State estimation for dynamic weighing using Kalman filter

Abstract

Dynamic weighing has become an essential requirement in a diverse range of industries. Dynamic weighing is different from static weighing in that static weighing involves determining the weight while the product being weighed is stationary whereas dynamic weighing weighs the products while they are moving. Force sensors are commonly used in these weighing systems. In static weighing, the weighed object is placed stationary on the platform and the steady state of the sensor signal is used to assess the weight. However, in dynamic weighing the sensor signal may not reach the steady state during the brief time of weighing, hence the weight is assessed for example, by averaging the tail end of the signal after it has been through a low-pass filter. The resulting mass estimates can be inaccurate for faster heavier items. It is useful to consider better ways of estimating the true weight, in high speed weighing applications.

The proposed method is to employ the 1-D Kalman filter algorithm to estimate the optimal state of the signal. The improved steady state signal is then used in weight estimation. The proposed method has been tested using data collected from a loadcell when different masses pass over the loadcell. The results show a significant improvement in the filtered signal quality which is then used to improve the weight assessment.