# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 4 Deep-Learning (DL) Methods for AE Testing
### Convolutional Neural Network (CNN) Solutions
However, in most applications, it is not necessary to have a high resolution of the image, so it is possible to select only the useful information by reducing the size of the feature maps for subsequent processing. In fact, the pooling level has the purpose of resizing the feature maps while leaving the features of interest unchanged [72]. There are different pooling mechanisms, and the most used is max pooling, which consists of applying a filter usually of \(2\times 2\) size, which moves on the feature map with a step of the same length. The pooling filter identifies the receptive fields and finds the maximum value for each [73]. Finally, there are the completely connected levels that carry out the classification and then generate the output of the neural network. This layer of levels receives as input the matrix manipulated by the previous levels and produces a vector of dimension N that corresponds to the number of classes to be predicted. By analyzing the correlations present in the matrices, the relative probability of belonging to each class is calculated [74; 75]. [PERSON] et al. [76] applied ANN-based AE testing for quality monitoring of additive manufacturing of 3D printers. The acoustic emission signals were collected by a fiber Bragg grating sensor during the additive manufacturing process of the powder bed in a selective laser-melting machine. The relative energies of the narrow frequency bands of the wavelet packet transform were extracted and sent to a classifier based on the spectral convolutional neural network. [PERSON] et al. [77] simulated the effects of seismic events on a physical scale model of a pile-dwelling foundation using a vibrating table. The AE signals were collected with the use of accelerometers and sent to a CNN for the classification of the damage suffered by the structure. The authors demonstrated that the robustness of the method  
Figure 5: Typical CNN architecture: structure is divided into two sections, the first in which features composed of convolutional layers, ReLU, and Max pooling are extracted. Data classification section is instead made up of fully connected network. depends on the quality of the data: preparing the data with accurate labeling is crucial; it is necessary to consider a variety of different types of damage-induced AE signals that produce them in reinforced concrete structures, the model must be trained with various types of ambient noise, and, finally, the AE signals quickly attenuate with distance. [PERSON] et al. [78] collected AE signals from an excited aluminum plate with pencil-lead break and two steel balls of different diameters using piezoelectric sensors. The signals in the time and time-frequency domains were extracted and sent to an ANN and a CNN-1D. Subsequently, the RGB images of the wavelet transforms were extracted and sent to a CNN-2D. The best classification results were obtained from the Conv2D architecture, which uses the deep transfer learning method, and the VGG16 architecture. Furthermore, deep transfer learning significantly reduces the number of parameters required for training the model. [PERSON] et al. [79] monitored and classified the multivariant wear behavior of plain bearings. The AE signals were collected using a Nano30 sensor with a good frequency response (150-750 kHz), and subsequently, the continuous wavelet transform (CWT) was evaluated. The extracted features were sent to a 22-layer deep convolutional neural network (CNN) (GoogLeNet) for classification. Plain bearings are subject to multiple wear mechanisms, and the AE can detect critical operating conditions of wear: the spectra of the corresponding AE signals were in the frequency range from 40 kHz up to 700 kHz. [PERSON] et al. [80] used CNNs to identify the area where an AE source is generated. The authors validated the methodology by exploiting a metal plate stressed with Hsu-Nielsen sources [81]. [PERSON] et al. [82] extracted synchrosqueezed wavelet transforms from AE signals collected in railway crack monitoring.