scholarly journals Effective Defect Features Extraction for Laser Ultrasonic Signal Processing by Using Time–Frequency Analysis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 128706-128713 ◽  
Author(s):  
Zhenyu Zhu ◽  
Hao Sui ◽  
Lei Yu ◽  
Hongna Zhu ◽  
Jinli Zhang ◽  
...  
Keyword(s):  
Signal Processing ◽  
Frequency Analysis ◽  
Features Extraction ◽  
Ultrasonic Signal ◽  
Laser Ultrasonic ◽  
Time Frequency Analysis ◽  
Time Frequency

Effective detection of metal surface defects based on double-line laser ultrasonic with convolutional neural networks

2021 ◽  
pp. 2150263
Author(s):  
Zixi Liu ◽  
Zhengliang Hu ◽  
Longxiang Wang ◽  
Tianshi Zhou ◽  
Jintao Chen ◽  
...  
Keyword(s):  
Signal Processing ◽  
Metal Surface ◽  
Surface Defects ◽  
Ultrasonic Signal ◽  
Classification Model ◽  
Frequency Resolution ◽  
Laser Ultrasonic ◽  
Double Line ◽  
Time Frequency ◽  
Defects Classification

The time–frequency analysis by smooth Pseudo-Wigner-Ville distribution (SPWVD) is utilized for the double-line laser ultrasonic signal processing, and the effective detection of the metal surface defect is achieved. The double-line source laser is adopted for achieving more defects information. The simulation model by using finite element method is established in a steel plate with three typical metal surface defects (i.e. crack, air hole and surface scratch) in detail. Besides, in order to improve the time resolution and frequency resolution of the signal, the SPWVD method is mainly used. In addition, the deep learning defect classification model based on VGG convolutional neural network (CNN) is set up, also, the data enhancement method is adopted to extend training data and improve the defects detection properties. The results show that, for different types of metal surface defects with sub-millimeter size, the classification accuracy of crack, air holes and scratch surface are 94.6%, 94% and 94.6%, respectively. The SPWVD and CNN algorithm for processing the laser ultrasonic signal and defects classification supplies a useful way to get the defect information, which is helpful for the ultrasonic signal processing and material evaluation.

THE LINEAR TIME FREQUENCY ANALYSIS TOOLBOX

2012 ◽  
Vol 10 (04) ◽  
pp. 1250032 ◽  
Author(s):  
PETER L. SØNDERGAARD ◽  
BRUNO TORRÉSANI ◽  
PETER BALAZS
Keyword(s):  
Signal Processing ◽  
Frequency Analysis ◽  
Linear Time ◽  
Computational Time ◽  
Computational Tool ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mathematical Background ◽  
Specific Signal

The Linear Time Frequency Analysis Toolbox is a MATLAB/Octave toolbox for computational time-frequency analysis. It is intended both as an educational and computational tool. The toolbox provides the basic Gabor, Wilson and MDCT transform along with routines for constructing windows (filter prototypes) and routines for manipulating coefficients. It also provides a bunch of demo scripts devoted either to demonstrating the main functions of the toolbox, or to exemplify their use in specific signal processing applications. In this paper we describe the used algorithms, their mathematical background as well as some signal processing applications.

Digital signal processing for self-vibration monitoring in grinding: A new approach based on the time-frequency analysis of vibration signals

Measurement ◽  
2019 ◽  
Vol 145 ◽  
pp. 71-83 ◽  
Author(s):  
Rogério Thomazella ◽  
Wenderson Nascimento Lopes ◽  
Paulo Roberto Aguiar ◽  
Felipe Aparecido Alexandre ◽  
Arthur Alves Fiocchi ◽  
...  
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Frequency Analysis ◽  
Digital Signal ◽  
Vibration Monitoring ◽  
Vibration Signals ◽  
New Approach ◽  
Time Frequency Analysis ◽  
Time Frequency
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