A bispectrum feature extraction enhanced structure damage detection approach

Abstract

The subject of structure defect diagnosis has been extensively investigated in the field of nondestructive testing (NDT). In this paper, a new approach for detecting structure damage is proposed, which is based on the combination of the bispectrum feature extraction method and the learning vector quantization (LVQ) identification method. Because bispectrum analysis possesses the capability of restraining Gaussian noise, it may be employed to enhance the performance of the feature extraction method. In simulation, by using the proposed method, it has been shown that very high accuracy of structure damage identification can be obtained compared with the modal assurance criterion (MAC) formed from the modal parameters method, especially in the case of low signal-to-noise ratio environment.