Internal defect inspection in magnetic tile by using acoustic resonance technology

This paper focuses on the validity of a nondestructive methodology for magnetic tile internal defect inspection based on acoustic resonance. The principle of this methodology is to analyze the acoustic signal collected from the collision of magnetic tile with a metal block. To accomplish the detection process, the separating part of the detection system is designed and discussed in detail in this paper. A simplified mathematical model is constructed to analyze the characteristics of the impact of magnetic tile with a metal block. The results demonstrate that calculating the power spectrum density (PSD) can diagnose the internal defect of magnetic tile. Two different data-driven multivariate algorithms are adopted to obtain the feature set, namely principal component analysis (PCA) and hierarchical nonlinear principal component analysis (h-NLPCA). Three different classifiers are then performed to deal with magnetic tile classification problem based on features extracted by PCA or h-NLPCA. The classifiers adopted in this paper are fuzzy neural networks (FNN), variable predictive model based class discrimination (VPMCD) method and support vector machine (SVM). Experimental results show that all six methods are successful in identifying the magnetic tile internal defect. In this paper, the effect of environmental noise is also considered, and the classification results show that all the methods have high immunity to background noise, especially PCA-SVM and h-NLPCA-SVM. Considering the accuracy rate, computation cost problem and the ease of implementation, PCA-SVM turns out to be the best method for this purpose.