使用机器学习算法对灵活温度传感器进行温度校准
Ui-Jin Kim1, Ju-Hun Ahn2, Ji-Han Lee1
1Department of Aerospace Engineering and the Program in Aerospace Systems Convergence, Inha University, Incheon 21999, Republic of Korea.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
用于结构健康监测的灵活温度传感器由于热歇斯底里而面临可靠性问题. 在静态数据上训练的LSTM模型有效校准这些传感器,在动态条件下提高精度.
科学领域:
- 材料科学 材料科学 材料科学
- 传感器技术 传感器技术
- 结构健康监测 结构健康监测
背景情况:
- 热波动对大结构如桥梁和建筑物构成风险.
- 灵活的温度传感器对于结构健康监测 (SHM) 系统至关重要.
- 印刷传感器表现出复杂的动态歇斯底里,挑战其可靠性.
研究的目的:
- 为EHD喷墨打印灵活温度传感器开发可靠的校准方法.
- 为了应对传感器读数中动态歇斯底里的挑战.
- 在快速的热变化下提高SHM系统的准确性.
主要方法:
- 使用EHD喷墨印刷制造灵活的温度传感器的制造.
- 开发和培训一个长期短期记忆 (LSTM) 校准模型.
- 模型训练完全基于准静态温度数据 (20-70°C).
- 使用未见的动态温度数据进行验证.
主要成果:
- 与准静态数据上的多项式回归相比,LSTM模型在预测错误中实现了33.563%的改进.
- 在动态数据上测试时,LSTM模型的RMSE从12.451°C下降到4.899°C.
- 静态训练的LSTM模型在动态条件下表现出卓越的概括能力.
结论:
- 数据驱动型号,特别是LSTM,为校准灵活打印传感器提供了强大的解决方案.
- 开发的LSTM方法在现实世界SHM应用中显著提高了传感器可靠性.
- 这种方法提高了大型结构中热监测的准确性和可靠性.
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