Quantitative inspection for identifying broken coal mine wire rope based on wavelet packet sparse representation

Abstract

The magnetic flux leakage (MFL) signal of steel wire rope is easily affected by background noise, rope strands and so on. A preprocessing method for the damage signal based on wavelet packet sparse representation is proposed. This method is suitable for the damage signal of the wire rope. The original signal is decomposed into three layers of wavelet packets and the wavelet packet coefficients are sparsely represented by the matching pursuit (MP) and orthogonal matching pursuit (OMP) algorithms. The signal-to-noise ratio (SNR) of the reconstructed signal is much higher than that obtained through the wavelet threshold shrinkage method, the median filter method and the singular value difference spectrum method. The proposed method can significantly improve the noise reduction effect of the damage signal. A principal component analysis (PCA)-based particle swarm optimisation support vector machine (PSO-SVM) model for quantitative recognition is proposed. Seven global eigenvalues and wavelet packet energy entropy details of damage signals are extracted as effective eigenvalues. The eight eigenvalues are used as the input for the SVM that is designed and trained. A PSO-SVM classification model based on PCA is proposed. The results show that the recognition rate of the SVM is 94.73%. The quantitative recognition accuracy is improved.