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Test method for lowering load equivalent of large overhead cranes

A technology of bridge cranes and detection methods, applied in the direction of testing of machine/structural components, measuring devices, instruments, etc.

Inactive Publication Date: 2015-09-16
TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the above-mentioned problems existing in the load test of the existing large-tonnage bridge crane, the present invention provides a detection method for the load reduction equivalent test of the large-tonnage bridge crane

Method used

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  • Test method for lowering load equivalent of large overhead cranes
  • Test method for lowering load equivalent of large overhead cranes
  • Test method for lowering load equivalent of large overhead cranes

Examples

Experimental program
Comparison scheme
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Embodiment

[0053] 1. Test the stress of a box-section double-girder bridge crane with a rated lifting load of 50t by step-by-step loading and displacement , some data are as follows:

[0054]

[0055] Based on the data obtained from the test, the nonlinear mapping of lifting load, stress and displacement is established by BP neural network method; the input of BP neural network is the lifting load and the output is stress and displacement, and the sigmoid function is the transfer function. Building a model with BP neural network needs to be implemented with the help of a computer, and there are literatures that can be referred to, so I won't describe it in detail here.

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Abstract

The invention belongs to the field of detection of bridge cranes, particularly relates to a test detection method of load shedding equivalent weight of a large bridge crane, and solves the problem existing in a load test of a large-tonnage bridge crane. According to the method, stress data and displacement data of bridge cranes with different spans under different lifting loads are tested and recorded, continuous approximately-real mapping of the lifting loads, stress and displacement is obtained with a neural network numerical method; stress and displacement of a structure under the load shedding and lifting load actions are obtained through a load shedding test of a to-be-detected bridge crane, stress similitude ratios and displacement similitude ratios of the to-be-detected bridge crane and a detected crane are calculated when the lifting load ratios of the to-be-detected bridge crane and the detected crane are equal to rated lift load ratios, and stress and displacement of the to-be-detected bridge crane in the static test are calculated according to the stress, the displacement data, the stress similitude ratio and the displacement similitude ratio of the detected crane in the static test. The method improves the economic efficiency and the safety of the detection test of the large bridge crane.

Description

technical field [0001] The invention belongs to the field of inspection of bridge cranes, and in particular relates to a load reduction test detection method for large bridge cranes. Background technique [0002] According to the relevant regulations, the special equipment supervision and inspection department needs to regularly inspect the crane to ensure the normal use of the crane. At present, the test method commonly used in the supervision and inspection of bridge cranes is to gradually load the test load to 1.25 times the rated lifting load to observe whether there are obvious cracks in the crane structure and to measure whether the mid-span displacement of the crane meets the relevant technical regulations. The relationship between the load, stress and displacement of the crane system is simplified to be linear in design, and no systematic experimental research has been carried out. With the continuous enlargement and heavy load of cranes, the following problems have...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M99/00
Inventor 杨瑞刚徐格宁银明段治斌
Owner TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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