# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 3 Machine-Learning-Based Methodology for AE Testing
### Artificial Neural Network (ANN) Techniques  
ANNs are composed of artificial neurons that are organized in an interconnected structure that allows the connection of the inputs and outputs of various neurons [52]. This type of structure allows neurons to receive both initial and processed data from other neurons, depending on the level of the neuron. The ANN architecture arranges neurons on different levels, with several levels and a number of neurons for each level characterizing their structure [53]. ANNs are composed of levels containing a certain number of nodes: each node connects to another in which a weight and a threshold are associated with it. If the output of any single node is above the specified threshold, that node is activated and sends the data to the next network layer. Otherwise, no data are passed to the next network layer. From a mathematical point of view underlying ANNs, we can express a function \(f\) as a composition of other g functions, which, in turn, can be expressed in simpler functions. An ANN is an interconnected set of elementary functions in which the outputs are the inputs of the subsequent functions. In general, ANNs rely on training data to learn how to improve their accuracy. Once optimized, these learning algorithms are powerful tools in computer science and artificial intelligence. At the base of the ANN, there is the perceptron, in complete analogy with the neuron in a biological neural network. In Figure 4, each dot is a node representing a perceptron. These are functions that take n input elements and return only a single output, which is sent as input for subsequent perceptrons [54]. An ANN is an adaptive system capable of modifying its structure based on both external data and internal information that connects and passes through the ANN during the learning phase. A biological ANN receives external data and signals; these are processed into information through an impressive number of interconnected neurons in a nonlinear and variable structure in response to those data and external stimuli themselves. Similarly, ANNs are nonlinear structures of statistical data organized as modeling tools: they receive external signals on a layer of nodes. Each of these input nodes is connected to various internal nodes of the network, which, typically, are organized at multiple levels so that each single node can process the received signals by transmitting the result of its processing to subsequent levels [55]. Generally, ANNs consist of three layers (Figure 4):* Input layer: It is the one that has the task of receiving and processing the input signals, adapting them to the demands of the neurons in the network. * Hidden layer: In this layer, the data processing takes place. * Output layer: The results of the processing of the H layer are collected here and adapted to the requests of the next level block of the neural network. ANNs have been widely used to classify the acoustic emission (AE) signal. [PERSON] et al. [56] have developed a procedure for the identification of damage for composite materials based on the grouping of acoustic emission signals using artificial neural networks. The authors adopted an unsupervised methodology based on the Kohonen self-organization map. The methodology was tested on a cross-layered fiberglass-polyester laminate subjected to a tensile test. [PERSON] et al. [57] have developed an acoustic emission localization system based on the use of ANNs. The acoustic sources were applied to the test object to obtain data to be sent as input to an ANN. The method was tested on a type III carbon-fiber-reinforced polymer pressure vessel with metal coating. [PERSON] et al. [58] investigated the recognition of single-source one-off partial discharge forms that occur in isolation systems of power transformers. The system developed by the authors uses unidirectional artificial neural networks for the recognition of the acoustic emissions generated by the paper-oil isolation altered by the aging processes. [PERSON] et al.