College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
China Petroleum & Chemical Corporation, Beijing 100728, China.
Sensors (Basel). 2023 Jul 5;23(13):6167. doi: 10.3390/s23136167.
Piezoresistive pressure sensors exhibit inherent nonlinearity and sensitivity to ambient temperature, requiring multidimensional compensation to achieve accurate measurements. However, recent studies on software compensation mainly focused on developing advanced and intricate algorithms while neglecting the importance of calibration data and the limitation of computing resources. This paper aims to present a novel compensation method which generates more data by learning the calibration process of pressure sensors and uses a larger dataset instead of more complex models to improve the compensation effect. This method is performed by the proposed aquila optimizer optimized mixed polynomial kernel extreme learning machine (AO-MPKELM) algorithm. We conducted a detailed calibration experiment to assess the quality of the generated data and evaluate the performance of the proposed method through ablation analysis. The results demonstrate a high level of consistency between the generated and real data, with a maximum voltage deviation of only 0.71 millivolts. When using a bilinear interpolation algorithm for compensation, extra generated data can help reduce measurement errors by 78.95%, ultimately achieving 0.03% full-scale (FS) accuracy. These findings prove the proposed method is valid for high-accuracy measurements and has superior engineering applicability.
压阻式压力传感器表现出固有非线性和对环境温度的敏感性，需要多维补偿才能实现准确测量。然而，最近关于软件补偿的研究主要集中在开发先进而复杂的算法上，而忽略了校准数据的重要性和计算资源的局限性。本文旨在提出一种新的补偿方法，该方法通过学习压力传感器的校准过程来生成更多的数据，并使用更大的数据集而不是更复杂的模型来提高补偿效果。该方法由提出的雕鸮优化混合多项式核极限学习机（AO-MPKELM）算法执行。我们进行了详细的校准实验，以评估生成数据的质量，并通过消融分析评估所提出方法的性能。结果表明，生成数据与真实数据之间具有高度一致性，最大电压偏差仅为 0.71 毫伏。当使用双线性插值算法进行补偿时，额外生成的数据可以帮助将测量误差降低 78.95%，最终实现 0.03%满量程（FS）的精度。这些发现证明了所提出的方法对于高精度测量是有效的，并且具有优越的工程适用性。
