# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 3 Sound Source Localization Methods for Two-Dimensional (2D) Structures
### Modal Acoustic Emission (MAE)  
Modal acoustic emission is a waveform analysis method. Common waveform analysis methods include Fourier transform, wavelet analysis, neural networks and full waveform analysis. In 1991, the American scholar [PERSON] published a study on plate wave acoustic emission (PWAE), which deepened researchers' understanding of Lamb waves and applied this theory more to acoustic emission monitoring. After PWAE, it is also known as modal acoustic emission (MAE). The MAE theory combines the physical mechanism of the acoustic emission source with the plate wave theory. The results show that this method is suitable for monitoring acoustic emission signals of thin sheet metal materials and thin-walled long pipe corrosion. Due to the typical characteristics of extended waves and bending waves, the waveform features are significantly different from noise, making it easy to identify the waveform of the corrosion signal. The advantage of modal acoustic emission is that it requires fewer sensors and can be located using a single sensor. The limitations are that (1) it is necessary to identify the arrival time of the stretching wave and bending wave modes (which is not easy), as well as (2) separation of wave reflection and multiple AE events. [PERSON] et al. [42] introduced a single-sensor sound source localization technique, which firstly predicted the dispersion characteristics of elastic waves in a thin plate by analyzing the modes of propagating elastic waves. Then, the Gabor wavelets were applied. The results showed that the arrival times of different modes can be determined from the peaks of wavelet transform amplitudes. [PERSON] et al. [43] adopted multimodal edge reflections to identify AE sources by estimating the distance between the AE source and a single transducer using the continuous wavelet transform and dispersion curves of the fundamental Lamb wave modes. This step used the MAE method. Then, an analytical model was proposed to simulate the waves reflected from the edges using the estimated distance. Finally, the  
Figure 8: (**a**) AE source localization method with rhombic wavefront [41]. (**b**) AE source localization method with elliptical wavefront. correlation between the experimental and simulated waveforms was utilized to estimate the location of the AE source. The method was applied to isotropic plates. [PERSON] [44] proposed a method to minimize the effects of geometry, boundaries and edges. The method was based on identifying the front of the time-frequency representation (TFR) plot of the AE signal, i.e., the first few high-energy regions in the TFR, and then aligning these regions according to the contours of the group velocity profile of the Lamb mode. [PERSON] et al. [45] used the MAE method to characterize damage in a carbon fiber composite pressure vessel. [PERSON] et al. [46] proposed a method for continuous leakage source localization in inflatable pipelines and noisy environments using a single transducer and wavelet analysis and modal localization theory. [PERSON] et al. [47] applied an optimized microfiber coupler sensor (MFCS) to acoustic source localization using an MAE method to apply an optimized microfiber coupler sensor (MFCS) to sound source localization. [PERSON] et al. [48] used an MFCS in an improved modal analysis method for source localization. The method employed the least square error to reduce the dependence of the localization results on frequency selection and to automate the localization process for contour threshold modal analysis. [PERSON] et al. [49] studied complex damage mechanisms and challenging identification of damage AE signals in carbon-fiber-reinforced composites (CFRP). They proposed a classification algorithm based on the modal separation method for the identification of CFRP damage signals, which provided a modal characterization of CFRP damage AE signals. [PERSON] et al.