Investigation on Features Extraction of Defect Signals Using Support Vector Machine and Multi-feature Fusion

Abstract

Abstract This article investigated the sensitivity of each acoustic feature to steel plate defects through the coin-tapping method in order to improve acoustic detection methods precision and intelligent detection efficiency. 87 acoustic characteristic parameters of detection signals were researched including time domain, frequency domain, and time-frequency domain. 10 kinds of percussion conditions were designed to meet the two classifications of acoustic signals. The degree of contribution of acoustic parameters to damage detection was arranged by sensibility analysis, which could be used as a basis for selecting acoustic parameters for similar structural damage detection. The characteristic vectors of various parameter combinations were determined as the inputs of the acoustic detection method by analysing the sensitivity and correlation of different parameters. The multi-parameter feature vector with low redundancy was obtained by correlation analysis, which could be used as a combination method of the input value of the damage recognition classifier. The results show that the zero-crossing rate, main frequency and time-frequency domain parameters have high damage sensitivity, and there are always low-correlation feature pairs between them. The research results provide solutions for the selection and combination of feature parameters for similar data classification problems.