Structural damage monitoring for metallic panels based on acoustic emission and adaptive improvement variational mode decomposition–wavelet packet transform

Abstract

The metallic panel acoustic emission signal with strong non-stationary properties is normally composed of multiple components (e.g. impulses, background noise, and other external signal), where impulses relevant to metallic panel are easily contaminated by background noise and other external signal, making it difficult to excavate the inherent acoustic emission signal features. To address this issue and achieve the damage monitoring of metallic panels based on acoustic emission technology, a new scheme based on adaptive improvement variational mode decomposition–wavelet packet transform is developed for extracting acoustic emission signal features of metallic panels. Specifically, three different dimensions of Q235B steel plates are utilized to collect acoustic emission signal during three-point bending experiments, to evaluate the effectiveness of the proposed approach and to investigate the influence of size effect on the acoustic emission signal characteristics. In addition, the transient process and centroid frequency distribution of each damage stage are investigated, and the internal structure variations in the bending damage process are detected by scanning electron microscopy inspection. Moreover, the generalization of the proposed damage monitoring method is evaluated for plate-like structures that have complex geometric features, such as welds. The results demonstrate the effectiveness of the proposed method for acoustic emission–based structural health monitoring of metallic plate-like structures.