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.

In the field of nondestructive testing (NDT) of wire rope, magnetic flux leakage (MFL) detection is the most widely used method. The traditional single-dimensional MFL detection system has the disadvantages of large volume, heavy weight, low sensitivity and low recognition rate. In order to overcome these problems, this paper designed the three-dimensional MFL acquisition system based on unsaturated magnetic excitation using the tunnel magnetoresistive (TMR) elements and proposed the threedimensional MFL detection method. We mapped the three-dimensional MFL signals after noise reduction to Red-Green-Blue (RGB) space for color imaging. Then, the localization and segmentation algorithms were used to crop color images of broken wire defects. Finally, the color moment eigenvalues of color images of six types broken wire defects were extracted as the input of the back propagation (BP) neural network to quantitatively identify the broken wires. From the experimental results of the quantitative identification of broken wire defects, it can be seen that it is feasible to use the color images of the threedimensional MFL signals to quantitatively identify the broken wire defects. Moreover, the threedimensional MFL color imaging can effectively improve the recognition rate of broken wires.

Most traditional strong magnetic inspection equipment has disadvantages such as big excitation devices, high weight, low detection precision, and inconvenient operation. This paper presents the design of a giant magneto-resistance (GMR) sensor array collection system. The remanence signal is collected to acquire two-dimensional magnetic flux leakage (MFL) data on the surface of wire ropes. Through the use of compressed sensing wavelet filtering (CSWF), the image expression of wire ropes MFL on the surface was obtained. Then this was taken as the input of the designed back propagation (BP) neural network to extract three kinds of MFL image geometry features and seven invariant moments of defect images. Good results were obtained. The experimental results show that nondestructive inspection through the use of remanence has higher accuracy and reliability compared with traditional inspection devices, along with smaller volume, lighter weight and higher precision.

Detection of magnetic flux leakage (MFL) is widely used in nondestructive testing (NDT) of wire rope. We built and tested an MFL measuring device based on unsaturated magnetic excitation to address the problems of large volume, low accuracy and inconvenience of the traditional MFL instruments for locating and quantifying broken wires in wire ropes. We employed a pseudo-color imaging protocol to convert the MFL information to a pictorial representation. Localization of the breaks in the wire utilized the maximum modulus method. The color moment, statistical texture features, and spectral texture features were extracted from the image, revealing a network of broken wires, which could be localized and quantified. Testing showed that the device has high-detection accuracy and could detect small broken wires. Compared with traditional wire inspection devices, it has the advantages of small size, light weight and portability. We showed that pseudo-color image enhancement can effectively improve the recognition rate of broken wires and reduce the recognition error.

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.

Estimating the exact residual lifetime of wire rope involves the security of industry manufacturing, mining, tourism, and so on. In this paper, a novel testing technology was developed based on unsaturated magnetic excitation, and a fabricating prototype overcame the shortcomings of traditional detection equipment in terms of volume, sensibility, reliability, and weight. Massive artificial discontinuities were applied to examine the effectiveness of this new technology with a giant magneto resistance(GMR) sensor array, which included types of small gaps, curling wires, wide fractures, and abrasion. A resolution enhancement method, which was adopted for multiframe images, was proposed for promoting magnetic flux leakage images of a few sensors. Characteristic vectors of statistics and geometry were extracted, then we applied a radial basis function neural network to achieve a quantitative recognition rate of 91.43% with one wire-limiting error. Experimental results showed that the new device can detect defects in various types of wire rope and prolong the service life with high lift-off distance and high reliability, and the system could provide useful options to evaluate the lifetime of wire rope.