Guided Waves in Steel Rails – Experimental Works and Wavelet Signal Processing

2007 ◽  
Vol 347 ◽  
pp. 115-120
Author(s):  
Magdalena Rucka ◽  
Krzysztof Wilde
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Guided Waves ◽  
Flaw Detection ◽  
Transverse Waves ◽  
Dispersive Waves ◽  
Time Frequency ◽  
Time Signals ◽  
Frequency Components ◽  
Short Time

This paper presents experimental study on dispersive waves propagation in steel rails. The propagation of longitudinal and transverse waves was generated by an impulse hammer and measured in three points. Wavelet transform (WT) and short time Fourier transform (STFT) were applied to analyze the time signals. Analysis of signal by STFT does not provide a proper timefrequency representation due to a fixed size window. The wavelet transform can effectively identify the time-frequency components in waves. The wavelet signal processing of the experimental wave propagation signals is intended to be used for rail flaw detection.

THE FLEXIBLE GABOR-WAVELET TRANSFORM FOR CAR CRASH SIGNAL ANALYSIS

2009 ◽  
Vol 07 (04) ◽  
pp. 481-490 ◽  
Author(s):  
DARIAN M. ONCHIŞ ◽  
ESPERANZA M. SÚAREZ SÁNCHEZ
Keyword(s):  
Wavelet Transform ◽  
Low Frequency ◽  
Processing Technique ◽  
Gabor Wavelet ◽  
Signal Processing Technique ◽  
Gabor Wavelet Transform ◽  
Car Crash ◽  
Time Signals ◽  
Frequency Components ◽  
Short Time

This paper is concerned with the spectral decomposition and the adaptive analysis of data coming from car crash simulations. The mathematical ingredient of the proposed signal processing technique is the flexible Gabor-wavelet transform or the α-transform that reliably detects both high and low frequency components of such complicated short-time signals. We go from the functional treatment of this wavelet-type transform to its numerical implementation and we show how it can be used as an improved tool for spectral investigations compared to the short-time Fourier transform or the classical wavelet transform.

Research on Applications of Wavelet Transform Threshold Method to Strong Motion Signal Processing

2012 ◽  
Vol 446-449 ◽  
pp. 2387-2391
Author(s):  
Wei Li ◽  
Shan You Li ◽  
Zhen Zhao ◽  
Zhi Xin Sun
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
High Frequency ◽  
Strong Motion ◽  
Short Time Fourier Transform ◽  
Threshold Method ◽  
Time Frequency ◽  
Motion Signal ◽  
Short Time

Fourier transform and short-time Fourier transform are the main methods in signal analysis, which can reflect the spectrum signature of signals in the whole time domain; but they cannot be used in the multi-resolution analysis on the non-stationary signals. However, the wavelet transform overcome the limits of Fourier and short-time Fourier transform, which can be performed in accurate time-frequency analysis of signals. Furthermore, the diversity of wavelet functions makes the wavelet transform more adaptive and flexible. Applying the wavelet transform to seismic signal processing is the complement and improvement of existing processing methods. In this paper, the basic theory of the wavelet threshold denoising method and its application to the strong motion signal processing were mainly introduced. The high-frequency noises were removed, and simultaneously the high-frequency signals were effectively retained.

scholarly journals Strain Sensitivity Enhancement of Broadband Ultrasonic Signals in Plates Using Spectral Phase Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2582
Author(s):  
Lucas M. Martinho ◽  
Alan C. Kubrusly ◽  
Nicolás Pérez ◽  
Jean Pierre von der Weid
Keyword(s):  
Fourier Transform ◽  
Guided Waves ◽  
Strain Level ◽  
Energy Concentration ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
The Fourier Transform ◽  
Short Time ◽  
Sensitivity Gain

The focused signal obtained by the time-reversal or the cross-correlation techniques of ultrasonic guided waves in plates changes when the medium is subject to strain, which can be used to monitor the medium strain level. In this paper, the sensitivity to strain of cross-correlated signals is enhanced by a post-processing filtering procedure aiming to preserve only strain-sensitive spectrum components. Two different strategies were adopted, based on the phase of either the Fourier transform or the short-time Fourier transform. Both use prior knowledge of the system impulse response at some strain level. The technique was evaluated in an aluminum plate, effectively providing up to twice higher sensitivity to strain. The sensitivity increase depends on a phase threshold parameter used in the filtering process. Its performance was assessed based on the sensitivity gain, the loss of energy concentration capability, and the value of the foreknown strain. Signals synthesized with the time–frequency representation, through the short-time Fourier transform, provided a better tradeoff between sensitivity gain and loss of energy concentration.

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

Decomposition of Event-Related Brain Potentials into Multiple Functional Components Using Wavelet Transform

2001 ◽  
Vol 32 (3) ◽  
pp. 122-138 ◽  
Author(s):  
Tamer Demiralp ◽  
Ahmet Ademoglu
Keyword(s):  
Wavelet Transform ◽  
Three Dimensional ◽  
Experimental Session ◽  
Brain Potentials ◽  
Time Frequency ◽  
Similar Characterization ◽  
Component Identification ◽  
Frequency Components ◽  
Neurophysiological Mechanisms ◽  
Functional Components

Event related brain potential (ERP) waveforms consist of several components extending in time, frequency and topographical space. Therefore, an efficient processing of data which involves the time, frequency and space features of the signal, may facilitate understanding the plausible connections among the functions, the anatomical structures and neurophysiological mechanisms of the brain. Wavelet transform (WT) is a powerful signal processing tool for extracting the ERP components occurring at different time and frequency spots. A technical explanation of WT in ERP processing and its four distinct applications are presented here. The first two applications aim to identify and localize the functional oddball ERP components in terms of certain wavelet coefficients in delta, theta and alpha bands in a topographical recording. The third application performs a similar characterization that involves a three stimulus paradigm. The fourth application is a single sweep ERP processing to detect the P300 in single trials. The last case is an extension of ERP component identification by combining the WT with a source localization technique. The aim is to localize the time-frequency components in three dimensional brain structure instead of the scalp surface. The time-frequency analysis using WT helps isolate and describe sequential and/or overlapping functional processes during ERP generation, and provides a possibility for studying these cognitive processes and following their dynamics in single trials during an experimental session.

scholarly journals Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design

2021 ◽  
Author(s):  
Alain Beaudelaire Tchagang ◽  
Ahmed H. Tewfik ◽  
Julio J. Valdés
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
High Speed ◽  
Density Functional ◽  
Molecular Design ◽  
Absolute Error ◽  
Molecular Data ◽  
Deep Convolutional Neural Networks ◽  
Time Frequency ◽  
Short Time

Abstract Accumulation of molecular data obtained from quantum mechanics (QM) theories such as density functional theory (DFTQM) make it possible for machine learning (ML) to accelerate the discovery of new molecules, drugs, and materials. Models that combine QM with ML (QM↔ML) have been very effective in delivering the precision of QM at the high speed of ML. In this study, we show that by integrating well-known signal processing (SP) techniques (i.e. short time Fourier transform, continuous wavelet analysis and Wigner-Ville distribution) in the QM↔ML pipeline, we obtain a powerful machinery (QM↔SP↔ML) that can be used for representation, visualization and forward design of molecules. More precisely, in this study, we show that the time-frequency-like representation of molecules encodes their structural, geometric, energetic, electronic and thermodynamic properties. This is demonstrated by using the new representation in the forward design loop as input to a deep convolutional neural networks trained on DFTQM calculations, which outputs the properties of the molecules. Tested on the QM9 dataset (composed of 133,855 molecules and 16 properties), the new QM↔SP↔ML model is able to predict the properties of molecules with a mean absolute error (MAE) below acceptable chemical accuracy (i.e. MAE < 1 Kcal/mol for total energies and MAE < 0.1 ev for orbital energies). Furthermore, the new approach performs similarly or better compared to other ML state-of-the-art techniques described in the literature. In all, in this study, we show that the new QM↔SP↔ML model represents a powerful technique for molecular forward design. All the codes and data generated and used in this study are available as supporting materials. The QM↔SP↔ML is also housed at the following website: https://github.com/TABeau/QM-SP-ML.

Time Frequency Analysis of Dispersive Waves by Means of Wavelet Transform

1995 ◽  
Vol 62 (4) ◽  
pp. 841-846 ◽  
Author(s):  
Kikuo Kishimoto ◽  
Hirotsugu Inoue ◽  
Makoto Hamada ◽  
Toshikazu Shibuya
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Gabor Wavelet ◽  
Flexural Wave ◽  
Lateral Impact ◽  
Dispersive Waves ◽  
Time Frequency Analysis ◽  
Arrival Times ◽  
Time Frequency ◽  
Wide Range

A new approach is presented for investigating the dispersive character of structural waves. The wavelet transform is applied to the time-frequency analysis of dispersive waves. The flexural wave induced in a beam by lateral impact is considered. It is shown that the wavelet transform using the Gabor wavelet effectively decomposes the strain response into its time-frequency components. In addition, the peaks of the time-frequency distribution indicate the arrival times of waves. By utilizing this fact, the dispersion relation of the group velocity can be accurately identified for a wide range of frequencies.

scholarly journals Wavelet Transform in Vibration Analysis for Mechanical Fault Diagnosis

1996 ◽  
Vol 3 (1) ◽  
pp. 17-26 ◽  
Author(s):  
W.J. Wang
Keyword(s):  
Wavelet Transform ◽  
Time Scale ◽  
Vibration Analysis ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Vibration Signals ◽  
Time Frequency ◽  
Special Cases ◽  
Mechanical Faults ◽  
Short Time

The wavelet transform is introduced to indicate short-time fault effects in associated vibration signals. The time-frequency and time-scale representations are unified in a general form of a three-dimensional wavelet transform, from which two-dimensional transforms with different advantages are treated as special cases derived by fixing either the scale or frequency variable. The Gaussian enveloped oscillating wavelet is recommended to extract different sizes of features from the signal. It is shown that the time-frequency and time-scale distributions generated by the wavelet transform are effective in identifying mechanical faults.

scholarly journals Application of Short Time Fourier Transform and Wavelet Transform for Sound Source Localization Using Single Moving Microphone in Machine Condition Monitoring

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Meifal Rusli
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Sound Source ◽  
Low Frequency ◽  
Peak Position ◽  
Source Position ◽  
Short Time Fourier Transform ◽  
Sound Sources ◽  
Time Frequency ◽  
Short Time

<p class="TTPParagraphothers"><em>The paper discusses means to predict sound source position emitted by fault machine components based on a single microphone moving in a linear track with constant speed.</em> The position of sound source that consists of some frequency spectrum is detected by time-frequency distribution of the sound signal through Short Time Fourier Transform (STFT) and Continues Wavelet Transform (CWT). <em>As the amplitude of sound pressure increases when the microphone moves closer, the source position and frequency are predicted from the peaks of time-frequency contour map</em><em>. </em>Firstly, numerical simulation is conducted using two sound sources that generate four different frequencies of sound. The second case is experimental analysis using rotating machine being monitored with unbalanced, misalignment and bearing defect. The result shows that application of both STFT and CWT are able to detect multiple sound sources position with multiple frequency peaks caused by machine fault. The STFT can indicate the frequency very clearly, but not for the peak position. On the other hand, the CWT is able to predict the position of sound at low frequency very clearly. However, it is failed to detect the exact frequency because of overlapping.</p>

scholarly journals Wavelet application in acoustic emission signal detection of wire related events in pipeline

2021 ◽  
Author(s):  
Ran Wu
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Wavelet Transform ◽  
Signal Detection ◽  
Time Scale ◽  
Inspection Time ◽  
Time Frequency ◽  
Pipeline Inspection ◽  
Ae Signal ◽  
Thesis Work

This thesis establishes an automatic classification program for the signal detection work in pipeline inspection. Time-scale analysis provides the basic methodology of this thesis work. The wavelet transform is implemented in the program for filtering out the majority of noise and detect needed signals. As a popular nondestructive test, acoustic emission (AE) testing has been widely used in many physical and engineering fields such as leak detection and pipeline inspection. Among those applied AE tests, a common problem is to extract the physical features of the ideal events, so as to detect similar signals. In acoustic signal processing, those features can be represented as joint time frequency distribution. However, classical signal processing methods only give global information on either time or frequency domain, while local information is lots. Although the short-time Fourier transform (STFT) is developed to analyze time and frequency details simultaneously, it can only achieve limited precision. Other time-frequency methods are also applied in AE signal processing, but they all have the problem of resolution and time consuming. Wavelet transform is a time-scale technique with adaptable precision, which makes better feature extraction and detail detection. This thesis is an application of wavelet transform in AE signal detection where various noise exists. The wavelet transform with Morelet wavelet as the mother wavelet provides the basis of the program for auto classification in this thesis work. Finally the program is tested with two industrial projects to verify the workability of wavelet transforms and the reliability of the developed auto classifiers.

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