应变梁多源载荷校准模型的误差估计方法

金婉莹, 江文松, 罗哉, 沈雯阳, 李占海

计量学报 ›› 2026, Vol. 47 ›› Issue (1) : 8-15.

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计量学报 ›› 2026, Vol. 47 ›› Issue (1) : 8-15. DOI: 10.3969/j.issn.1000-1158.2026.01.02
力学计量

应变梁多源载荷校准模型的误差估计方法

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Error Estimation Method for Multi-source Load Calibration Model of Strain Beams

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摘要

应变梁是应变传感器高精度校准的重要工具。但受到激励衰减和响应测不准的双重影响,多源载荷作用下的应变梁校准模型极易出现偏差。为此,提出了多源载荷校准误差估计方法。该方法根据应变梁悬臂的受力特性,分析了多源激励与测量切面结构响应之间的载荷传递关系,建立了测量切面应力模型;在最小二乘法的基础上,通过线性经验验证和抵消策略,对输入-输出多变量误差进行近似线性拟合,建立了多变量误差正规方程;采用矩阵求逆运算方法实现正规方程的回归系数辨识。为了验证该方法的有效性,搭建了应变梁校准系统,完成了多源载荷加载实验。实验结果表明,该方法的准确度均优于0.99;相对估计误差优于1.3%。

Abstract

The strain beam is an important tool for high-precision calibration of strain sensors. However, due to the dual influence of excitation attenuation and inaccurate response measurement, the load calibration model of strain beam under multi-source load is easy to deviation. For this reason, a multi-source load calibration error estimation method is proposed to error estimation. According to the mechanical characteristics of the cantilever of the strain beam, the load transfer relationship between the multi-source excitation and the measured section of the structural response is analyzed, and the stress model of the measured section is established. In order to verify the effectiveness of this method, a strain beam calibration system is built, and a multi-source load loading experiment is completed. Experimental results show that the accuracy of the proposed method is better than 0.99, and the relative estimation error is better than 1.3%.

关键词

力学计量 / 应变梁 / 载荷校准 / 参数辨识 / 最小二乘法 / 多源加载

Key words

mechanical metrology / strain beams / load calibration / parameter identification / least squares / multi-source loading

引用本文

导出引用
金婉莹, 江文松, 罗哉, . 应变梁多源载荷校准模型的误差估计方法[J]. 计量学报. 2026, 47(1): 8-15 https://doi.org/10.3969/j.issn.1000-1158.2026.01.02
JIN Wanying, JIANG Wensong, LUO Zai, et al. Error Estimation Method for Multi-source Load Calibration Model of Strain Beams[J]. Acta Metrologica Sinica. 2026, 47(1): 8-15 https://doi.org/10.3969/j.issn.1000-1158.2026.01.02
中图分类号: TB931   

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基金

国家重点研发计划(2022YFF0705704)
国家市场监督管理总局技术保障专项项目(2023YJ10)

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