Testing the Technique for the Cluster Analysis of Acoustic Emission Pulse Arrays under the Formation of a Conical Glass Granulate Pile

Махутов, Н. А.; Васильев, И. Е.; Иванов, В. И.; Elizarov, S. V.; Chernov, D. V.

doi:10.1134/s0020168517150080

Inorg Mater

2017

DOI: 10.1134/s0020168517150080

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Testing the Technique for the Cluster Analysis of Acoustic Emission Pulse Arrays under the Formation of a Conical Glass Granulate Pile

Н. А. Махутов

И. Е. Васильев

В. И. Иванов

et al.

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The use of acoustic emission in assessing the ductility loss in steel products under the effect of impact

Makhutov,

Vasil’ev,

Chernov

et al. 2023

Zavod. lab., Diagn. mater.

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Acoustic emission (AE) diagnostics was carried out during tensile testing of 20KhN2MA steel samples to study the loss of ductility after the impact. We used V-notched samples (3.3 mm in depth) with overall dimensions of 300 × 20 × 6 mm. The impact in the concentrator zone caused the depletion of the plasticity of the material, which was accompanied by a decrease in the partial fraction of ductile fracture and an increase in brittle fracture. The test samples were divided into six batches. The samples of the first batch were not subjected to the impact. In the second batch, the impact energy was 50 J, in the third — 75 J, in the fourth — 100 J, in the fifth — 125 J and in the sixth — 150 J. The rupture tests were carried out at room temperature and at a speed of the movable traverse of 1 mm/min. The kinetics of damage in the notch zone during loading was monitored using the acoustic emission (AE) method and video recording. Processes of brittle and ductile (caused by cleavage and shear, respectively) destruction of the crystal lattice of a metal differ primarily in the speed and duration of stress waves. To separate AE pulses generated by these processes, spectrograms of time-frequency transformations and waveforms were analyzed. Pulse selection was carried out using a complex parameter reflecting the steepness of the amplitude drop at the phase of signal attenuation. Boundary values were determined that allow separation of the recorded pulses into flows caused by ductile and brittle structural damage to structural steels. It is shown that manifestation of the effect of impact on the exhaustion of the plastic properties of steel 20KhN2MA becomes noticeable when the level of specific work exceeds 50 J/cm2. Moreover, with an increase in the specific work up to 150 J/cm2, the weight content of location pulses characterizing the kinetics of brittle destruction of structural bonds increased by 3 – 4 times, relative to that recorded for the samples without impact. This result correlates with the duration of the rupture test of the samples, which was reduced by three times when the level of the specific work increased to 150 J/cm2

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The use of acoustic emission in assessing the ductility loss in steel products under the effect of impact

Makhutov,

Vasil’ev,

Chernov

et al. 2023

Zavod. lab., Diagn. mater.

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Combined ResNet Attention Multi-Head Net (CRAMNet): A Novel Approach to Fault Diagnosis of Rolling Bearings Using Acoustic Radiation Signals and Advanced Deep Learning Techniques

Xu,

Li,

Ding

2024

Applied Sciences

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The fault diagnosis of rolling bearing acoustic radiation signals holds significant importance in industrial equipment maintenance. It effectively prevents equipment failures and downtime, ensuring the smooth operation of the production process. Compared with traditional vibration signals, acoustic radiation signals have the advantage of non-contact measurement. They can diagnose faults in special conditions where sensors cannot be installed and provide more comprehensive equipment status information. Therefore, to extract the fault characteristic information of rolling bearings from complex acoustic signals, this paper proposes an advanced deep learning model combining Gramian Angular Field (GAF), ResNet1D, ResNet2D, and multi-head attention mechanism, named CRAMNet (Combined ResNet Attention Multi-Head Net), to diagnose the faults of rolling bearing acoustic radiation signals. Firstly, this method includes converting one-dimensional signals into GAF images and performing data standardization and segmentation. Then, the method utilizes ResNet1D to extract features from one-dimensional signals and ResNet2D to extract features from GAF images. Further, it combines the multi-head attention mechanism to enhance feature representation and capture dependencies between different channels. Finally, this paper compares the proposed method with several traditional models (including CNN, LSTM, DenseNet, and CNN-Transformers). Experimental results show that the proposed method performs outstandingly in terms of accuracy and robustness. The combination of residual networks and multi-head attention mechanism in the model significantly enhances its ability to accurately diagnose rolling bearing faults, proving the superiority of the algorithm.

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Testing the Technique for the Cluster Analysis of Acoustic Emission Pulse Arrays under the Formation of a Conical Glass Granulate Pile

Cited by 2 publications

References 3 publications

The use of acoustic emission in assessing the ductility loss in steel products under the effect of impact

The use of acoustic emission in assessing the ductility loss in steel products under the effect of impact

Combined ResNet Attention Multi-Head Net (CRAMNet): A Novel Approach to Fault Diagnosis of Rolling Bearings Using Acoustic Radiation Signals and Advanced Deep Learning Techniques

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