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Quantitative identification method of broken wires of crane wire ropes driven by simulation data

Published:2024-01-29 author:WANG Xiaokun, JING Luyang, BAI Xiaorui, et al. Browse: 196 Check PDF documents

Quantitative identification method of broken wires of crane wire ropes

driven by simulation data

WANG Xiaokun1, JING Luyang1, BAI Xiaorui2, ZHAN Weixia1, LIU Yuncheng3, WANG Yansong3

(1.Mechanical and Automotive Engineering College, Qingdao Technological University, Qingdao 266520, China;

2.College of Weaponry Engineering, Naval University of Engineering, Wuhan 430032, China;

3.MESNAC Co., Ltd., Qingdao 266000, China)

Abstract: To solve the problem that damage detection of crane wire ropes suffers from insufficient fault samples in engineering, a quantitative identification method of broken wire ropes based on convolution neural network(CNN)model and driven by simulation data was proposed. Firstly, the finite element software COMSOL was used to model and simulate the different damage types of 6×37 structural wire ropes with a diameter of 24mm, and the simulated leakage magnetic field was subtracted from the background magnetic field at the lift-off value of 5mm to obtain the simulated damage signals. Secondly, the magnetic flux leakage test bench of wire ropes was set up to collect the measured signals of broken wire ropes consistent with the simulation parameters, and the wavelet denoising was used to solve the problem that the measured signals did not match the simulated signals due to noise interference. Finally, the convolution neural network model was established and the simulated data were used to assist the model training, and the early stop method was introduced to prevent the overfitting of the model to the simulated data. The experimental verification was conducted using simulation data as the training set and simulation data assisted with small sample measured data as the training set. The research results show that the recognition rate of broken wires can be greatly improved by the simulation data-driven and wavelet denoising, and the early stop method can effectively suppress overfitting problems in experiments. The accuracy of the method in two experimental forms is 84.5% and 97.5% respectively, which proves that the method can effectively identify the damage of wire ropes, and has certain theoretical research value and engineering application prospect.

Key words: hoisting machinery; electromagnetic detection; mode recognition method; convolutional neural network(CNN); wavelet transforms; small sample data; COMSOL

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