scholarly journals State estimation for dynamic weighing using Kalman filter

2021 ◽  
Author(s):  
◽  
Sunethra Pitawala

<p>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.</p>

2021 ◽  
Author(s):  
◽  
Sunethra Pitawala

<p>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.</p>


2013 ◽  
Vol 562-565 ◽  
pp. 426-430
Author(s):  
Xing Ma ◽  
Lun Chao Zhong ◽  
Jun Li Han ◽  
Chang Shun Liu

In allusion to low accurary and poor stability of the initial alginment based on the acceleration sensor, the HMM (Hidden Marco Model) / steady state Kalman filter is designed to solve the problem above. C8051F340 is used to sample the acceleration data of the MEMS acceleration sensor Model1221, then the digital low-pass filter is used to filter the acceleration data. The steady state Kalman filter based on the second order HMM model. The initial alignment algorithm outputs the angle value. The experiments demonstrate that the the HMM / steady state Kalman filter is more stable and more precise than the traditional algorithm.


2013 ◽  
Vol 437 ◽  
pp. 840-844 ◽  
Author(s):  
Xiao Gang Liu ◽  
Bing Zhao

This paper use the passive vision system through high-speed camera collects molten pool images; and then according to the frequency domain characteristics of the weld pool image Butterworth low-pass filter; gradient method for image enhancement obtained after pretreatment. Research Roberts, Sobel, Prewitt, Log, Zerocross, and Canny 6 both traditional differential operator edge detection processing results. Through comparison and analysis of choosing threshold for [0.1, 0. Canny operator can get the ideal molten pool edge character, for subsequent welding molten pool defect recognition provides favorable conditions.


2011 ◽  
Vol 105-107 ◽  
pp. 1966-1969
Author(s):  
Tao Guo ◽  
Jie Zhu ◽  
Gui Tang ◽  
Yan Xu

It is a challenging problem to test the acceleration of the high-speed missiles and space shuttle under high temperature. This paper proposed a design of LC-resonant and high-temperature resistant acceleration sensors about the phenomenon. With the operational amplifier OP4177, it produces the input signal that contents with A/D (Analog to Digital) signal. An eight level low-pass filter MAX291 is used for testing after the signal is regulated. This design mainly uses AD7934 to complete the conversion from analog signals to digital signals. It also recognizes the resonant point of LC acceleration sensor by the DSP (Digital Signal Processing)recognizing program. The acceleration is computed finally by the DSP chip.


2009 ◽  
Vol 131 (5) ◽  
Author(s):  
M. Senesh ◽  
A. Wolf

The most frequently used method in a three dimensional human gait analysis involves placing markers on the skin of the analyzed segment. This introduces a significant artifact, which strongly influences the bone position and orientation and joint kinematic estimates. In this study, we tested and evaluated the effect of adding a Kalman filter procedure to the previously reported point cluster technique (PCT) in the estimation of a rigid body motion. We demonstrated the procedures by motion analysis of a compound planar pendulum from indirect opto-electronic measurements of markers attached to an elastic appendage that is restrained to slide along the rigid body long axis. The elastic frequency is close to the pendulum frequency, as in the biomechanical problem, where the soft tissue frequency content is similar to the actual movement of the bones. Comparison of the real pendulum angle to that obtained by several estimation procedures—PCT, Kalman filter followed by PCT, and low pass filter followed by PCT—enables evaluation of the accuracy of the procedures. When comparing the maximal amplitude, no effect was noted by adding the Kalman filter; however, a closer look at the signal revealed that the estimated angle based only on the PCT method was very noisy with fluctuation, while the estimated angle based on the Kalman filter followed by the PCT was a smooth signal. It was also noted that the instantaneous frequencies obtained from the estimated angle based on the PCT method is more dispersed than those obtained from the estimated angle based on Kalman filter followed by the PCT method. Addition of a Kalman filter to the PCT method in the estimation procedure of rigid body motion results in a smoother signal that better represents the real motion, with less signal distortion than when using a digital low pass filter. Furthermore, it can be concluded that adding a Kalman filter to the PCT procedure substantially reduces the dispersion of the maximal and minimal instantaneous frequencies.


Author(s):  
Francesco Centurelli ◽  
Pietro Monsurro ◽  
Giuseppe Scotti ◽  
Pasquale Tommasino ◽  
Alessandro Trifiletti

Author(s):  
Ibrahim Mohd Alsofyani ◽  
Nik Rumzi Nik Idris ◽  
Yahya A. Alamri ◽  
Tole Sutikno ◽  
Aree Wangsupphaphol ◽  
...  

<span lang="EN-US">Torque calculation process is one of the major concerns for controlling induction motors in industry, which requires very accurate state estimation of unmeasurable variables of nonlinear models. This can be solved if the variables used for torque calculation is accurately estimated.  This paper presents a torque calculation based on a voltage model represented with a low-pass filter (LPF), and an extended Kalman filter (EKF). The experimental results showed that the estimated torque at low speed based on EKF is more accurate in the expense of more complicated and larger computational time. </span>


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