## 4 Discussion  
The majority of studies devoted to the issues of AE data analysis and processing are vulnerable to criticism since, due to the features of the method related to its structural sensitivity and remoteness, there are numerous factors influencing the nature of the diagnostic data. A significant difficulty is also that most of these factors are random in nature or highly variable, so their influence is difficult to take into account or predict. Therefore, when attempting to universalize data processing algorithms or generalize the results to a certain class of objects, problems related to the influence of unaccounted factors arise. Studies based on the results of mechanical tests do not consider the factors related to the attenuation and the features of dispersive propagation of AE waves. Studies devoted to the analysis of data obtained in the field are focused on defects with certain parameters and do not take into account the influence of the size and topography of defects or the influence of various loading modes. The current research proposes an approach that does not offer a radical solution to the problem but allows for some progress in this direction due to the procedure of complex modeling of various AE testing factors. A method for classifying AE data is proposed, which, with some assumptions, allows for taking into account the AE source and acoustic tract parameters, as well as the nature of the technological noise of the equipment under control. The paper considers the defects of a dissimilar welded joint, specifically the carbon and decarburized interlayers that form during the long-term operation of the object under high-temperature conditions, as a source of AE. The presence of diffusion interlayers leads to a decrease in the elastic limit and ultimate tensile strength of the weld joint [1]. Microstructure defect detection is a unique diagnostic feature of the AE method, which distinguishes it from a number of traditional nondestructive testing methods. The detection of microstructure defects is not a typical task, but it is a more favorable case in terms of determining the parameters of the AE source. In contrast, to crack propagation in a jump-like manner, the deformation of diffusion interlayers occurs continuously and leads to continuous generation of AE hits with an approximately constant level of activity and amplitudes belonging to a fairly narrow range of values. In addition, diffusion interlayers are a defect, the location of which is known as a priori; therefore, when determining the AE signal waveform, the uncertainty associated with the unknown length of the acoustic waveguide is significantly reduced. In the paper, the acoustic waveguide parameters are taken into account using the FEM model and the AE signal propagation along a single-layer cylindrical waveguide is considered; such a model corresponds to the case of an uninsulated pipeline filled with a gaseous medium. The modeling took into account various possible source locations in the azimuthal direction along the line and in-depth, as well as various positions of the AE sensors. Analysis of the  
Figure 21: Confusion matrixes for impulse noise with level 50 dB (**a**), for stationary noise with level 50 dB (**b**), for impulse noise with level 55 dB (**c**), for stationary noise with level 55 dB (**d**). data obtained during the modeling showed an insignificant variability in the AE signal waveform for different positions of the AE source and the AE sensor compared to the variation of the noise parameters, which is a favorable factor in identifying useful signals against a noise background. However, it should be noted that the modeling did not take into consideration the influence of random factors associated with possible changes in the acoustic parameters of the material, the presence of a liquid medium inside a pipe and various wall thicknesses. Long-term realization of technological noise measured in the factory conditions was considered as noise.