Feng Yang, Zhang Bingkun, Chen Yang, Wang Ben, Xia Hua, Yu Haoda, Yu Xulehan, Yang Pengfei
School of Information Science and Engineering (SISE), Hangzhou Normal University, Hangzhou 311121, China.
Mobile Health Management System Engineering Research Center of the Ministry of Education, Hangzhou 311121, China.
Micromachines (Basel). 2025 Sep 11;16(9):1044. doi: 10.3390/mi16091044.
This paper presents a design method for a continuous tension detection sensor based on a cantilever beam structure and compensates for the temperature drift of a SAW sensor based on a neural network algorithm. Firstly, a novel cantilever beam roller structure is proposed to significantly enhance the sensitivity of the transmission of silk thread tension to a SAW tension sensor. Secondly, to improve the sensitivity of the SAW tension sensor, the COMSOL finite element method (FEM) is employed for simulation to determine the optimal IDT placement. An unbalanced split IDT design is utilized to suppress potential parasitic responses. Finally, the designed sensor is tested, and a GA-PSO-BP algorithm is employed to fit the test data for temperature compensation. The experimental results demonstrate that the temperature sensitivity coefficient of the data optimized by the GA-PSO-BP algorithm is reduced by an order of magnitude compared to the raw data, with reductions of 6.0409×10-3 °C-1 and 3.0312×10-3 °C-1 compared to the BP neural network and the PSO-BP algorithm, respectively. The average output error of the optimized data is reduced by 5.748% compared to the sensor measurement data, and it is also lower than both the BP neural network and the PSO-BP algorithm. It provides new design ideas for the development of tension sensors.
本文提出了一种基于悬臂梁结构的连续张力检测传感器的设计方法，并基于神经网络算法对声表面波（SAW）传感器的温度漂移进行补偿。首先，提出了一种新颖的悬臂梁滚轮结构，以显著提高丝线张力传递到SAW张力传感器的灵敏度。其次，为了提高SAW张力传感器的灵敏度，采用COMSOL有限元方法（FEM）进行仿真，以确定最佳的叉指换能器（IDT）布局。采用不平衡分裂IDT设计来抑制潜在的寄生响应。最后，对所设计的传感器进行测试，并采用遗传算法-粒子群优化算法-反向传播（GA-PSO-BP）算法对测试数据进行拟合以进行温度补偿。实验结果表明，与原始数据相比，经GA-PSO-BP算法优化后的数据的温度灵敏度系数降低了一个数量级，与BP神经网络和PSO-BP算法相比，分别降低了6.0409×10-3℃-1和3.0312×10-3℃-1。优化后数据的平均输出误差比传感器测量数据降低了5.748%，并且也低于BP神经网络和PSO-BP算法。它为张力传感器的开发提供了新的设计思路。
