三种算法的比较和验证,以提高石英共振压力传感器的精度
Bin Yao1, Yanbo Xu2, Junming Jing2
1Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China.
Micromachines
|January 23, 2024
概括
这项研究开发了机器学习模型,以弥补AT切割石英共振压力传感器中的温度影响. 多变量多项式回归实现了高精度,将误差降低到不到0.008%的FS.
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
- 机器学习 机器学习
背景情况:
- AT切割石英是一种精确的压力传感器,但受到温度依赖的影响.
- 准确的压力测量在许多科学和工业领域都至关重要.
- 温度变化限制了石英压力传感器在不同环境中的应用.
研究的目的:
- 为AT切割石英共振压力传感器开发有效的温度补偿模型.
- 调查不同机器学习算法在传感器数据补偿方面的有效性.
- 为了提高在可变温度条件下压力测量的准确性和可靠性.
主要方法:
- 使用了AT切割和AC切割的石英晶体,分别用于压力和温度传感.
- 采用两种石英类型的输出频率作为机器学习模型的输入特征.
- 应用并比较了三个学习算法:多变量多项式回归 (MPR),多层感知子网络和支持向量回归.
主要成果:
- 所有三个研究的学习算法都在补偿温度效应方面表现出有效性.
- 多变量多项式回归 (MPR) 模型在压力预测方面表现出卓越的准确性.
- 在 -10-40°C温度范围内实现了小于全尺度 (FS) 0.008%的剩余误差.
- 开发了一种智能系统,用于自动补偿和实时处理共振压力传感器数据.
结论:
- 机器学习,特别是MPR,为共振压力传感器中的温度补偿提供了一个有前途的解决方案.
- 开发的补偿策略显著提高了传感器的准确性,并扩大了操作能力.
- 智能系统有助于在线校准和高精度压力传感器的工业规模应用.
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