钢丝绳无损检测技术研究综述
Review of Research Progress on Non-Destructive Testing Technologies for Steel Wire Ropes
DOI: 10.12677/me.2026.141001, PDF,   
作者: 王龙刚, 侯小静, 王国强：洛阳理工学院计算机学院，河南 洛阳；华翔宇, 陈 峰：洛阳泰斯特探伤技术有限公司，河南 洛阳
关键词: 钢丝绳；无损检测；损伤类型；检测技术；人工智能；Steel Wire Rope； Non-Destructive Detection； Damage Type； Detection Technology； Artificial Intelligence
摘要: 钢丝绳作为矿山、建筑、交通运输等重大工程中的关键承载构件，其运行状态直接关系到生产安全与经济效益。长期处于恶劣工况下，钢丝绳易发生断丝、磨损、锈蚀等损伤，若未及时检测，可能引发重大安全事故。本文系统综述了钢丝绳无损检测技术的研究进展。首先，阐述钢丝绳检测的研究背景与国内外发展现状，梳理技术演进脉络；其次，分析钢丝绳主要损伤类型及其成因，为检测需求提供理论基础；随后，详细评述电磁检测、机器视觉、声发射等主流无损检测技术的原理、优势、局限性及最新研究成果；进一步探讨人工智能、多传感器融合、数字孪生等前沿技术在钢丝绳检测中的应用潜力；最后，总结当前技术瓶颈并展望未来发展方向，为钢丝绳无损检测的创新研发与工程应用提供参考。
Abstract: Steel wire ropes, critical load-bearing components in industries such as mining, construction, and transportation, significantly impact production safety and economic efficiency. Prolonged exposure to harsh conditions makes them susceptible to damage like wire breakage, wear, and corrosion, which, if undetected, may lead to severe safety accidents. This paper systematically reviews the research progress in non-destructive testing technologies for steel wire ropes. It begins by outlining the research background and global development status, clarifying the technological evolution. Next, it analyzes the primary damage types and their causes, providing a theoretical basis for detection requirements. Subsequently, it evaluates the principles, advantages, limitations, and recent advancements of mainstream NDT methods, including electromagnetic, machine vision, and acoustic emission detection. Furthermore, it explores the potential of emerging technologies such as artificial intelligence, multi-sensor fusion, and digital twins in enhancing steel wire rope NDT. Finally, it summarizes current technical challenges and prospects for future development directions, offering insights for innovative research and engineering applications.
文章引用：王龙刚, 侯小静, 华翔宇, 陈峰, 王国强. 钢丝绳无损检测技术研究综述[J]. 矿山工程, 2026, 14(1): 1-10. https://doi.org/10.12677/me.2026.141001

参考文献

[1] 谭继文. 钢丝绳安全检测原理与技术[M]. 北京: 科学出版社, 2009.
[2] 周坪. 钢丝绳视觉无损检测与评估方法研究[D]: [博士学位论文]. 北京: 中国矿业大学, 2021.
[3] 王济广. 矿用提升机钢丝绳缺陷检测系统研制[D]: [硕士学位论文]. 淮南: 安徽理工大学, 2022.
[4] 王士豪. 矿用钢丝绳局部损伤磁检测方法研究[D]: [硕士学位论文]. 北京: 中国矿业大学, 2024.
[5] Mazurek, P. (2023) A Comprehensive Review of Steel Wire Rope Degradation Mechanisms and Recent Damage Detection Methods. Sustainability, 15, Article 5441. [Google Scholar] [CrossRef] 
[6] Zhou, P., Zhou, G., Zhu, Z., He, Z., Ding, X. and Tang, C. (2019) A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes. Applied Sciences, 9, Article 2771. [Google Scholar] [CrossRef] 
[7] Zhang, J., Li, H. and Qu, Y. (2025) Quantitative Study on Characteristic Values of Magnetic Signals for Early Damage of Steel Wire Ropes under Weak Magnetic Excitation. Journal of Failure Analysis and Prevention, 25, 2458-2468. [Google Scholar] [CrossRef] 
[8] Wei, J., Zhang, J. and Wang, H. (2025) Detection of Surface Damage on Steel Wire Ropes Based on Improved U-Net. Journal of Failure Analysis and Prevention, 25, 458-467. [Google Scholar] [CrossRef] 
[9] Zhao, C., Tian, J., Wang, H., Shi, Z., Wang, X., Huang, J., et al. (2025) An End-To-End Quantitative Identification Method for Mining Wire Rope Damage Based on Time Series Classification and Deep Learning. Journal of Nondestructive Evaluation, 44, Article No. 25. [Google Scholar] [CrossRef] 
[10] 于小杰, 李旭东, 解社娟, 等. 钢丝绳断丝损伤涡流检测方法[J]. 中国机械工程, 2019, 30(22): 2757-2763.
[11] 朱海平. 矿井提升钢丝绳表面损伤在线视觉检测系统研究[D]: [硕士学位论文]. 北京: 中国矿业大学, 2023.
[12] 王红尧, 吴佳奇, 李长恒, 等. 矿用钢丝绳损伤检测信号处理方法研究[J]. 工矿自动化, 2021, 47(2): 58-62.
[13] 王浩宇. 矿用钢丝绳损伤漏磁检测系统研究[D]: [硕士学位论文]. 北京: 中国矿业大学, 2023.
[14] Wang, H., Zheng, H., Tian, J., He, H., Ji, Z. and He, X. (2024) Research on Quantitative Identification Method for Wire Rope Wire Breakage Damage Signals Based on Multi-Decomposition Information Fusion. Journal of Safety and Sustainability, 1, 89-97. [Google Scholar] [CrossRef] 
[15] Zhou, P., Zhou, G., Wang, H., Li, X., Wang, H., He, Z., et al. (2024) Intelligent Visual Detection Method for the Early Surface Damage of Mine Hoisting Wire Ropes. Measurement Science and Technology, 35, Article ID: 115018. [Google Scholar] [CrossRef] 
[16] Tian, J., Zhao, C. and Wang, H. (2024) Damage Identification for Mining Wire Rope Based on Continuous Wavelet Transform and Convolutional Neural Network. Nondestructive Testing and Evaluation, 40, 2598-2620. [Google Scholar] [CrossRef] 
[17] 姚毅. 钢丝绳损伤检测与定量识别研究[D]: [硕士学位论文]. 济南: 济南大学, 2021.
[18] 石晟, 张炳福, 赵庆龙. 基于机器视觉的矿用钢丝绳无损监测系统研究[J]. 煤矿机电, 2017(4): 19-22.
[19] 杨叔子, 康宜华, 陈厚桂, 等. 钢丝绳电磁无损检测[M]. 北京: 机械工业出版社, 2017.
[20] Wang, H., Zhang, J. and Wei, J. (2024) Nondestructive Detection of Wire Rope Damage Using Leakage Magnetic Technique Based on Dual-Layer Sensors. Russian Journal of Nondestructive Testing, 60, 801-812. [Google Scholar] [CrossRef] 
[21] Li, X., Sun, Y., Liu, X. and Zhang, S. (2024) Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes. Machines, 12, Article 801. [Google Scholar] [CrossRef] 
[22] Zhao, M., Ding, N., Fang, Z., Jiang, B., Zhong, J. and Deng, F. (2025) Nondestructive Inspection of Steel Cables Based on Yolov9 with Magnetic Flux Leakage Images. Journal of Sensor and Actuator Networks, 14, Article 80. [Google Scholar] [CrossRef] 
[23] 吴澎, 花虎跃. 钢丝绳无损检测中存在问题的探讨[J]. 无损检测, 2017, 39(6): 65-68.
[24] Mazurek, P., Roskosz, M. and Kwaśniewski, J. (2024) Analysis of the Resolution of the Passive Magnetic Method on the Example of Nondestructive Testing of Steel Wire Ropes. Journal of Magnetism and Magnetic Materials, 589, Article ID: 171607. [Google Scholar] [CrossRef] 
[25] Chen, J., Wang, Y., Liu, S., Ji, Z. and Zhang, Z. (2024) Non-Destructive Testing of Wire Rope Algorithm Based on Lightweight YOLOv7-Tiny. International Conference on Algorithms Software Engineering and Network Security, Nanchang, 26-28 April 2024, 77-83. [Google Scholar] [CrossRef] 
[26] Bao, Y. and Hu, B. (2024) A New Method for Optical Steel Rope Non-Destructive Damage Detection. 2024 2nd International Conference on Intelligent Perception and Computer Vision (CIPCV), Xiamen, 17-19 May 2024, 87-95. [Google Scholar] [CrossRef] 
[27] Li, G., Cao, B., Zhou, Y., Fan, M. and Yang, L. (2025) Evaluation of Broken Steel Wire Rope Using Magnetic Flux Leakage and Optimised Convolutional Neural Network. Nondestructive Testing and Evaluation. [Google Scholar] [CrossRef] 
[28] Shivkumar, K., Konindanala, N.P., Ashfaq, S.S. and Boothalingam, R. (2025) Classification of Steel Wire Ropes Using Signal Processing and Machine Learning. AIP Conference Proceedings, 3175, Article ID: 020046. [Google Scholar] [CrossRef] 
[29] Han, J., Zhang, Y., Feng, Z. and Zhao, L. (2024) Research on Intelligent Identification Algorithm for Steel Wire Rope Damage Based on Residual Network. Applied Sciences, 14, Article 3753. [Google Scholar] [CrossRef] 
[30] Peng, Y., Liu, J., He, J., Qiu, Y., Liu, X., Chen, L., et al. (2024) Steel Wire Rope Damage Width Identification Method Based on Residual Networks and Multi-Channel Feature Fusion. Machines, 12, Article 744. [Google Scholar] [CrossRef] 

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