ORIGINAL RESEARCH article
Front. Mech. Eng.
Sec. Vibration Systems
A Cable Tension Measurement Method for Transmission Lines Based on Micro-Vibration Broadband Phase Motion Magnification and Deep Learning
Provisionally accepted
- Guangdong Power Grid Corporation, Guangzhou, China
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Cable components are widely used in transmission lines, and their tension values and variations are critical factors affecting the intrinsic safety of these lines. Thus, tension monitoring becomes a priority during both construction and operational maintenance. Traditional cable tension measurement methods suffer from limitations such as low accuracy, stringent environmental requirements, and difficulties in live-line monitoring, resulting in a lack of universality for application in transmission lines. To address these issues, this paper utilizes visual image technology and Broadband Phase Motion Magnification (BPMM) to amplify the micro-vibration amplitude and enhance the vibration images of transmission line cable-type components under environmental excitation. Furthermore, this study develops a combined segmentation algorithm using the U-Net network architecture and level set loss entropy to accurately capture the centroid motion trajectory of cables, thereby precisely extracting the vibration displacement time series. Finally, spectrum analysis is applied to invert the self-vibration characteristic parameters of the components and establish a tension calculation model. Experimental verification shows that the proposed method can precisely capture the micro-vibration signals induced by environmental excitation. The tension calculation results, when compared to standard sensor data, have a deviation of no more than 8%. This method successfully establishes a non-contact, high-precision measurement system for cable-type components, providing a new technical pathway for intelligent monitoring during the construction and maintenance of transmission lines.
Keywords: Transmission lines, Micro vibration, tension measurement, deep learning, imagerecognition, Vibration frequency
Received: 24 Sep 2025; Accepted: 15 Dec 2025.
Copyright: © 2025 Huang, Wang, Wen and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhi Ming Huang
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