一个多头注意网络,用于快速预测超声波导向波散射在温度和应力合下
Xiao Ying1, Zhao Wang1, Jian Li1
1State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
超声波导向波传感器在极端环境中面临着挑战. 一个新的分散多头注意网络 (D-MHAN) 模型准确地预测传感器响应,使实时结构健康监测成为可能.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 超声波导向波传感器对于结构健康监测 (SHM) 是至关重要的.
- 极端温度和应力导致分散特征的非线性漂移,损害传感器的可靠性.
- 传统方法的计算局限性阻碍了实时传感器校准.
研究的目的:
- 开发一个计算效率高的实时超声波传感器校准模型.
- 为了克服极端环境中信号解释可靠性的挑战.
- 为了使材料性质能够进行可靠的分布外推断.
主要方法:
- 提出了一个分散多头注意力网络 (D-MHAN) 模型.
- 直接将11个原始物理参数映射到全频段分散响应.
- 利用非规范化的输入策略来内化物理定律.
主要成果:
- 与高保真数据集相比,获得了0.9999的皮尔森相关系数.
- 演示的计算速度比半分析有限元法 (SAFE) 快30倍,比波浪有限元法 (WFEM) 快168倍.
- 通过在航空航天储上的冷实验验验证了实用的实用性.
结论:
- D-MHAN模型为实时超声波传感器校准提供了强大的解决方案.
- 建立了实时参数灵敏度分析和环境影响补偿的基础.
- 在极端操作条件下提高超声波传感的可靠性.
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