A Non-destructive Testing Method and System for Steel Wire Rope Based on Sensor and Physical Feature Fusion

Abstract

This invention discloses a non-destructive testing method and system for steel wire ropes based on the fusion of sensing and physical features, belonging to the field of non-destructive testing technology for steel wire ropes. The method first saturates the steel wire rope with an axial Gaussian difference excitation probe to improve the signal-to-noise ratio of the defect leakage magnetic field signal; simultaneously, an inertial measurement unit synchronously acquires instantaneous acceleration. A velocity-signal model is established based on the law of electromagnetic induction to dynamically compensate the original voltage signal of the leakage magnetic field. Then, characteristic parameters with clear physical meaning, such as peak voltage, maximum gradient, and full width at half maximum (FWHM), are quantized and extracted from the standardized leakage magnetic field signal, and fused with deep features extracted by a deep learning model. The fused feature vector is input into a fully connected neural network and a classifier to achieve defect type identification and severity assessment. This invention enables high-precision, interpretable intelligent detection and remaining life prediction of steel wire rope defects, significantly improving detection reliability and supporting predictive maintenance.

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