Page 79 - 摩擦学学报2025年第8期

第 8 期 王大刚, 等: 钢丝绳摩擦损伤定量识别研究 1177

strong linear correlation between some characteristic values of damage signal, the linear correlation coefficient between
mean value and root mean square characteristic value was as high as 0.98, the linear correlation coefficient between peak
factor and margin factor was as high as 0.89, and the linear correlation between waveform factor and pulse factor was as
high as 0.9. The linear correlation coefficient between wavelet energy and peak-to-peak value was higher than that
between wavelet energy and other eigenvalues and was 0.8. After principal component analysis, the feature value of the
reduced signal was reduced from 15 to 7 and the cumulative contribution rate was 99.19%, which reduced the
complexity of signal processing and retained the damage feature information. The damage signal wavelet time-frequency
graph could effectively reflect the damage degree of wire rope cross-sectional area. The larger the damage of wire rope
cross-sectional area, the darker the color of damage signal wavelet time-frequency graph. The performance of different
quantitative recognition models was different, in order from high to low: IncepRegCNN neural network > PSO-BP
neural network > BP neural network, in which IncepRegCNN neural network RMSE and MAE were reduced by more
2
than 50%, and R was increased by more than 15%. The maximum absolute error of IncepRegCNN neural network for
quantitative identification of the cross-sectional area loss of steel wire rope friction damage was 0.4%, which had a high
recognition accuracy.
Key words: steel wire rope; friction damage; wire break; neural network; quantitative identification

钢丝绳在海洋钻井平台和矿井提升机的起升系 定量识别方法.
统等领域应用广泛 [1-2] ，但随着钢丝绳服役时间的增加 本文中通过自制的钢丝绳摩擦磨损试验台开展
以及受复杂载荷工况的影响，易导致钢丝绳与各种摩 不同滑移与循环次数的钢丝绳间的摩擦磨损试验，并
擦配副间（如钢丝绳与钢丝绳间、钢丝绳与绳槽间）接 提出1种钢丝绳截面积损失定量表征方法. 通过漏磁
触和滑移，引起钢丝绳产生摩擦磨损损伤 [3-4] ，致使钢 检测装置获取损伤钢丝绳漏磁信号并进行降噪处理，
丝绳出现断丝及磨损钢丝等情况，进而损害钢丝绳的 全面提取信号特征值并对特征值进行相关性分析与
有效承载横截面积，对人员安全和经济效益造成严重 降维处理. 基于特征值降维后的成分构建机器学习数
影响. 因此，定期安全检测钢丝绳有效承载横截面积 据库，采用时频图转换方法处理降噪后信号获得深度

对保证生产顺利和保障人员安全具有重要意义. 学习数据库，基于损伤钢丝绳定量识别数据库设计多
漏磁检测指通过磁传感器对饱和磁化后的钢丝 种定量识别算法，实现对钢丝绳摩擦损伤的定量识别.

绳损伤处漏磁场进行检测，并对检测信号进行定量分
析与损伤反演 [5-7] . 学者们结合信号处理方法与定量识 1 钢丝绳摩擦磨损试验

别方法对漏磁信号进行处理与损伤反演，在对钢丝绳 1.1 试验方案
损伤进行定量识别方面进行了多方面的工作. 在信号 本文中选用江苏中英钢丝绳有限公司生产的同
降噪方面，可以通过低通滤波器和高通滤波器法滤除 一批次海洋钻机起升钢丝绳6×19+IWS作为研究对
信号中的高频和低频信号 ，以及利用量子粒子群优 象，包含6个外层螺旋股和1个中心股，每股中包含
[8]
[9]
化算法优化移位平均法的窗宽提高信噪比 、模糊提 19根钢丝，钢丝绳结构参数列于表1中.
升小波包及模糊阈值等小波分析法消除信号中的噪
声 [10-11] . 在漏磁信号特征提取方面，对信号的峰值、波 表 1 钢丝绳结构参数
Table 1 Structural parameters of wire rope
形下面积、小波能量和短时波动能量等时域与频域特
Parameters Specifications
征进行提取 [12-13] ，但信号特征提取较依赖于人工，提取 Wire rope diameter 13.0 mm
的特征较为有限，难以完全获得损伤信号特征. 在损 Lay of rope 78.0 mm
Spiral strand wire diameter 0.8 mm
伤定量识别方面，通常运用支持向量法、最小二乘法
Center strand wire diameter 1.0 mm
[14]
和主元分析法等进行缺陷计算 ，也可以通过使用卷 Twisting mode Right cross twist
积神经网络对漏磁检测信号进行缺陷大小评估，提高
[15]
检测识别率 . 现有研究中多基于少量制备的损伤钢 根据提升系统中钢丝绳在卷筒的折线段区域存
丝绳样品、提取少量信号特征和建立简单模型上，对 在钢丝绳交叉摩擦行为以及钢丝绳在缠绕过程中会
于实际钢丝绳摩擦损伤中同时包含断丝与磨损钢丝 出现咬绳和骑绳等大交叉角度的恶劣摩擦工况，并且
损伤的识别效果有限，缺乏对钢丝绳摩擦损伤的有效 参考国内外学者对不同交叉角度下钢丝绳及钢丝的