Research on Identification Method of Cable Cross-Sectional Loss Rates Based on Multiple Magnetic Characteristic Indicators

The identification of cross-sectional loss in cables due to corrosion is crucial for evaluating the remaining strength of bridge cables. To accurately determine the cross-sectional loss rate, this paper derived a three-dimensional magnetic dipole model for spatial cable damage. The study employed an independently designed self-magnetic flux leakage (SMFL) sensor array to detect corrosion on a bundle of 37 parallel steel wires. The analysis investigated the correlation between corrosion degrees and SMFL signal features. The results show that the spatial magnetic field inversion collected by the sensor array device is more accurate. The cable damage location can be pinpointed by observing abrupt changes in the B_x and B_z curves. Additionally, this paper introduces five corrosion characterization features, all correlated with the cable cross-sectional loss rate. However, recognition stability using a single characteristic value is insufficient. The cable cross-sectional loss rate identification method, utilizing a back propagation neural network in conjunction with multiple characteristic indicators, demonstrates robust quantitative and adaptive capabilities. The maximum relative error of this method is 7.6%, offering a new perspective for future cable damage detection.