Wood hole quantity feature extraction and identification based on VMD-SVD of stress wave and mahalanobis distance

Abstract

Aiming at the problem of wood hole defects, this paper proposed a method to identify the number of holes based on SVD. First, four holes with a diameters of 5 mm, 6 mm and 8 mm were artificially created on the specimen, and a wave source was generated on the surface of one side of the holes by pencil-lead break (PLB) tests, and a sensor was placed on the other side. Then 15 groups of signals were randomly selected for each hole case. 6-layer VMD decomposition was performed into a series of IMFs by using VMD, where the number of decomposition layers was determined based on the index of energy conservation and the index of orthogonality. SVD was performed on the matrix composed of the IMF signals to obtain the corresponding singular value row vectors, and composed as the corresponding standardized feature matrix. Finally, for the actual measured signals, the Mahalanobis distances between the eigenvectors and each standardized feature matrices were calculated separately, and the number of holes was determined based on the minimum distance. The results show that the standardized feature matrix calculated are significantly different for different numbers of holes, and the accuracy rate of identifying by calculating the Mahalanobis distance is 92 %.