Selection of a rational algorithm for data processing of the weight measuring system of a hoisting crane

Abstract

The paper deals with the choice of the algorithm for processing the data of the load sensor of the crane weight measuring system being developed. The requirements for the weight measuring system are based on the operating conditions of the crane (the nature of the dynamic process, the limitation of the weighing time) and the planned use of information about the weight of goods (the required accuracy). A comparison of 5 signal processing algorithms is presented: direct averaging, search for extremes with two different modes of pre-smoothing, nonlinear regression, and averaging using a short-term memory buffer. A series of experiments was conducted to compare the methods. The data was obtained on the KMG-201 bridge crane. Based on the results of numerical evaluation of the results of experimental data processing, conclusions are drawn about the applicability of the considered algorithms for analyzing the dynamic process of the crane lifting mechanism. The algorithms for finding extremes, regardless of the pre-smoothing modes, did not show the required accuracy (the relative error was 5%). The averaging algorithm using a short-term memory buffer showed the highest accuracy (the relative error was 1%). The influence of the parameters of the data reception board on the result of the algorithms is estimated. The parameters varied in the range: bit depth 10-12 bits, sensor polling frequency 4.1-256 Hz. Most experiments showed an increase in accuracy with increasing bit depth. Also, according to the results of the experiments, it was found that an increase in the sensor polling frequency of more than 64 Hz in the conditions of this dynamic process does not affect the accuracy of determining the weight of the load. The study showed the need for further work in the direction of determining the optimal parameters of the averaging algorithm with a short-term memory buffer and determining the limit of increasing accuracy with increasing the bit depth of the data reception board.

Reducing the energy consumption of lifting machines

“…𝑁 𝐺 𝐺 𝐺 • 𝑉 (5) for the case of a cabin descent without cargo (Figure 1b) 𝑁 𝐺 𝐺 • 𝑉 (6) where V is the cabin speed G cab is the weight of the cabin, G c is the weight of the lifted cargo, G counter is the weight of the counterweight. The energy of the lifting electric motor is consumed when the fully loaded cabin moves up (Figure 2g) or the empty cabin moves down (Figure 2d and 2e) [8][9][10].…”