A cable tension measurement method for transmission lines based on micro-vibration broadband phase motion magnification and deep learning

Introduction 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. Methods This paper utilizes visual image technology and Broadband Phase Motion Magnification 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. Results 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%. Discussion 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.