机器学习用于航空航天结构的结构健康监测:一篇评论
Gennaro Scarselli1, Francesco Nicassio2
1Department of Aeronautics and Astronautics, University of Southampton, Building 176, Boldrewood Innovation Campus, Burgess Road, Southampton SO16 7QF, UK.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
机器学习 (ML) 增强了航空航天结构健康监测 (SHM) 以更好地检测和预测损坏. 这篇评论绘制了ML的进步,挑战和未来更安全的飞机和航天器的方向.
科学领域:
- 航空航天工程 航空航天工程
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 结构健康监测 (SHM) 对航空航天安全和性能至关重要.
- 随着系统复杂性的增加,需要先进的SHM技术.
- 机器学习 (ML) 集成正在改变航空航天领域的损害检测,定位和预测.
研究的目的:
- 为航空航天应用提供基于ML的SHM近期进展的全面审查.
- 突出航空航天SHM中各种ML技术的功能和挑战.
- 概述未来在航空航天领域研究和部署ML的路线图.
主要方法:
- 审查监督,无监督,深度和混合学习技术.
- 分析ML用于处理高维传感器数据和管理不确定性.
- 探索新兴的ML趋势,如数字双胞胎,转移学习和联合学习.
主要成果:
- 机器学习方法显示了实时诊断和损害预测的巨大潜力.
- 关键的挑战包括数据稀缺性,操作变化性和安全关键系统的可解释性.
- 确定了基于ML的SHM的当前优势和局限性.
结论:
- 基于ML的SHM为航空航天安全提供了一种革命性的方法.
- 应对挑战对于将ML从研究转向作战部署至关重要.
- 未来的研究应该专注于数字双胞胎,转移学习和联合学习,以实现强大的SHM.
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