In-situ Calibration Method for Dual-sensor Weighing System Based on Sensitivity Change Mechanism

The sensitivity coefficient is no longer the same before and after the installation of weighing system sensor due to the unbalance loading resulting from the shape error of the sensor mounting surface. Thus, it need to be re-calibrated before use. For dual-sensor systems, due to the different installation conditions of each sensor, the sensitivity coefficient is not only different before and after installation, but also not the same as each other, on the other hand, due to the randomness of load center when calibrating, there is no guarantee that the two sensor bear the same force, so the load weight is unknown. The stochastic interaction between unbalance loading and randomness of loading position makes it difficult to calibrate the sensitivity coefficient of the dual-sensor system. At present, the common calibration method is to attach potentiometer and other components to the processing circuit to changing the magnification of the original signal to make the sensitivity of each path be the same, and then load standard weight to complete the calibration. This method needs to change the original circuit, which may bring unknown influence, and it's difficult to realize for integrated processing circuit. Aiming at the these problems the sensor force model was established, the influence mechanism of unbalance loading and gravity position on sensitivity coefficient was defined by force analysis, and the sensitivity calibration algorithm was deduced. The sensitivity coefficient of dual sensor system was decomposed into two parts, coupling coefficient and conversion coefficient, and calibration could be completed after loading two times in different positions with the same standard weight. This new calibration method could ensure the measurement accuracy of the system under unbalance loading. The experimental results showed that the new method did not change the original measuring circuit and mechanical structure, and the system error was less than 0.03%. © 2020, Editorial Department of Advanced Engineering Sciences. All right reserved.