# Investigation of Acoustic Emission of Cracks in Rails under Loading Close to Operational
###### Abstract  
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. acoustic emission; rail; crack; defect; crack detection +
Footnote †: journal: Medical Imaging  
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Footnote †: journal: Medical Imaging  
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Footnote 1: institutetext: Institute of Information Technologies and Computer Science, National Research University \"Moscow Power Engineering Institute\", Krasnokazarmennaya Str. to defects in rail structure, based on the wavelet decomposition of the AE data. Various modifications of the wavelet transform are used--wavelet packets [11], continuous [2], and discrete wavelet transform. In [3; 12], optimal wavelet bases are constructed based on the AE signal's waveform. After the wavelet decomposition of detected AE hits, criteria based on a comparative analysis of the various wavelet coefficients entropy are applied [13]. Also, classification methods, both statistical and based on the use of neural networks, are effectively used to identify AE hits associated with various types of rail defects. The classification is used to determine hazardous and non-hazardous states of the system based on characteristic AE patterns or to identify useful AE signals against the noise background. Thus, in [14], the degree of danger of the detected defects is determined using the Chebyshev inequality, which makes it possible to establish the correspondence of the AE data to certain characteristic patterns of rail elastic and plastic deformation. In [15], to determine the defectiveness of a rail, Bayesian regression of the frequency parameter characterizing the structural state of the material is used. As a rule, self-organizing networks are used as neural networks, for example. In [16; 17], a convolutional neural network is used to identify signals corresponding to various sources: noise caused by train movement, wheel impacts on the rail, and defects as sources of acoustic emission. In [18], within the framework of deep learning technology, a Long Short Term Memory network is used, which is trained on a representative sample of noises, and then detects signals of a nontypical waveform, which at the next stage of the algorithm, based on a set of specific features, can be classified as AE hits corresponding to defects. Analysis of the publication activity on various topics related to AE diagnostics of rails reveals that most of the papers are devoted to data science issues, namely, data filtering and the identification of useful signals against the noise background. At the same time, the number of studies devoted to the analysis of the laws of acoustic emission during the development of cracks in rail steels and the characteristics of a rail fatigue crack as an AE source is rather limited. Researchers in [5] investigated the effectiveness of the AE method in detecting crack initiation and growth in rail specimens exposed to bending, and the success rate for detecting the onset of crack progression was 100%.