For the effective detection of acoustic emission (AE) impulses against a noisy background, the correct assessment of AE parameters, and an increase in defect location accuracy during data processing are needed. For these goals, it is necessary to consider the waveform of the AE impulse. The results of numerous studies have shown that the waveforms of AE impulses mainly depend on the properties of the waveguide, the path along which the signal propagates from the source to the sensor. In this paper, the analytical method for modeling of AE signals is considered. This model allows one to obtain model signals that have the same spectrum and waveform as real signals. Based on the obtained results, the attenuation parameters of the AE waves for various characteristics of the waveguide are obtained and the probability of defect detection at various distances between the AE source and sensor utilized for evaluation.

Estimation of probability of defect detection (POD) is one of the most important problems in acoustic emission (AE) testing. It is caused by the influence of the material microstructure parameters on the diagnostic data, variability of noises, the ambiguous assessment of the materials emissivity, and other factors, which hamper modeling the AE data, as well as the a priori determination of the diagnostic parameters necessary for calculating POD. In this study, we propose an empirical approach based on the generalization of the experimental AE data acquired under mechanical testing of samples to a priori estimation of the AE signals emitted by the defect. We have studied the samples of common industrial steels 09G2S (similar to steel ANSI A 516-55) and 45 (similar to steel 1045) with fatigue cracks grown in laboratory conditions during cyclic testing. Empirical generalization of data using probabilistic models enables estimating the conditional probability of record emissivity and amplitudes of AE signals. This approach allows to eliminate the existing methodological gap and to build a comprehensive method for assessing the probability of fatigue cracks detection by the AE testing.

This paper presents a study of acoustic emission (AE) during the deformation of dissimilar welded joints of austenitic steel to pearlitic steel. One of the specific problems in these welded joints is the presence of decarburized and carbide diffusion interlayers, which intensively increase in width during long-term high-temperature operation. The presence of wide interlayers negatively affects the mechanical properties of welded joints. Moreover, welded defects are difficult to diagnose in welded joints containing interlayers: due to the high structural heterogeneity, interlayers create structural noises that can hinder the detection of defects such as cracks, pores, or a lack of penetration. The AE method may become a complex decision for diagnosing dissimilar welded joints due to applicability to the testing of heterogenic materials with a complex microstructure. Specimens cut from dissimilar welded joints of austenitic steel to pearlitic steel were tested by tension to rupture, with parallel AE data registration. According to the research results, the characteristic features of the AE were revealed for specimens containing defects in the form of lack of penetration as well as for specimens with diffusion interlayers. The results obtained show that the AE method can be used to test both typical welding defects and diffusion interlayers in welded joints of steels of different structural classes.

Acoustic emission (AE) testing is traditionally carried out on non-operating objects. Such requirement is associated both with the Kaiser effect, leading to the necessity for exceeding the test load above the working one and with a high level of noise during object operation. However, AE testing could be performed under operating conditions, if the AE data acquisition period is increased and a specialized method is developed, which should take account of the effect of various noise, features of the object loading under operating conditions, the effect of damaging factors and possible destruction mechanisms. This paper investigates the possibility to carry out structural health monitoring (SHM) of hydrotreaters, highway bridges, high-temperature pipelines, gas adsorbers, roller bearings of rotary kilns and draglines on the basis of AE method. Architecture of SHM-system and specific data analysis procedures are proposed.

In this study, we investigated defect detection in dissimilar welded joints by the acoustic emission (AE) method. The study objects were carbide and decarburized interlayers, which are formed at the fusion boundary between austenitic and pearlitic steels. Diffusion interlayers, as a structural defect, usually have microscopic dimensions and cannot be detected using conventional non-destructive testing (NDT) methods. In this regard, the AE method is a promising approach to diagnose metal objects with a complex structure and to detect microscopic defects. In this paper, the AE signatures obtained from testing defect-free specimens and specimens with diffusion interlayers are analyzed. We found that the AE signature for defective and defect-free welded joints has significant differences, which makes it possible to identify descriptors corresponding to the presence of diffusion interlayers in dissimilar welded joints.

This article is devoted to materials testing by the acoustic emission (AE) method, which is the analysis of models and diagnostic parameters to assess the probability of detection of a defect in steel structures. The paper proposes to evaluate the emissivity of the material quantitatively by the number and dynamics of the accumulation of acoustic emission impulses. Experimental studies were carried out on pearlitic structural steels, including the loading of samples with fatigue cracks. It was established that the number of AE impulses emitted during loading of an object with a fatigue crack is a random variable corresponding to the normal distribution law. The results show that an estimate of the number of AE impulses emitted during the loading of samples with fatigue cracks can be obtained by distributing the multiplicative parameter D of the Palmer-Heald model by taking into account the maximum value of the applied load.