# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 6 Summary and Future Trends
Given the complexity of the phenomenon, fault estimation is often carried out starting from incomplete monitoring due, for example, to anomalies found in the acquisition devices or due to an interruption in data transmission, without forgetting the background noise that is always present in the workplaces. A monitoring affected by these problems will return unreliable diagnoses. The missing data, therefore, become a crucial element for the success of the procedure and must, therefore, be adequately treated through the different approaches available. Possible preventive actions to deal with this problem include deleting incomplete data or estimating missing data [110]. Some ML-based algorithms can automatically manage incomplete data through the calculation of probabilities for estimating the degree of uncertainty or using expectation-maximization (EM) technology for parameter estimation. Additionally, most ML-based methods take a supervised approach by leveraging the associated labels from the expert to make the diagnosis. In this way, however, the model only learns the type of defect labeled, whereas a new defect is rejected due to the distance between the labeled and unlabeled data, showing an inability of the model to adapt to the evolution of the system. Another important factor limiting the performance of these models is the unbalanced distribution of the data: the data collected that identify the operating conditions with the health of the structure are usually more represented than those that instead identify the damage. Working with unbalanced data involves the elaboration of a model with reduced accuracy of the diagnosis, strongly shifted toward the most observed class. The adoption of ensemble methods and resampling techniques can reduce the effect of this imbalance in the performance of the model. In the previous sections, we have analyzed different applications of artificial intelligence techniques for the processing of AE signals: ML-based algorithms can be used to predict and locate structural defects in the absence of explicit analytic functions. It has been shown that the application of ML to the position of the source AE compensates for the effects of acoustic anisotropy, boundary reflections, and obstacles in the propagation path thanks to its ability to manage complex problems. The training of these algorithms is linked to the configuration of the monitoring system and the geometric and physical characteristics of the target structures. Most of the experimental AE test database is required to correctly interpret the signals and establish a well-trained algorithm for the location of the AE source. Obtaining such a database through experiments requires a lot of work, time, and money. To minimize the required experiments, an alternative way is to use simulation to study the mechanism underlying the detection of AE. Most existing studies using the wave propagation simulation model focus on flat plates and simple geometries. Referring to realistic structures, the analysis with simulated data is mainly used to identify the regions of possible damage sites that can, therefore, be considered areas of primary interest for structural monitoring. A very important aspect of the AE technique is the ability to identify the modality of fissure starting from recorded parameters. By determining the mechanism underlying the cracking event, the classification of the cracking mode plays an important role in understanding and predicting the likely failure modes of the complete structure. Furthermore, it can help detect the progress of the damage and provide guidelines for the correct maintenance process to improve safety and structural durability. Unsupervised ML methods have been successfully used for the grouping of AE signals. The results obtained depend on the structure of the dataset collected with AE sensors. The techniques based on k-means have proved to be simple, fast, but above all effective when applied to structures on contained datasets.