Nowadays, the localization and identification of acoustic emission (AE) source is widely utilized to structural health monitoring (SHM) of complex metallic structures. However, traditional AE source localization methods are generally difficult to localize and characterize AE sources in plate-like structure that has complex geometric features. To alleviate the problem, a novel AE source localization method based on all-phase fast Fourier transform and multiple cross-correlation analysis is proposed in this article. Moreover, least squares and Geiger iteration algorithm are applied to determine the coordinates of AE sources. In addition, an improved Bayesian information criterion (BIC) version named autoregressive BIC (i.e., AR-BIC) is presented to increase the accuracy of source localization. To validate the performance of the proposed approach, the classical pencil lead break tests are carried out on a 316 L stainless steel with 10 laser cladding layers. Experimental waveforms are generated from AE sources near laser cladding layers, the surface of the structure, and on its edges. Additionally, to evaluate the performance of the proposed approach in three-dimensional AE source localization, an industrial storage tank is used to acquire three-dimensional AE sources through manually striking. Finally, to further verify the effectiveness of the proposed approach, comparisons with conventional AE source location methods (i.e., PAC or SAMOS AE acquisition system, Newton’s method, and multiple cross-correlation based on Geiger algorithm) and two representative approaches (i.e., deep learning and Bayesian methodology) for localizing AE sources generated by complex metallic structures are conducted. The comparative results demonstrate the effectiveness and superiority of the proposed method in AE-based SHM of complex metallic structures.

如今，声发射（AE）源的定位和识别被广泛用于复杂金属结构的结构健康监测（SHM）。然而，传统的声发射源定位方法通常难以定位和表征具有复杂几何特征的板状结构中的声发射源。为了缓解这一问题，本文提出了一种基于全相位快速傅里叶变换和多重互相关分析的声发射源定位新方法。此外，应用最小二乘法和盖革迭代算法来确定AE源的坐标。此外，提出了一种改进的贝叶斯信息准则（BIC）版本，称为自回归 BIC（即 AR-BIC），以提高源定位的准确性。为了验证所提出方法的性能，经典的铅笔芯断裂测试是在具有 10 层激光熔覆层的 316 L 不锈钢上进行的。实验波形是从靠近激光熔覆层、结构表面及其边缘的 AE 源生成的。此外，为了评估所提出的方法在三维 AE 源定位中的性能，使用工业储罐通过手动打击获取三维 AE 源。最后，为了进一步验证所提出方法的有效性，与传统AE源定位方法（即PAC或SAMOS AE采集系统、牛顿法和基于盖革算法的多重互相关）和两种代表性方法（即，深度学习和贝叶斯方法）用于定位由复杂金属结构产生的 AE 源。比较结果证明了该方法在基于 AE 的复杂金属结构 SHM 中的有效性和优越性。