scholarly journals Flaw Detection in Highly Scattering Materials Using a Simple Ultrasonic Sensor Employing Adaptive Template Matching

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 268
Author(s):  
Biao Wu ◽  
Yong Huang
Keyword(s):  
Template Matching ◽  
Rayleigh Scattering ◽  
Flaw Detection ◽  
Ultrasonic Signal ◽  
Ultrasonic Sensor ◽  
Polycrystalline Materials ◽  
Spectral Difference ◽  
Time Frequency ◽  
Frequency Features ◽  
Spectrum Model

Ultrasonic sensors have been extensively used in the nondestructive testing of materials for flaw detection. For polycrystalline materials, however, due to the scattering nature of the material, which results in strong grain noise and attenuation of the ultrasonic signal, accurate detection of flaws is particularly difficult. In this paper, a novel flaw-detection method using a simple ultrasonic sensor is proposed by exploiting time-frequency features of an ultrasonic signal. Since grain scattering mostly happens in the Rayleigh scattering region, it is possible to separate grain-scattered noise from flaw echoes in the frequency domain employing their spectral difference. We start with the spectral modeling of grain noise and flaw echo, and how the two spectra evolve with time is established. Then, a time-adaptive spectrum model for flaw echo is proposed, which serves as a template for the flaw-detection procedure. Next, a specially designed similarity measure is proposed, based on which the similarity between the template spectrum and the spectrum of the signal at each time point is evaluated sequentially, producing a series of matching coefficients termed moving window spectrum similarity (MWSS). The time-delay information of flaws is directly indicated by the peaks of MWSSs. Finally, the performance of the proposed method is validated by both simulated and experimental signals, showing satisfactory accuracy and efficiency.

Classification of spot-welded joint strength using ultrasonic signal time-frequency features and PSO-SVM method

Ultrasonics ◽  
2019 ◽  
Vol 91 ◽  
pp. 161-169 ◽  
Author(s):  
Xiaokai Wang ◽  
Shanyue Guan ◽  
Lin Hua ◽  
Bin Wang ◽  
Ximing He
Keyword(s):  
Joint Strength ◽  
Welded Joint ◽  
Ultrasonic Signal ◽  
Time Frequency ◽  
Frequency Features ◽  
Signal Time ◽  
Spot Welded

Fault diagnosis for analog circuits utilizing time-frequency features and improved VVRKFA

2018 ◽  
Vol 29 (4) ◽  
pp. 045004 ◽  
Author(s):  
Wei He ◽  
Yigang He ◽  
Qiwu Luo ◽  
Chaolong Zhang
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Time Frequency ◽  
Frequency Features

CREEPING SURFACE LONGITUDINAL ACOUSTIC WAVE: MAIN PROPERTIES AND APPLICATION POSSIBILITIES

2021 ◽  
pp. 4-12
Author(s):  
V. G. Shevaldykin
Keyword(s):  
Critical Angle ◽  
Flaw Detection ◽  
Ultrasonic Signal ◽  
Ultrasonic Waves ◽  
Concave Surface ◽  
Ultrasonic Tomography ◽  
Transverse Waves ◽  
Near Surface ◽  
The Third ◽  
Creeping Wave

Creeping ultrasonic waves have long been successfully used for flaw detection of near-surface and near-bottom zones of metal products. However, due to the fact that the creeping wave generates a lateral transverse wave directed into the metal volume at the third critical angle, it is also possible to test internal defects in principle. At known velocities of propagation of longitudinal and transverse waves in the metal, the third critical angle is easily calculated. Therefore, the time of propagation of the ultrasonic signal along any trajectory between points on the surface and in the volume of the metal can be calculated. Usually, creeping waves are used to test products of plane-parallel shape. There are no cases of their application on curved surfaces in the literature. It is possible that the creeping wave can also propagate over a concave surface. The aim of the article is to test experimentally new ways of using creeping waves. The propagation trajectories of the creeping and lateral transverse waves were studied on a steel plate. The time of passage of the ultrasonic signal along such trajectories of different lengths was measured, and the measurement results were compared with the calculated time values. The measured and calculated values coincided with accuracy sufficient for the coherent accumulation of echo signals that passed through the metal part of the path by the creeping wave and another part of the path by the lateral transverse wave.The propagation of the creeping wave over a concave surface was studied on a steel sample with cylindrical faces of different radii. As a result, it turned out that on a concave surface, the creeping wave propagates at the same speed of longitudinal waves as on a flat surface, but it decays much more strongly with distance. Studies have shown that creeping waves can be used in ultrasonic tomography, where a preliminary calculation of the propagation trajectories of ultrasonic signals is required. The propagation of creeping waves over concave surfaces extends the capabilities of the TOFD method to the area of intube testing

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.

VENTRICULAR TACHYCARDIA AND FIBRILLATION DETECTION USING DWT AND DECISION TREE CLASSIFIER

2019 ◽  
Vol 19 (03) ◽  
pp. 1950008
Author(s):  
MONALISA MOHANTY ◽  
PRADYUT BISWAL ◽  
SUKANTA SABUT
Keyword(s):  
Ventricular Tachycardia ◽  
Decision Tree ◽  
Ventricular Arrhythmias ◽  
Ventricular Tachyarrhythmia ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Time Frequency ◽  
Tree Classifier ◽  
Frequency Features

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the life-threatening ventricular arrhythmias that require treatment in an emergency. Detection of VT and VF at an early stage is crucial for achieving the success of the defibrillation treatment. Hence an automatic system using computer-aided diagnosis tool is helpful in detecting the ventricular arrhythmias in electrocardiogram (ECG) signal. In this paper, a discrete wavelet transform (DWT) was used to denoise and decompose the ECG signals into different consecutive frequency bands to reduce noise. The methodology was tested using ECG data from standard CU ventricular tachyarrhythmia database (CUDB) and MIT-BIH malignant ventricular ectopy database (VFDB) datasets of PhysioNet databases. A set of time-frequency features consists of temporal, spectral, and statistical were extracted and ranked by the correlation attribute evaluation with ranker search method in order to improve the accuracy of detection. The ranked features were classified for VT and VF conditions using support vector machine (SVM) and decision tree (C4.5) classifier. The proposed DWT based features yielded the average sensitivity of 98%, specificity of 99.32%, and accuracy of 99.23% using a decision tree (C4.5) classifier. These results were better than the SVM classifier having an average accuracy of 92.43%. The obtained results prove that using DWT based time-frequency features with decision tree (C4.5) classifier can be one of the best choices for clinicians for precise detection of ventricular arrhythmias.

Daubechies Wavelets Based Robust Audio Fingerprinting for Content-Based Audio Retrieval

2012 ◽  
Vol 4 (2) ◽  
pp. 49-69
Author(s):  
Wei Sun ◽  
Zhe-Ming Lu ◽  
Fa-Xin Yu ◽  
Rong-Jun Shen
Keyword(s):  
Wavelet Transforms ◽  
Wavelet Decomposition ◽  
Experimental Results ◽  
Daubechies Wavelet ◽  
Linear Speed ◽  
Time Frequency ◽  
Audio Fingerprinting ◽  
Audio Retrieval ◽  
Audio Clip ◽  
Frequency Features

Audio fingerprinting is the process to obtain a compact content-based signature that summarizes the essence of an audio clip. In general, existing audio fingerprinting schemes based on wavelet transforms are not robust against large linear speed changes. The authors present a novel framework for content-based audio retrieval based on the audio fingerprinting scheme that is robust against large linear speed changes. In the proposed scheme, 8 levels Daubechies wavelet decomposition is adopted for extracting time-frequency features and two fingerprint extraction algorithms are designed. The experimental results from this study are discussed further into the article.

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