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As an actual steel material application, steel wire rope (SWR) is widely used in various industrial cases. However, in real cases, SWRs are inevitably damaged by erosion, friction, etc. Therefore, it is of great importance to monitor SWRs’ surface conditions in case of accidents. In recent years, computer vision recognition of SWR’s surface has been extensively studied. Based on it, we propose an image-processing method to detect local flaws of SWRs, which extracts the features from the illumination area on the surface of the SWR. Firstly, a three-feature dimension descriptor is constructed by combining the edge grayscale value gradient of the illumination area and morphological features of the illumination area. Then, the KNN (k-nearest neighbour) algorithm is used as a classification method to distinguish the local flaws. According to the experiment results, our proposed method has a high accuracy which is about 94.35%, and low time cost. It can be used in future local flaw detection.

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Steel wire rope (SWR) defects plagued its application in many important fields, such as cranes, ports, etc. However, the accuracy of SWRs’ local flaws detection based on magnetic flux leakage (MFL) method is susceptible to the lift-off distance. This paper proposes an instantaneous lift-off distance estimation method to study the effect of lift-off distance on MFL detection for SWR. We firstly derive the relation between the lift-off distance and the MFL field strength based on the magnetic dipole model. After obtaining the time-frequency representation of the MFL signal by using multi-synchrosqueezing transform, we extract the instantaneous amplitude (IA) of the strand signal without other noise interference. Finally, the nonlinear least-squares method is used to fit the relation between the IAs of the strand signals and the lift-off distances to achieve the estimation of the instantaneous lift-off distance. A SWR with three local flaws is tested to validate the proposed method. This work provides instantaneous monitoring of lift-off distances, and it can be used in future noise suppressing methods development or quantitative analysis of local flaws.