Load moment limiting device self-adaption accuracy calibrating method based on artificial neural network algorithm

Abstract

A precision calibration method of a load moment limiter which timely measures and displays crane working condition parameters as the weight, the amplitude, the length, the angles and the like. The method comprises the following steps: adopting the self-adapting calibration technology based on artificial neural net algorithm; through transforming a circuit, a single chip microcomputer and a memoryto automatically collect and store the working condition parameters in different motion states (upward and downward or stillness) and timely operating and treating data, obtaining precision value of the weight and the amplitude of the crane in the working condition; having no need for operating and treating data additionally; effectively excluding the errors due to man-made interference; and greatly improving the efficiency and the precision of the load moment limiter. The method can cause the load moment limiter to precisely calibrate according to the practical use condition, improve the precision and the debugging efficiency of the load moment limiter, effectively guarantee the operating safety of the crane, and is widely applicable to the precision calibration of load moment limiters of mobile cranes (an automobile crane, a crawler crane and a tyre crane) and non-mobile cranes.

Description

1. Technical field [0001] The invention relates to a method for calibrating the accuracy of crane working condition parameters such as weight, amplitude, length and angle measured and displayed in real time by a lifting moment limiter. 2. Background technology [0002] The lifting moment limiter obtains real-time working condition parameter signals from the position sensor (measures signals such as length and angle) and force sensor (measures signals such as tension and pressure). The precision calibration method adopted is to measure the different working conditions of the crane The sensor signal during hoisting is obtained through a specific algorithm to obtain the parameter conversion formula, and then written into the single-chip microcomputer for processing and calculation, to obtain the actual lifting weight and amplitude, and compare and judge with the maximum torque, and output control signals in a dangerous state to prevent the crane from moving Dangerous state acti...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the problems of long time, low efficiency and easy to generate errors in the precision calibration of existing lifting moment limiters, the present invention adopts self-adaptive calibration based on artificial neural network algorithm Technology, through the conversion circuit, single-chip microcomputer and memory to automatically collect and store the working condition parameter data of the boom in different motion states (upward, downward or stationary) and immediately perform calculation processing to obtain the working weight of the crane under this working condition Accurate numerical value and amplitude do not need additional data calculation processing, effectively eliminate the error caused by human interference, and greatly improve the efficiency and accuracy of the precision calibration of the lifting moment limiter

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