## 1 Introduction
The proposed approach seems to be more reliable since the defect detection procedure is not empirical but is based on the calculation of realistic models of AE signals and the development of methods for their detection against the background of noise obtained in field conditions [16]. Realistic signal models are obtained by considering the AE activity and the AE hits amplitude values obtained during the experiment on cyclic tension of specimens with dissimilar welded joints and also by determining the signal waveform using numerical modeling of signal propagation along the acoustic waveguide. Such a hybrid modeling method is justified since, during mechanical testing of specimens, the AE source parameters are determined quite accurately, but the signal waveform is distorted due to an increase in duration and rise time caused by multiple reflections of the AE waves from the specimen edges [17]. Numerical, analytical and hybrid modeling are widely used to calculate the propagation of AE signals along waveguides of various shapes to determine the signal onset time [18], refine the location procedure [19], and implement waveguide testing methods [20]. A comparison of modeling and experimental signals shows high accuracy in the modeling results. The use of an acoustic waveguide numerical model allows us to consider the size and shape of the testing structure and welded joint and to form a set of AE signals for different source parameters and mutual arrangements of the AE source and AE sensor. Neural networks are used to identify signals obtained from simulations against noise background measured in the field conditions. The advantage of neural networks in AE data processing compared to statistical classifiers and machine learning methods is the formalization of the pre-processing stage. The use of statistical classifiers usually requirespre-processing data, searching for patterns and identifying informative features associated with certain physical processes [21]. AE data, both useful signals and noises, are random in nature and are characterized by significant variability [22], so the search for informative parameters is a complex and labor-intensive task that does not always lead to a reliable result. Due to the more complex nonlinear structure of the classifier, the use of a neural network allows for simplifying the process of preliminary processing by shifting the search for patterns and data generalization from the pre-processing stage to the classification stage. With the help of neural networks, it is possible to use an excessive number of features to apply standard AE parameters or the actual time realizations and AE signal spectra as informative features [23]. Thus, in this paper, we propose an approach to AE data neural network classification adapted for use in AE monitoring systems, providing greater flexibility and the possibility of universalization of approaches compared to existing methods. The universality of the proposed method is achieved through the hybrid formation of a training data sample: the values of AE hits amplitudes, and activity are formed because of a physical experiment, the acoustic waveguide of the test object is considered using numerical modeling, and when forming a sample of noise realization of technological interference, data obtained in field conditions are used.