## 1 Introduction
[22] presents analytical and experimental evidence that such a relationship may exist within fatigue crack AE signals. The general concept is that when energy is released from the fatigue crack, it resonates with the crack forming a standing wave pattern. The wave pattern contains frequency information related to the crack's resonant frequencies. Utilizing this understanding, we propose the potential to develop an artificial intelligence-related signal analysis system that could discern the AE wave information and predict the length of the crack from which it originates. The development of such capability would yield significant benefits for various industries, offering the potential of a paradigm shift from scheduled maintenance to on-demand maintenance of monitored engineering structures. Artificial intelligence proficiencies have been investigated and used in similar manners in recent years. [PERSON] et al. [23] and [PERSON] et al. [24] used machine learning clustering techniques to characterize different fracture mechanisms in fatigue-loaded wind turbine blades and to perform damage mode correlation in adhesive composite joints subjected to tensile AE tests, respectively. [PERSON] et al. [25] tackled the minimally addressed issue of complex and varying environmental and operational conditions, which affect the guided waves in aerospace composites by utilizing Gaussian mixture models. [PERSON] et al. [26] and [PERSON] et al. [27] utilized signal clustering and pattern recognition machine learning algorithms to group AE signals into desired categories. [PERSON] et al. [28] and [PERSON] et al. [29] showed the early application of artificial neural networks (ANN) to structural damage detection using multilayer perception models (MLP) by applying a Bayesian probabilistic approach to help design an appropriate ANN [28] and to use ANNs for the estimation of the damage percentage of joints for truss bridge structures [29]. [PERSON] et al. [30] used deep learning convolutional neural networks to build an automated fatigue damage characterization model for composite structures. [PERSON] and [PERSON] [31] applied the machine learning to predict the structural performance and fracture of additively manufactured components. The applications of AI in the fracture prediction of various materials can be found in [32; 33; 34]. The exact quantification of the crack length is very important for scheduling the maintenance of the structure in which the crack growth is happening. In the work presented herein, the objective was to develop an automated system capable of discerning the differences between AE waveforms originating from different crack lengths. The goal is to estimate the length of a fatigue crack in sheet-metal structures from individual AE signals without recourse to the AE-signal history or AE-signal amplitude. To do this, the robust capabilities of a convolutional neural network for image recognition were sought out and utilized. The waveforms of AE signals were processed by means of a wavelet transform, which would yield data that would be in a format consumable for an image recognition artificial neural network. An automated system with such capability would greatly advance the future of fatigue crack SHM by drastically improving the efficiency of analysis and of returning usable information to the end user. The schematic of the proposed approach is shown in Figure 1.