Quantitative Identification of Internal and External Wire Rope Damage Based on VMD-AWT Noise Reduction and PSO-SVM

Abstract

As a common load-bearing component, mining wire rope produces different types of damage during a long period of operation, especially in the case of damage inside the wire rope, which cannot be identified by the naked eye, and it is difficult to accurately detect such damage using the present technology. In this study we designed a non-destructive testing device based on leakage magnetism, which can effectively detect the internal defects of wire rope damage, and carried out simulation analysis to lay a theoretical foundation for the subsequent experiments. To address the noise reduction problem in the design process, a variational mode decomposition–adaptive wavelet thresholding noise reduction method is proposed, which can improve the signal-to-noise ratio and also calculate the wavelet energy entropy in the reconstructed signal to construct multi-dimensional feature vectors. For the quantitative identification of system damage, a particle swarm optimization–support vector machine algorithm is proposed. Moreover, based on the signal following the noise reduction step, seven different feature vectors, namely, the waveform area, peak value, peak-valley value, wavelet energy entropy classification, and identification of internal and external damage defects, have been determined. The results show that the device can be used to effectively identify internal damage defects. In addition, the comparative analysis showed that the algorithm can reduce the system noise and effectively identify internal and external damage defects with a certain superiority.

Noise reduction algorithm of GPR data based on MMSE‐PDS

“…On the other hand, the sub‐signal is reconstructed from the structural level of the signal. Such algorithms mainly include singular value decomposition (SVD) (Yang & Han, 2021), empirical mode decomposition (EMD) (Xie et al., 2016; Zhao & Li, 2022), ensemble EMD (Cheng et al., 2017), variational mode decomposition (Peng et al., 2021; Tian et al., 2022; Zheng et al., 2022) and so forth. Some scholars use this idea of signal decomposition and reconstruction to decompose the signal in the GPR B‐SCAN into strong interference signal component and target disease signal component and then remove the strong interference component from the B‐SCAN to achieve clutter suppression.…”