By linking the two, transmission faults in key components can serve as a substitute for pointing accuracy as one of the criteria for antenna maintenance decisions, vibration signals, serving as a basis for fault diagnosis, can be collected and processed in real-time without the need for equipment shutdowns, undoubtedly bringing convenience to antenna maintenance providing a theoretical basis for the development of antenna maintenance strategies. In order to overcome the problem of insufficient data, this paper has established an antenna elevation system dynamic simulation model containing pre-defined transmission faults. Furthermore, to link antenna fault diagnosis with antenna pointing errors, a mathematical model for antenna axis error analysis has been established. Finally, labeled fault data and antenna pointing errors have been put into the deep neural network model for training to obtain the prediction model for predicting antenna axis error. The results showed that faults in the key transmission components have a significant impact on antenna pointing errors and the proposed deep neural network learning model exhibits a high predictive accuracy.
WOS关键词ROLLING-ELEMENT BEARINGS ; DYNAMICS ; WEAR
资助项目National Key Research and Development Program of China[2021YFC2203600] ; National Natural Science Foundation of China[52475278] ; National Natural Science Foundation of China[52275269] ; Fundamental Research Funds for the Central Universities[ZYTS24030] ; Fundamental Research Funds for the Central Universities[ZYTS24024] ; Project about Building up Scientists + Engineers of Shaanxi Qinchuangyuan Platform[2022KXJ-030]
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:001386372800041
出版者NATURE PORTFOLIO
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Project about Building up Scientists + Engineers of Shaanxi Qinchuangyuan Platform
源URL[http://ir.xao.ac.cn/handle/45760611-7/7359]  
专题射电天文研究室_天线技术实验室
新疆天文台_110米口径全可动射电望远镜（SmART）_技术成果
通讯作者Xue, Song; Wang, Congsi
作者单位1.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi, Peoples R China
2.Xidian Univ, State Key Lab Electromech Integrated Mfg High Perf, Xian, Peoples R China
3.Xidian Univ, Guangzhou Inst Technol, Guangzhou 510555, Peoples R China
4.Xidian Univ, Sch Mechanoelect Engn, Xian, Shaanxi, Peoples R China
5.Shaanxi Huanghe Grp Co Ltd, Res Inst, Xian 710043, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Lihui,Xue, Song,Lian, Peiyuan,et al. A deep learning approach for predicting the antenna pointing error caused by transmission faults with simulation data[J]. SCIENTIFIC REPORTS,2024,14(1):23.
APA Chen, Lihui,Xue, Song,Lian, Peiyuan,Xu, Qian,Wang, Meng,&Wang, Congsi.(2024).A deep learning approach for predicting the antenna pointing error caused by transmission faults with simulation data.SCIENTIFIC REPORTS,14(1),23.
MLA Chen, Lihui,et al."A deep learning approach for predicting the antenna pointing error caused by transmission faults with simulation data".SCIENTIFIC REPORTS 14.1(2024):23.

入库方式： OAI收割

来源：新疆天文台

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