LambNet-T:一种轻量级的路径条件变压器自编码器,用于在Lamb-wave SHM中进行温度感知基线学习.
Jan Horňas1, Ondřej Vích1, Lenka Šedková1
1VZLU AEROSPACE, Beranových 130, 19900 Prague, Czech Republic.
Ultrasonics
|February 3, 2026
概括
本研究介绍了LambNet-T,LambNet-T是一种基于变压器的自编码器,用于Lamb-wave结构健康监测 (SHM) 中的温度感知基线选择. 它提高了多个传感器路径的诊断准确性和稳定性,即使在有限的基线温度下也是如此.
科学领域:
- 结构健康监测 (SHM) 是一种结构健康监测.
- 声波传播的声波传播
- 机器学习用于工程.
背景情况:
- 使用Lamb波的可靠结构健康监测 (SHM) 受到温度变化影响基线数据的挑战.
- 准确的基线选择对于有效的损害检测和定位在SHM系统中至关重要.
- 现有的方法经常在温度补偿和多路径数据集成方面扎.
研究的目的:
- 开发一种温度感知基线学习方法,用于多路径的Lamb-wave SHM.
- 介绍LambNet-T,一个轻量级的基于变压器的自动编码器,用于高效和强大的基线选择.
- 为了提高SHM系统的诊断精度和温度稳定性.
主要方法:
- 开发了LambNet-T,这是一个基于路径条件的基于变压器的自动编码器,使用注意力聚合 (AP) 进行上下文嵌入.
- 实施了基于中位数的评价策略的共弦相似性 (CS),以进行可靠的基线选择.
- 采用二次对比来增强数据,并使用有限的基线温度 (-10至+50°C),以反映实际约束.
主要成果:
- 与卷积自编码器 (CAE-GAP) 相比,LambNet-T表现出更高的训练效率.
- 最高路径特定CS值的中位数有效地确定了最佳温度补偿基线.
- 实现了高精度 (R2 = 0.994 ± 0.001),表现优于CAE-GAP和传统的最佳基线选择 (OBS).
- 整合将撞击位置误差降低到4.12毫米,并纳入了用于不确定性管理的统计过器.
结论:
- LambNet-T为多路径的Lamb-wave SHM基线选择提供了一个高度准确和耐温度的解决方案.
- 拟议的方法在实际的SHM应用中显著提高了诊断性能和效率.
- 开放式可用的数据集确保了可重现性,并促进了对温度感知SHM的进一步研究.
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