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| A deep learning approach for predicting the antenna pointing error caused by transmission faults with simulation data | |
Chen, Lihui1,3; Xue, Song1,2,3; Lian, Peiyuan1,2,3; Xu, Qian4 ; Wang, Meng5; Wang, Congsi2
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| 2024-12-30 | |
| Source Publication | SCIENTIFIC REPORTS
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| ISSN | 2045-2322 |
| Volume | 14Issue:1Pages:23 |
| Contribution Rank | 4 |
| Abstract | 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. |
| Keyword | Large antennas Transmission faults Pointing accuracy Axis error Intelligent prediction |
| DOI | 10.1038/s41598-024-83103-1 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Keyword | ROLLING-ELEMENT BEARINGS ; DYNAMICS ; WEAR |
| Funding Project | 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 Research Area | Science & Technology - Other Topics |
| WOS Subject | Multidisciplinary Sciences |
| WOS ID | WOS:001386372800041 |
| Publisher | NATURE PORTFOLIO |
| Funding Organization | 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 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.xao.ac.cn/handle/45760611-7/7360 |
| Collection | 射电天文研究室_天线技术实验室 110米口径全可动射电望远镜（QTT）_技术成果 |
| Corresponding Author | Xue, Song; Wang, Congsi |
| Affiliation | 1.Xidian Univ, State Key Lab Electromech Integrated Mfg High Perf, Xian, Peoples R China 2.Xidian Univ, Guangzhou Inst Technol, Guangzhou 510555, Peoples R China 3.Xidian Univ, Sch Mechanoelect Engn, Xian, Shaanxi, Peoples R China 4.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi, Peoples R China 5.Shaanxi Huanghe Grp Co Ltd, Res Inst, Xian 710043, Peoples R China |
| Recommended Citation 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Chen-2024-A deep lea（13256KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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