scholarly journals Heart Cardiac Sounds analysis using the Wigner distribution (WD) Method

2021 ◽  
Vol 04 (15) ◽  
pp. 01-04
Author(s):  
S.M. DEBBAL
Keyword(s):  
Wigner Distribution ◽  
Time Frequency ◽  
Quantitative Measurements ◽  
Signal Characteristics ◽  
Pcg Signals

This paper is concerned a “The Wigner distribution (WD)” analysis of the Heart cardiac (or phonocardiogram signals: PCG). The Wigner distribution (WD) and the corresponding WVD (Wigner Ville Distribution) have shown good performances in the analysis of non-stationary and quantitative measurements of the time-frequency PCG signal characteristics. It is shown that these transforms provides enough features of the PCG signals that will help clinics to obtain diagnosis.

THE FAST FOURIER TRANSFORM AND THE CONTINUOUS WAVELET TRANSFORM ANALYSIS OF THE PHONOCARDIOGRAM SIGNAL

2004 ◽  
Vol 04 (03) ◽  
pp. 257-272 ◽  
Author(s):  
S. M. DEBBAL ◽  
F. BEREKSI-REGUIG ◽  
A. MEZIANE TANI
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Fast Fourier Transform ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Quantitative Measurements ◽  
Signal Characteristics ◽  
Wavelet Transform Analysis ◽  
Pcg Signals

This paper is concerned with a synthesis study of the fast Fourier transform (FFT) and the continuous wavelet transform (CWT) in analysing the phonocardiogram signal (PCG). It is shown that the continuous wavelet transform provides enough features of the PCG signals that will help clinics to obtain qualitative and quantitative measurements of the time-frequency PCG signal characteristics and consequently aid to diagnosis. Similary, it is shown that the frequency content of such a signal can be determined by the FFT without difficulties.

HEARTBEAT SOUND ANALYSIS WITH THE WAVELET TRANSFORM

2004 ◽  
Vol 04 (02) ◽  
pp. 133-141 ◽  
Author(s):  
S. M. DEBBAL ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Sound Analysis ◽  
Time Frequency ◽  
Qualitative And Quantitative ◽  
Quantitative Measurements ◽  
Signal Characteristics ◽  
Pcg Signals

This paper is concerned with a synthesis study of the continuous wavelet transform (CWT) in analyzing the phonocardiogram (PCG). It is shown that the CWT provides enough features of the PCG signals that will help physicians to obtain qualitative and quantitative measurements of the time and the time-frequency PCG signal characteristics, and consequently aid to diagnosis.

scholarly journals MULTIDIMENSIONAL REPRESENTATION OF AUTOMATIC DOSING DEVICE SIGNALS

2018 ◽  
pp. 26-32
Author(s):  
Denis Borisovich Fedosenkov ◽  
Anna Alekseevna Simikova ◽  
Boris Andreevich Fedosenkov ◽  
Stanislav Matveevich Kulakov
Keyword(s):  
Fourier Transform ◽  
Wigner Distribution ◽  
Low Frequency ◽  
Stationary Flow ◽  
Complex Model ◽  
Signal Spectrum ◽  
One Dimensional ◽  
Time Frequency ◽  
Cohen's Class ◽  
Discrete Values

The article describes the development of a special approach based on using multidimensional wavelet distributions principle to monitor and control the feed dozing processes in the mix preparation unit. As a key component, this approach uses the multidimensional time-frequency Wigner-Ville distribution, which is the part of Cohen's class distributions. The research focuses on signals characterizing mass transfer processes in the form of material flow measuring signals in relevant points of the unit. Wigner-Ville distribution has been shown in time terms as Fourier transform of products of multiplied parts of the signal under consideration for past and future time moments; corresponding distribution for the frequency spectrum is shown as Fourier transform of the products of signal parts for high-frequency and low-frequency fragments of the signal spectrum. It has been noted that when using a complex model of a dozing signal, discrete values (samples) of the latter are considered as its real values. The description of the signal parameters (amplitude, phase, frequency) has been carried out with the help of Hilbert transform. In Cohen's class distributions which represent one-dimensional non-stationary flow signals, the concept of ‘instantaneous frequency’ has been introduced. A graphical explanation for the transformation of a process flow signal from a one-dimensional time domain to a time-frequency 2 D/ 3 D -space is presented. The technology of developing a multidimensional image in the form of Wigner distribution for one-dimensional signals of continuous spiral or screw-type feeders has been examined in detail. There have been considered the features to support Wigner distribution, which allow to guess the presence or absence of time-frequency distribution elements in the interval of signal recording. There has been demonstrated how Wigner distribution can be obtained for a continuous-intermittent feeding signal. It has been concluded that for a certain types of the signal for zero fragments of the latter, non-zero time-frequency elements (i.e. virtual, anomalous ones) appear on the distribution. In addition to Wigner distribution, two other distributions - of Rihachek and Page - are considered. They display the same signal and also contain virtual elements, but in different domains of the time-frequency space. A generalized multidimensional compound signal distribution with a so-called distribution kernel available in it is presented, which includes a correction parameter that allows controlling the intensity of the virtual signal energy.

Wigner–Ville distribution based on cyclic spectral density and the application in rolling element bearings diagnosis

2011 ◽  
Vol 225 (12) ◽  
pp. 2831-2847 ◽  
Author(s):  
Y Zhou ◽  
J Chen ◽  
G M Dong ◽  
W B Xiao ◽  
Z Y Wang
Keyword(s):  
Fault Diagnosis ◽  
Spectral Density ◽  
Wigner Distribution ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Accurate Analysis ◽  
Time Frequency ◽  
Bearing Fault ◽  
Rolling Element ◽  
Cross Terms

The vibration signals of rolling element bearings are random cyclostationary when they have faults. Also, statistical properties of the signals change periodically with time. The accurate analysis of time-varying signals is an essential pre-requisite for the fault diagnosis and hence safe operation of rolling element bearings. The Wigner distribution is probably most widely used among the Cohen’s class in order to describe how the spectral content of a signal changes over time. However, the basic nature of such signals causes significant interfering cross-terms, which do not permit a straightforward interpretation of the energy distribution. To overcome this difficulty, the Wigner–Ville distribution (WVD) based on the cyclic spectral density (CSD) is discussed in this article. It is shown that the improved WVD, based on CSD of a long time series, can render the time–frequency distribution less susceptible to noise, and restrain the cross-terms in the time–frequency domain. Simulation and experiment of the rolling element-bearing fault diagnosis are performed, and the results indicate the validity of WVD based on CSD in time–frequency analysis for bearing fault detection.

Identification of Fault Signals in Rotating Machinery Using Higher Order Time Frequency Analysis

1997 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Paul R. White
Keyword(s):  
Fault Diagnosis ◽  
Wigner Distribution ◽  
Higher Order ◽  
Rotating Machinery ◽  
Data Sets ◽  
Higher Order Statistics ◽  
Vibration Signals ◽  
Time Frequency ◽  
Increasing Trend ◽  
Monitoring Application

Abstract Impulsive acoustic and vibration signals within rotating machinery are often induced by irregular impacting. Thus the detection of these impulses can be useful for fault diagnosis. Recently there is an increasing trend towards the use of higher order statistics for fault detection within mechanical systems based on the observation that impulsive signals tend to increase the kurtosis values. We show that the fourth order Wigner Moment Spectrum, called the Wigner Trispectrum, has superior detection performance to second order Wigner distribution for typical impulsive signals found in a condition monitoring application. These methods are also applied to data sets measured within a car engine and industrial gearbox.

Construction of the Health Monitoring System of a Machine Structure

2009 ◽  
Author(s):  
Masanori Shintani ◽  
Keita Masaki
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Normal Vibration ◽  
Wigner Distribution ◽  
Random Noise ◽  
Unsteady State ◽  
High Frequency Region ◽  
Health Monitoring System ◽  
Time Frequency ◽  
Vibration Wave

When big power like an earthquake acts at the place that the machine is normally operating, abnormalities may occur to a machine. If the machine is operated without finding abnormally, danger may attain to mechanical fatal damage and a mechanical work pursuer. Therefore, detecting in the situation where mechanical abnormalities are operated is very important as a health monitoring system. In this research, the system that takes in the vibration wave on the rotation part of the machine currently rotated is constructed. A vibration wave is analyzed using time-frequency analysis (STFT, the Wigner distribution, wave let analysis) From the result, the system by which normal vibration and abnormal vibration are evaluated is constructed from random noise. As a result of comparing normal vibration with abnormal vibration, the peak may have occurred in the high frequency region. It turned out that the analysis result of an unsteady state has a peak 2000Hz–3000Hz of frequency domains, and 4000Hz–5000Hz also in STFT and Wigner distribution. I think that this becomes the important tool which distinguishes the stationary state and unsteady state in health monitoring.

ECG Signal Denoising Algorithm Based on Optimization Wavelet Transform

2013 ◽  
Vol 706-708 ◽  
pp. 785-788
Author(s):  
Guo Shun Yuan ◽  
Li Qing Geng
Keyword(s):  
Wavelet Transform ◽  
Median Filter ◽  
Noise Removal ◽  
Signal Denoising ◽  
Time Frequency ◽  
Multi Resolution Analysis ◽  
Signal Characteristics ◽  
Eigen Values ◽  
Resolution Analysis ◽  
Ecg Noise Removal

Wavelet transform algorithm with its unique multi-resolution analysis and it is in the time - frequency domain has the advantage of the ability to characterize the local signal characteristics, let it has been widely used in signal detection, noise removal, feature extraction, image compression and so on. In this paper, on the basis of already wavelet transform ECG noise removal, proposed a median filter optimization algorithm, enables ECG noise removal effect is more obvious, also for the Eigen values detection of ECG lay a better foundation.

Nondestructive Evaluation of In-Isolation Pile Shaft Integrity by Wigner-Ville Distribution

2007 ◽  
Vol 23 (1) ◽  
pp. 15-21 ◽  
Author(s):  
S.-H. Ni ◽  
J.-J. Charng ◽  
K.-F. Lo
Keyword(s):  
Nondestructive Evaluation ◽  
Frequency Domain ◽  
Frequency Domain Analysis ◽  
Analysis Tool ◽  
Signal Process ◽  
Time Frequency ◽  
Concrete Piles ◽  
Integrity Testing ◽  
Signal Characteristics ◽  
Cast Concrete

AbstractThe Wigner-Ville Distribution is a new numerical analysis tool for signal process technique in the time-frequency domain and it can offer assistance and enhance signal characteristics for better resolution both easily and quickly. Time-frequency transform can describe how a spectrum of signals changes with time owing to defects and boundary conditions. In this study, five single pre-cast concrete piles have been tested and evaluated by both sonic echo method and Wigner-Ville distribution (WVD). The appropriateness of time-frequency domain analysis is discussed. Furthermore, two difficult problems in nondestructive evaluation problems are discussed and solved: the first one is with a pile with slight defect, whose necking area percentage is less than 10%, and the other is a pile with multiple defects. The results show that WVD can not only recognize the characteristics easily, but also locate the defects more clearly than the traditional pile integrity testing method.

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