ILF diagnosis for steel wire ropes based on physical information under uniform circular array

Abstract

Internal local flaws (ILFs) are a principal cause of failure in steel wire ropes (SWRs), and accurately assessing their condition remains a significant challenge. In this article, a novel localization and quantification method based on physical information under a uniform circular array (UCA) is proposed. First, simulations and theoretical analyses are employed to investigate the diffusion pattern of the leakage magnetic field induced by ILFs and the corresponding signal distribution within the UCA. Additionally, a relationship between the spatial position of the ILFs and the physical model is established, enabling precise determination of its location in the SWR cross-section. Quantification of the ILFs is further achieved by applying the physical information obtained to a radial basis function (RBF) neural network. Results from simulations and experiments verify that the proposed method can effectively determine the location of ILFs, maintaining a localization error of less than 1 mm. Moreover, by integrating localization, it enhances quantification accuracy, achieving precision finer than 1 broken wire. The proposed method provides a novel perspective for ILF analysis, contributing to the enhanced safety and reliability of SWRs.