在复合空间结构中通过深度学习进行多重损害检测
Federica Angeletti1, Paolo Gasbarri1, Massimo Panella2
1School of Aerospace Engineering, Sapienza University of Rome, Via Salaria 851, 00138 Rome, Italy.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究介绍了一种数据驱动的方法,使用长短期记忆 (LSTM) 网络来检测太阳能电池组中的损坏. 该方法有效地使用传感器数据识别损坏位置,增强航天器结构健康监测.
科学领域:
- 航空航天工程 航空航天工程
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 航天器依赖于像太阳能电池板这样的大型轻质复合结构,增加了对轨道碎片冲击的脆弱性.
- 由于全球动态的微妙变化,在这些广泛的结构中检测损坏是具有挑战性的.
- 先进的结构健康监测对于确保太空平台的运行安全至关重要.
研究的目的:
- 开发和评估数据驱动的方法,用于诊断复合太阳能电池组中的环境诱导损害.
- 为了比较加速度计和压电传感器在识别损坏位置方面的有效性.
- 加强大型空间结构的结构健康监测能力.
主要方法:
- 利用长短期内存 (LSTM) 网络来检测太阳能电池组中的损坏.
- 采用有限元模型来模拟关键风险区域的损坏位置.
- 从使用局部加速和压电电压的模拟态度机动生成的数据集.
- 训练有素的LSTM网络将时间序列传感器数据与特定的损坏标签联系起来.
主要成果:
- 基于LSTM的框架有效地确定了太阳能阵列中受损元件的位置.
- 加速度计和压电传感器数据都提供了准确的损坏定位.
- 该方法即使在有限的测量时间样本上也被证明是有效的.
结论:
- 数据驱动的LSTM方法为太空中大型复合结构的结构健康监测提供了一个有希望的解决方案.
- 该研究验证了使用加速度计或压电传感器来检测损坏的有效性.
- 这种方法有助于确保太空平台的运行安全,因为它可以快速识别损坏.
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