Measurement method of cable tension in transmission lines based on micro vibration broadband phase-based motion amplification and deep learning
Cable type components are widely distributed in transmission lines,and their tension values and changes are key factors affecting intrinsic safety of transmission lines.Therefore,they are the focus of state monitoring during engineering construction,operation and maintenance of transmission lines.The traditional method of measuring cable tension has problems of low accuracy,high environmental requirements and being difficult to monitor with electricity,and is not universally applicable in transmission lines.Here,an image tension measurement method combining broadband phase-based motion magnification(BPMM)method with deep learning semantic segmentation was proposed.By enhancing image vibration amplitude,micro vibration image of transmission line cable component under environmental excitation could be magnified.To eliminate noise artifacts caused by BPMM algorithm in vibration video processing and improve recognition accuracy,a joint segmentation method based on deep learning U-Net network and level set loss entropy was proposed to extract the centroid of cable components,and realize accurate picking of micro vibration pixel changes.Then,natural frequency was obtained with frequency domain analysis and cable tension was calculated.Tests and engineering applications showed that the tension measurement method for cable component of transmission line based on micro vibration amplification can effectively identify small vibration changes of cables under environmental excitation;the measured cable tension value has an error of less than 6%compared to the measured value with sensor to realize high-precision,non-contact measurement of cable component tension of transmission line and solve the difficult problem of tension with electricity measurement of transmission line.
transmission linemicro vibrationtension measurementdeep learningimage recognitionvibration frequency