Electromagnetic methods are commonly employed to detect wire rope discontinuities. However, determining the residual strength of wire rope based on the quantitative recognition of discontinuities remains problematic. We have designed a prototype device based on the residual magnetic field (RMF) of ferromagnetic materials, which overcomes the disadvantages associated with in-service inspections, such as large volume, inconvenient operation, low precision, and poor portability by providing a relatively small and lightweight device with improved detection precision. A novel filtering system consisting of the Hilbert-Huang transform and compressed sensing wavelet filtering is presented. Digital image processing was applied to achieve the localization and segmentation of defect RMF images. The statistical texture and invariant moment characteristics of the defect images were extracted as the input of a radial basis function neural network. Experimental results show that the RMF device can detect defects in various types of wire rope and prolong the service life of test equipment by reducing the friction between the detection device and the wire rope by accommodating a high lift-off distance.

The magnetic flux leakage method is widely used for non-destructive testing in wire rope applications. A non-destructive testing device for wire rope based on remanence was designed to solve the problems of large volume, low accuracy, and complex operations seen in traditional devices. A wavelet denoising method based on ensemble empirical mode decomposition was proposed to reduce the system noise in broken wire rope testing. After extracting the defects image, the wavelet super-resolution reconstruction technique was adopted to improve the resolution of defect grayscale. A back propagation neural network was designed to classify defects by the feature vectors of area, rectangle, stretch length, and seven invariant moments. The experimental results show that the device was not only highly precise and sensitive, but also easy to operate; noise is effectively suppressed by the proposed filtering algorithm, and broken wires are classified by the network.

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In this paper, we present a nondestructive testing device for wire rope by unsaturated magnetic excitation as an alternative to existing magnetic flux leakage (MFL) detection devices. The existing devices are heavy and inconvenient and offer somewhat lower accuracy and low signal-to-noise ratios (SNRs). Our design implements variational mode decomposition (VMD) and a wavelet transformation to remove noise from the raw MFL signals. Grayscale images representing the denoised MFL data simplify visual interpretation of the results and location of defects in both axial and circumferential directions. Quantification of defects is enabled using a k-nearest neighbor (KNN) algorithm to classify broken wires. Experimental results show that our design offers lighter weight, better convenience, and high sensitivity along with better removal of noise and more accurate classification of defects.