Xia Kun, Lou Yang, Yuan Qingqing, Zhu Benjing, Li Ruikai, Du Yao
Department of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Sensors (Basel). 2024 Feb 29;24(5):1576. doi: 10.3390/s24051576.
The torque is a significant indicator reflecting the comprehensive operational characteristics of a power system. Thus, accurate torque measurement plays a pivotal role in ensuring the safety and stability of the system. However, conventional torque measurement systems predominantly rely on strain gauges adhered to the shaft, often leading to reduced accuracy, poor repeatability, and non-traceability due to the influence of strain gauge adhesion. To tackle the challenge, this paper introduces a photoelectric torque measurement system. Quadrants of photoelectric sensors are employed to capture minute deformations induced by torque on the rotational axis, converting them into measurable voltage. Subsequently, the system employs the radial basis function neural network optimized by simulated annealing combined with particle swarm algorithm (SAPSO-RBF) to establish a correlation between measured torque values and standard references, thereby calibrating the measured values. Experimental results affirm the system's capability to accurately determine torque measurements and execute calibration, minimizing measurement errors to 0.92%.
扭矩是反映电力系统综合运行特性的重要指标。因此，精确的扭矩测量对于确保系统的安全与稳定起着关键作用。然而，传统的扭矩测量系统主要依赖粘贴在轴上的应变片，由于应变片粘贴的影响，常常导致精度降低、重复性差以及不可追溯性。为应对这一挑战，本文介绍了一种光电扭矩测量系统。采用光电传感器象限来捕捉扭矩在旋转轴上引起的微小变形，并将其转换为可测量的电压。随后，该系统采用结合模拟退火与粒子群算法优化的径向基函数神经网络（SAPSO-RBF），在测量扭矩值与标准参考之间建立关联，从而校准测量值。实验结果证实了该系统准确确定扭矩测量值并进行校准的能力，将测量误差最小化至0.92%。
