基于PSO-LSSVM算法的SAW绕线张力传感器的温度补偿
Yang Feng1, Wenbo Liu1, Haoda Yu1
1School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China.
Micromachines
|November 25, 2023
概括
这项研究引入了一种改进的表面声波 (SAW) 绕线张力传感器. 数据融合和PSO-LSSVM算法通过补偿温度错误显著提高了测量准确性.
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
- 数据科学数据科学数据科学
背景情况:
- 传统的绕张力传感器在测量准确性方面面临挑战,特别是由于温度变化.
- 表面声波 (SAW) 设备为高精度传感应用提供了潜力.
研究的目的:
- 设计一个高精度的 SAW 绕线张力传感器.
- 通过数据融合和先进的算法来提高SAW绕张力传感器的测量精度.
- 为了弥补SAW传感器测量中的温度引起的错误.
主要方法:
- 使用不平衡的分裂电极数字间传感器 (IDT) 和电极重叠包裹的SAW绕线张力传感器的设计.
- 应用粒子集群优化-最小平方支持向量机 (PSO-LSSVM) 算法用于温度错误补偿.
- 数据融合技术提高了测量精度.
主要成果:
- 在温度补偿后,SAW传感器的灵敏度温度系数 (αs) 降低了一个数量级.
- 与LSSVM模型 (1.42%) 相比,PSO-LSSVM模型的输出误差 (0.50%) 显著降低.
- 预测结果的整体准确性被证明为5.95%.
结论:
- 开发的SAW上张力传感器与PSO-LSSVM补偿证明了卓越的测量准确性.
- 拟议的方法为SAW卷轴张力传感器的数据分析和错误补偿提供了一种新的方法.
- 这一进步有助于在绕过程中更可靠地监测压力.
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