This article describes the dynamic calibration of a three component accelerometer balance for impulse forces applied in three perpendicular directions and prediction of these forces using a modified deconvolution and adaptive neuro-fuzzy inference system (ANFIS) technique. The accelerometer balance is housed inside a hemispherical model and a tri-axial accelerometer is mounted to measure the accelerations in axial, normal and azimuthal directions. The experimental accelerations are compared with the accelerations obtained from finite element simulations performed using ANSYS Workbench 18.0. The predicted forces using modified deconvolution and ANFIS are compared with each other and with the actual input forces applied during the calibration experiment. Impulse force prediction using deconvolution from multiple components of acceleration is a complex task and needs excessive calculations and effort. In this study, a modified deconvolution methodology has been devised for force prediction using the resultant of the accelerations to obtain the impulse response function. This technique reduces the complexity of force prediction using multiple components of acceleration. The forces can be predicted without making complex calculations for considering the coupling effects of the accelerations in different directions. It has been observed that the modified deconvolution technique performed using the resultant of the accelerations in three directions is able to predict the forces with an average accuracy of 94.50%. However, the accuracy of forces predicted using ANFIS is slightly higher, having an average accuracy of 96.30%. Thus, a comparison of two force prediction techniques which are used in dynamic calibration has been studied in this paper. It has been observed that both modified deconvolution and ANFIS can be used for force prediction without intensive calculations, however the accuracy of ANFIS (having a maximum accuracy of 97.20%) is slightly higher than that of modified deconvolution (having a maximum accuracy of 96.80%). This confirms the ability of modified deconvolution to predict the forces in line with the other standard techniques such as ANFIS with less complexity and can be useful for accurate force prediction.

本文介绍了在三个垂直方向上施加的脉冲力的三分量加速度计天平的动态校准，以及使用改进的反卷积和自适应神经模糊推理系统（ANFIS）技术预测这些力的方法。加速度计天平安装在半球形模型内，并安装了三轴加速度计以测量轴向，法向和方位方向上的加速度。将实验加速度与使用ANSYS Workbench 18.0进行的有限元模拟获得的加速度进行比较。将使用改进的反卷积和ANFIS的预测力彼此进行比较，并与在校准实验期间施加的实际输入力进行比较。使用来自多个加速度分量的反卷积来进行脉冲力预测是一项复杂的任务，需要大量的计算和精力。在这项研究中，已经设计了一种改进的反卷积方法来进行力的预测，使用加速度的结果来获得冲激响应函数。该技术降低了使用多个加速度分量进行力预测的复杂性。无需进行复杂的计算即可考虑不同方向上的加速度耦合效应，就可以预测力。已经观察到，使用在三个方向上的加速度结果执行的改进的去卷积技术能够以94.50％的平均精度预测力。但是，使用ANFIS预测的力的精度略高，平均准确度为96.30％。因此，本文研究了用于动态校准的两种力预测技术的比较。已经观察到，改进的反褶积和ANFIS都可以用于力预测，而无需进行大量的计算，但是ANFIS的精度（具有97.20％的最大精度）比改进的反褶积（具有96.80％的最大精度）稍高。 ）。这证实了修改后的反卷积能力可以与其他标准技术（例如ANFIS）相一致地以较小的复杂性来预测力，并且可以用于精确的力预测。已经观察到，改进的反褶积和ANFIS都可以用于力预测，而无需进行大量的计算，但是ANFIS的精度（具有97.20％的最大精度）比改进的反褶积（具有96.80％的最大精度）稍高。 ）。这证实了修改后的反卷积能力可以与其他标准技术（例如ANFIS）相一致地以较小的复杂性来预测力，并且可以用于精确的力预测。已经观察到，改进的反褶积和ANFIS都可以用于力预测，而无需进行大量的计算，但是ANFIS的精度（具有97.20％的最大精度）比改进的反褶积（具有96.80％的最大精度）稍高。 ）。这证实了修改后的反卷积能力可以与其他标准技术（例如ANFIS）相一致地以较小的复杂性来预测力，并且可以用于精确的力预测。