Robust Fault-Tolerant Flush Air Data Sensing Algorithm via Incorporating Physical Knowledge
文献类型：期刊论文
| 作者 | Liu Y(刘洋)1 ; Yang WC(杨文超)1; Liu W(刘文)1; Yan, Xunshi2; Liu ZT(刘子提)1; Zhang CA(张陈安)1
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| 刊名 | IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
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| 出版日期 | 2025-04-01 |
| 卷号 | 61期号:2页码:4329-4342 |
| 关键词 | Layout Sensors Pressure measurement Fault tolerant systems Fault tolerance Aerodynamics Atmospheric modeling Mathematical models Data models Surface reconstruction Fault diagnosis flush air data sensing (FADS) incorporates physical knowledge neural networks robust fault tolerance |
| ISSN号 | 0018-9251 |
| DOI | 10.1109/TAES.2024.3504500 |
| 英文摘要 | The flush air data sensing (FADS) system resolves air data state issues through redundant measurements of surface pressure distributions on the vehicle, with its fault-tolerant algorithm being crucial for ensuring flight safety. However, voting-based fusion strategies for redundant measurements may lead to incorrect judgments under specific conditions, along with limitations such as high algorithmic complexity and underutilization of pressure signals. To address these challenges, this manuscript introduces a fault-tolerant FADS algorithm based on dimensionless input and output convolutional neural networks (FT-DIONNFADS). We trained the neural networks with a fault dataset designed for adaptability, enabling it to work with various pressure port layouts. For each layout, the algorithm incorporates physical knowledge to assess the discrepancy between predicted and true air data states. This approach, based on the principle of minimal error, facilitates the selection of an optimal layout that improves fault diagnosis and tolerance. This algorithm undergoes assessment employing a simplified supersonic model, demonstrating its capability for accurate fault diagnosis and air data estimation across different bias levels. The manuscript also discusses the impact of varying bias levels on FT-DIONNFADS performance. |
| 分类号 | 一类 |
| WOS研究方向 | Engineering, Aerospace ; Engineering, Electrical & Electronic ; Telecommunications ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001464949400001 |
| 资助机构 | This work was supported in part by the Strategic Priority Research Program (B) of the Chinese Academy of Sciences (CAS) under Grant XDB0620402, in part by the CAS Project for Young Scientists in Basic Research under Grant YSBR-107, and in part by the Youth Innovation Promotion Association CAS under Grant 2023023. |
| 其他责任者 | 杨文超 |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/101435]  |
| 专题 | 宽域飞行工程科学与应用中心 力学研究所_高温气体动力学国家重点实验室 |
| 作者单位 | 1.Inst Mech; 2.Tsinghua University |
| 推荐引用方式 GB/T 7714 | Liu Y,Yang WC,Liu W,et al. Robust Fault-Tolerant Flush Air Data Sensing Algorithm via Incorporating Physical Knowledge[J]. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS,2025,61(2):4329-4342. |
| APA | 刘洋,杨文超,刘文,Yan, Xunshi,刘子提,&张陈安.(2025).Robust Fault-Tolerant Flush Air Data Sensing Algorithm via Incorporating Physical Knowledge.IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS,61(2),4329-4342. |
| MLA | 刘洋,et al."Robust Fault-Tolerant Flush Air Data Sensing Algorithm via Incorporating Physical Knowledge".IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 61.2(2025):4329-4342. |
入库方式： OAI收割
来源：力学研究所
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