基于神经网络的扭矩传感器稳定性分析预测

Research on Prediction of Stability of Torque Sensor Based on Neural Network

在扭矩检定中,扭矩值随时间的变化波动并非是已知的常见函数,探究其变化波动趋势对于扭矩检定及量值传递有一定价值和意义。为了探究扭矩传感器示值波动特性,针对扭矩测量中长期稳定性对扭矩值变化的影响进行研究,采用神经网络对给定条件下的检测数据进行训练,对其他时间节点的扭矩值进行预测。以严格控制实验条件下得到的近12年的扭矩值数据进行训练和分析,采用交叉验证的方式证明了模型的准确性和可靠性,并对下一检定周期的扭矩值进行预测,最终计算得到下一周期扭矩值满足0.03级的长期稳定性的概率大于71.8%。

In torque verification,the fluctuation of torque value over time is not a known common function,and it is vital to explore the trend of its change and fluctuation for torque verification and dissemination of value of quantity.To explore the fluctuation characteristic of the torque sensor,the influence of medium-and long-term stability on the change of torque value in torque measurement is investigated,and a neural network is used to train the detection data under given conditions to predict the torque value at other time points.The torque value data obtained under strictly controlled experimental conditions for the last 12 years were used for training and analysis,the accuracy and reliability of the model were demonstrated by cross-validation,the torque value of the next calibration cycle was predicted,and the probability that the torque value of the next cycle meets the long-term stability of 0.03 level was finally calculated to be greater than 71.8%.