Page 78 - 摩擦学学报2025年第8期

第 45 卷 第 8 期 摩擦学学报（中英文） Vol 45 No 8
2025 年 8 月 Tribology Aug, 2025

DOI: 10.16078/j.tribology.2024141 CSTR: 32261.14.j.tribology.2024141

王大刚, 汤梁, 孙远, 种海浪, 王博, 李陈晨, 纪玮. 钢丝绳摩擦损伤定量识别研究[J]. 摩擦学学报（中英文）, 2025, 45(8):
1176−1192. WANG Dagang, TANG Liang, SUN Yuan, CHONG Hailang, WANG Bo, LI Chenchen, JI Wei. Quantitative
Identification of Friction Damage of Steel Wire Rope[J]. Tribology, 2025, 45(8): 1176−1192.

钢丝绳摩擦损伤定量识别研究

*
王大刚 , 汤 梁, 孙 远, 种海浪, 王 博, 李陈晨, 纪 玮
(中国矿业大学 机电工程学院，江苏 徐州 221116)

摘 要: 提升钢丝绳因拉伸、弯曲和扭转载荷易发生摩擦损伤(摩擦磨损和断丝)，降低提升钢丝绳横截面有效承载
面积和承载强度，甚至诱发断绳事故，故钢丝绳摩擦损伤定量识别对预估钢丝绳剩余承载强度和提高钢丝绳承载
安全性至关重要. 运用自制钢丝绳摩擦磨损试验台开展钢丝绳摩擦磨损试验，提出了钢丝绳横截面积损失定量表
征方法，获得了钢丝绳摩擦损伤量化数据库；通过自制钢丝绳漏磁检测装置检测不同钢丝绳摩擦损伤试样轴向漏
磁信号，基于去趋势项、规范化和信号降噪方法及相关性分析和降维方法等特征值处理方法，构建了钢丝绳摩擦损
伤定量识别数据库；通过摩擦损伤定量识别算法进行训练，获得了钢丝绳摩擦损伤定量识别模型. 结果表明：基于
CNN网络Inception架构构建的回归神经网络对钢丝绳摩擦损伤横截面积损失定量识别具有高识别精度.
关键词: 钢丝绳; 摩擦磨损; 断丝; 神经网络; 定量识别
中图分类号: TH117.1 文献标志码: A 文章编号: 1004-0595(2025)08–1176–17

Quantitative Identification of Friction

Damage of Steel Wire Rope

*
WANG Dagang , TANG Liang, SUN Yuan, CHONG Hailang, WANG Bo, LI Chenchen, JI Wei

(School of Mechanical and Electrical Engineering, China University of Mining and Technology,
Jiangsu Xuzhou 221116, China)
Abstract: With the increase of the service time of the lifting wire rope and the influence of complex working conditions
such as tensile, bending and torsional loads, it is easy to cause friction damage to the rope, resulting in wire breakage and
wear of the rope, reducing the effective bearing area and bearing strength of the lifting wire rope cross-section, and even
inducing rope breaking accidents. Therefore, the quantitative identification of friction damage of wire rope is very
important for predicting the residual load strength of wire rope and improving the load safety of wire rope. This paper
took 6×19+IWS wire rope commonly used in lifting system as the research object. Based on the self-made wire rope
friction and wear test bench, the friction and wear experiments of wire rope under different slip amplitudes and cycles
were carried out. A quantitative characterization method was proposed for the cross-sectional area loss of wire rope. The
machine learning database of cross-sectional area loss and deep learning database of cross-sectional area loss of wire
rope were constructed by using discrete wavelet denoising, eigenvalue correlation analysis and principal component
analysis to process the damaged signal. A quantitative recognition model of friction damage of steel wire rope was
obtained by designing and training several algorithms. The results showed that the optimal denoising of the damaged
signal was the 3-layer decomposition of the basic wavelet db4 and the signal-to-noise ratio could reach 25.2. There was a

Received 2 June 2024, revised 19 August 2024, accepted 21 August 2024, available online 3 January 2025.
*Corresponding author. E-mail: wangdg@cumt.edu.cn, Tel: +86-15162110590.
This project was supported by the National Natural Science Foundation of China (52175205, 51875565, 51405489).
国家自然科学基金项目(52175205, 51875565, 51405489)资助.