Compensation of automatic weighing error of belt weigher based on BP neural network

A belt weigher is widely used in industrial production and trade settlement; however, it is difficult to maintain its nominal measuring accuracy in service. With the belt weigher indication, average speed of belt, variation in belt sag, running deviation of belt, environmental temperature, and humidity as inputs, and the control instrument indication as output, a BP neural network model to compensate the automatic weighing error of the belt weigher is built. We obtained the sample data depending on the test system for the type evaluation of the belt weigher in the Jiangsu Institute of Metrology, as well as supplemental relevant parameters for the monitoring devices. The BP model is trained and validated using MATLAB. The validation results show that the absolute value of the maximum relative automatic weighing error output by the BP model is less than 0.5%, significantly lower than the error before using the BP model for compensation. The BP model is effective, feasible, and practical for compensating the automatic weighing error of the belt weigher.