Analysis And Compensation Of Weighing Error Of Double-lever Belt Scale

Electronic belt weight is a weighing device for dynamic weighing of bulk material,its weighing error is influenced by many factors,the effect of some factors can be reduced by optimizing the structure of the load-bearing device,double lever type bearing device is a relatively fine structure But its dynamic measurement error cannot be ignored in the actual use.This article start from the dynamic measurement method of double lever type belt scale,analysis the source of the dynamic measurement error of the belt scale,then it is known that the dynamic error is retated to the the variation of the belt sag in the measuring section.In order to study the influence of the weight error and the velocity measurement error by the belt sag,it is should to know the sag signal.the weighing signal and the speed signal.Selecting the HG-C1100 laser displacement sensor to detect the change of belt sag,this sensor has the advantage of high measurement accuracy.fast sampling speed.and good linearity for its unique algorithm.Based on AD620 to amplify the weak weighing signal,measure speed signals by photoelectric encoder,realizing the linear compensation of double lever type weighing error,and the precision of dynamic measurement is obviously improved Using Kalman filter to reduce the influence of random noise in the weighing signal,then design an improved scheme of Caiman filter based on adjustment gain to filter the field values.Design a low pass filter based on IIR filter or FIR filter to reduce the influence of high frequency vibration and noise signals in the sag signal.Using the weighing signal of Caiman filter and sag signal of IIR filter,realize the linear compensation of dynamic error of double lever belt.In order to further improve the dynamic error,Based on the output of the belt scale is a cumulative amount of time,research process extreme learning machine to build compensation model.In the process of model training,based on Enhanced Incremental extreme learning machine and Error Minimized extreme learning machine design Variable Incremental extreme learning machine.During the model training find that the model of EM-PELM.VI-PELM has the shortest training time,and when artificially changing the sag of 2mm,the error compensation model of VI-ELM has the best compensation effect when the flow of material flow betweenlOkg/m and 35kg/m.