## 1 Introduction  
One of the most difficult tasks concerning diagnostics of industrial facilities is the assessment of the metal state. In addition to the mechanical properties and microstructure parameters estimation, the main role is assigned to the study of defects' presence and reasons for construction damage under the influence of operational loads. The timely and reliable detection of dangerous defects is likely to increase the safe operation of industrial facilities and reduce the risk of accidents. Currently, various non-destructive test (NDT) methods are carried out to detect metal damage as periodic diagnostics (for example, ultrasonic testing, radiation flaw detection, etc.) and as continuous monitoring (for example, acoustic testing, vibration diagnostic systems, etc.). When it comes to monitoring systems, one of the most promising methods is the acoustic emission (AE) method. Namely this technique allows defining the location and degree of damage in a real-time mode [1; 2; 3; 4]. The AE method is based on the phenomenon of elastic wave generation during the formation and development of defects in the material [5]. To conduct AE monitoring, multichannel systems for collecting, processing, and storing diagnostic signals recorded using piezoelectric transducers are used. As a result of the AE signals processing, the construction of a coordinate location and the assessment of the AE sources danger degree are performed. Finally, it is possible to draw a conclusion about the metal state [6; 7; 8]. When processing AE data, it is necessary to note many factors that have a significant impact on the method's accuracy. At the same time, in the regulatory documents for AE diagnostics (ASTM E1118/E1118M-16, ASTM E1139/E1139M-17, ISO 22096:2007, etc.) some critical factors do not mention. One of them is the scale factor, which, generally speaking, consists of the dependence of test results on the specimen dimensions. The scale factor also manifests itself in acoustic emission diagnostics. According to the results presented in [9; 10; 11; 12; 13; 14], the AE parameters recorded during specimen loading largely depend on the scale factor. Specimens were made of rocks and complex structural materials, such as concrete and composite. During AE monitoring of specimens of various sizes, the authors notice a change in the amplitude, activity, and spectral characteristics of the recorded AE signals. Under damage accumulation in small specimens, a stream of low-amplitude acoustic signals of high activity was recorded. For larger ones, there was a characteristic decrease in activity and an increase in the AE signals amplitude, registered upon reaching the limit loads. The danger degree of developing damage can be assessed using the _\"b-value\"_ parameter, based on the calculation of the distribution function slope of the AE signals amplitudes. The main feature of this technique is the possibility of carrying out a numerical assessment of the damage evolution in the material in real-time using the statistical and fractal analysis methods [15; 16; 17; 18; 19; 20]. To determine the damage degree in rocks, the energy and frequency characteristics of the AE data are also compared. In accordance with the analysis results given in [21], crack growth leads to a decrease in the maximum of the frequency spectrum and an increase in the energy of AE signals. The main reason for the scale factor's influence on the results of AE diagnostics is the different volumes of the tested material. When testing several constructions of similar shapes and different linear dimensions, the volume of the material under control and the AE signals propagation will be different. Thus, the number and parameters of the registered AE signals will also be various. A change in the AE data characteristics has a significant effect on the critical values of the standard criteria, which makes it difficult to carry out the appropriate AE diagnostics of damages in several typical objects with different linear dimensions.