Method for Monitoring the Condition of Steel Wire Ropes Based on the Analysis of Changes in the Linear Dimensions of Their Cross-Sections

Reliable detection of defects in steel wire ropes is pivotal to ensuring safety and maintaining operational reliability of hoisting and lifting systems in mining and other industries. This study proposes an automated monitoring method based on analyzing the cross-sectional size profile extracted from high-quality visual images. Each image undergoes preprocessing—adaptive binarization, noise suppression, and edge extraction—followed by formation of a one-dimensional thickness profile along the rope’s longitudinal axis. Aggregate statistical descriptors (mean, standard deviation, extrema, and shape descriptors) computed from this profile are supplied to a CatBoost gradient boosting classifier. The model achieves an F1-score exceeding 0.93 across diagnostic categories (intact, bend, kink, break), with particularly high accuracy for critical damage such as wire breaks. Compared with conventional image CNN classifiers, the proposed approach offers higher interpretability, lower computational complexity, and robustness to noise and visual artifacts. The results substantiate the method’s efficacy for real-time automated condition monitoring of mining equipment and its suitability for integration into industrial machine-vision systems. The results substantiate the method’s efficacy for real-time automated condition monitoring of mining equipment and its suitability for integration into industrial machine-vision systems.