The article is devoted to the organization of the structural health monitoring of a walking dragline excavator using the acoustic emission (AE) method. Since the dragline excavator under study is a large and noisy industrial facility, preliminary prospecting researches were carried out to conduct effective control by the AE method, including the study of AE sources, AE waveguide, and noise parameters analysis. In addition, AE filtering methods were improved. It is shown that application of the developed filtering algorithms allows to detect AE impulses from cracks and defects against a background noise exceeding the useful signal in amplitude and intensity. Using the proposed solutions in the monitoring of a real dragline excavator during its operation made it possible to identify a crack in one of its elements (weld joint in a dragline back leg).

The paper is devoted to the study of the possibility of detecting cracks in railway rails by the acoustic emission (AE) method. An experimental study of AE signals under cyclic compression loading of rail fragments, which simulates the rail operating load, has been carried out. Fragments of rails without defects, as well as fragments containing pre-grown fatigue cracks, were studied. It was found that AE signals generated by a rail with a crack have higher activity compared to signals from defect-free specimens. It is shown that the AE signals during the loading of defect-free specimens have a short duration and low amplitude and may be caused by the deformation of non-metallic inclusions. The crack presence leads to an increase in the AE hits rate and changes the nature of the distribution of the AE hits amplitudes. It is shown that the crack location has no effect on the reliability of its detection by the AE method. Criteria of crack detection by AE testing are offered as a result of this study.

The paper considers the neural network application to detect microstructure defects in dissimilar welded joints using the acoustic emission (AE) method. The peculiarity of the proposed approach is that defect detection is carried out taking into account a priori information about the properties of the AE source and the acoustic waveguide parameters of the testing structure. Industrial process pipelines with dissimilar welded joints were studied as the testing object, and diffusion interlayers formed in fusion zones of welded joints were considered microstructure defects. The simulation of AE signals was carried out using a hybrid method: the signal waveform was determined based on a finite element model, while the amplitudes of AE hits were determined based on a physical experiment on mechanical testing of dissimilar welded joints. Measurement data from industrial process pipelines were used as noise realizations. As a result, a data sample was formed that considered the parameters of the AE source and the parameters of the acoustic waveguide with realistic noise parameters and a signal-to-noise ratio. The proposed method allows for a more accurate determination of the waveform, spectrum, and amplitude parameters of the AE signal. Greater certainty in the useful signal parameters allows for achieving a more accurate and reliable classification result. When using a backpropagation neural network, a percentage of correct classification of more than 90% was obtained for a data set in which the signal-to-noise ratio was less than (−5 dB) in 90% of cases.

The paper studies the possibility of carbide and decarburized ferrite interlayers detection formed during the production and operation of dissimilar welded joints of austenitic to pearlitic steels. Diffusion interlayers may be interpreted as welded joint microstructure defects, since their formation can cause premature failure of the product under high-temperature operation conditions. In addition, with a certain chemical and structural-phase composition of the welded metals, brittle interlayers formation and development of cracks in the weld zone may be occurred. To identify diffusion interlayers, the acoustic emission (AE) method is used in this research. Specimens cut from welded joints were tested by tension until rupture with simultaneous recording of AE signals. Based on the research results, particular AE data signatures corresponding to the specimens with diffusion interlayers were identified. The main feature indicating the presence of diffusion interlayers is an increase in the amplitudes of AE impulses and AE activity at a stress value of 300 MPa, corresponding to the ultimate strength of the ferrite phase.