Abstract
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.
Highlights
Wire ropes are widely used in mining, metallurgy, transportation, construction, and other similar industries due to the following advantages: high strength, flexibility, elasticity, and diversity of structure. e safe operation of wire ropes relates directly to the safety of production and personnel, making detection of damage to wire ropes of critical importance [1]
Our design for a nondestructive wire rope testing system makes use of unsaturated magnetic excitation, data denoising with variational mode decomposition (VMD) and wavelets, and k-nearest neighbor (KNN) to identify broken wires in the tested ropes. eoretical analysis and experiments have both verified the validity of our nonsaturated magnetic testing system
We found that when using the same aperture acquisition board for data collection, different wire ropes with different structures would result in different lift-off behaviors, causing the data amplitude of the wire rope magnetic flux leakage (MFL) with different structures at the same number of broken wires to differ significantly. ese differences affected the subsequent quantitative identification. erefore, a fixed lift-off improves the accuracy of the detection system
Summary
Wire ropes are widely used in mining, metallurgy, transportation, construction, and other similar industries due to the following advantages: high strength, flexibility, elasticity, and diversity of structure. e safe operation of wire ropes relates directly to the safety of production and personnel, making detection of damage to wire ropes of critical importance [1]. Hong et al [14] analyzed the influences of defect depth and sensor lift-off on MFL testing through experiment and theory and designed a planar Hall magnetoresistive sensor, which had the advantages of a high SNR, small temperature drift, a large bipolar response range, and capable of detection with ultralow magnetization. Zhang et al [19] transformed MFL data into grayscale images, extracted image features, used a back-propagation (BP) neural network to classify defects, and performed quantitative recognition of defects They designed an RBF classification network to identify the number of broken wires in a wire rope [16]. Both these methods use neural networks for classification, which offers good generalization. To solve the existing problems in current defect evaluation methods, we use a KNN to classify broken wires quickly and accurately
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