scholarly journals Assessment of Polymer Concrete Sample Geometry Effect on Ultrasonic Wave Velocity and Spectral Characteristics

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7200
Author(s):  
Kamil Zalegowski
Keyword(s):  
Wave Propagation ◽  
Ultrasonic Wave ◽  
Path Length ◽  
Concrete Sample ◽  
Particulate Composites ◽  
Polymer Concrete ◽  
Optimization Approach ◽  
Frequency Spectra ◽  
Time Frequency ◽  
Sample Geometry

In this paper an analysis of the influence of polymer concrete sample shape and dimensions on ultrasonic wave propagation is carried out. Compositions of tested fly ash polymer concretes were determined using a material optimization approach. The tests were carried out on the samples of three shapes: cubes, beams, and plates. The ultrasonic testing was done by a direct method (transmission method) using a digital ultrasonic flow detector and piezoelectric transducers of 100 kHz central frequency. Propagation of the ultrasonic wave was characterized by pulse velocity. Frequency spectra and time-frequency spectrograms obtained using Fourier transform and Fourier-based synchrosqueezing transform were also presented. The correlation analysis showed that neither the path length nor the lateral dimension to the direction of wave propagation are not statistically significant for the UPV variability. However, a general trend of decrease in the UPV with increasing the path length was noticed. The analysis of the signal in time-frequency domain seemed to be useful in the analysis of particulate composites properties, especially when UPV changes are not clear enough, since it revealed greater differences in relation to changes in sample geometry than frequency spectra analysis.

scholarly journals Deep Learning Approach for Vibration Signals Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3929
Author(s):  
Han-Yun Chen ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Parameters Optimization ◽  
Frequency Spectra ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Time Frequency ◽  
And Performance ◽  
The One ◽  
Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

Simulation study of axial ultrasonic wave propagation in heterogeneous bovine cortical bone

2016 ◽  
Vol 140 (5) ◽  
pp. 3710-3717 ◽  
Author(s):  
Toshiho Hata ◽  
Yoshiki Nagatani ◽  
Koki Takano ◽  
Mami Matsukawa
Keyword(s):  
Wave Propagation ◽  
Cortical Bone ◽  
Ultrasonic Wave ◽  
Simulation Study ◽  
Ultrasonic Wave Propagation

Novel Method for Detecting Weak AE Signals Based on the Similarity of Time-Frequency Spectra

Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Wencheng Yang ◽  
Xiao Li ◽  
Yibo Wang ◽  
Yue Zheng ◽  
Peng Guo
Keyword(s):  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Critical Role ◽  
Real Data ◽  
Monitoring Method ◽  
Frequency Spectra ◽  
Arrival Times ◽  
Time Frequency ◽  
Fracturing Process ◽  
Ae Signals

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.

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