Application of Variational Mode Decomposition and k-Nearest Neighbor Algorithm in the Quantitative Nondestructive Testing of Wire Ropes

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.