scholarly journals Time-Frequency Based Methods for Non-Stationary Signal Analysis with Application To EEG Signals

2016 ◽  
Author(s):  
◽  
Amal Feltane
Keyword(s):  
Signal Analysis ◽  
Eeg Signals ◽  
Time Frequency ◽  
Non Stationary Signal

Vibration Analysis of Gears with Fatigue Crack in Tooth Root

2006 ◽  
Vol 324-325 ◽  
pp. 835-838
Author(s):  
Aleš Belšak ◽  
Jože Flašker
Keyword(s):  
Fatigue Crack ◽  
Laboratory Test ◽  
Signal Analysis ◽  
Vibration Analysis ◽  
Operating Conditions ◽  
Test Plant ◽  
Tooth Root ◽  
Unit Operation ◽  
Time Frequency ◽  
Non Stationary Signal

A crack in the tooth root, which often leads to failure in gear unit operation, is the most undesirable damage caused to gear units. This article deals with fault analyses of gear units with real damages. Numerical simulations of real operating conditions have been used in relation to the formation of those damages. A laboratory test plant has been used and a possible damage can be identified by monitoring vibrations. The influences of defects of a single-stage gear unit upon the vibrations they produce are presented. Signal analysis has been performed also in concern to a non-stationary signal, using the Time Frequency Analysis tools. Typical spectrograms, which are the result of reactions to damages, are a very reliable indication of the presence of damages.

Investigating Vibration Sources of Faulty Gear Units

2008 ◽  
Vol 385-387 ◽  
pp. 601-604
Author(s):  
Ales Belsak ◽  
Joze Flasker
Keyword(s):  
Signal Analysis ◽  
Analysis Tool ◽  
Tooth Root ◽  
Dynamic Parameters ◽  
Unit Operation ◽  
Time Frequency ◽  
Tooth Stiffness ◽  
Non Stationary Signal ◽  
High Degree ◽  
Very High

A crack in the tooth root is the least desirable damage of gear units, which often leads to failure of gear unit operation. A possible damage can be identified by monitoring vibrations. The influences that a crack in the tooth root of a single-stage gear unit has upon vibrations are dealt with. Changes in tooth stiffness are much more expressed in relation to a fatigue crack in the tooth root, whereas in relation to other faults, changes of other dynamic parameters are more expressed. Signal analysis has been performed in relation to a non-stationary signal, by means of the Time Frequency Analysis tool, such as Wavelets. Typical scalogram patterns resulting from reactions to faults or damages indicate the presence of faults or damages with a very high degree of reliability.

Non-Stationary Signal Analysis Based on Hilbert-Huang Transform and Median Filter

2014 ◽  
Vol 1049-1050 ◽  
pp. 1694-1697
Author(s):  
Xiao Li Wang ◽  
Dian Hong Wang
Keyword(s):  
White Noise ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Median Filter ◽  
Low Noise ◽  
Analytic Framework ◽  
Noise Resistance ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Non Stationary Signal

The Hilbert-Huang Transform (HHT) is a new time-frequency analysis with adaptability and orthogonality, but it is rather weak in terms of noise resistance, even low noise can disturb the HHT result greatly. The paper launches an investigation on how noises affect the HHT result and proposes the method to solve the problem. The analytic framework for HHT is first introduced, the feature of the test signal is extracted by HHT. Median filter is adopted to reduce the frequency leakage of certain signal component caused by white noise. The method proposed is experimentally simulated and the results demonstrate its effectiveness.

scholarly journals Classification of Enchepalo Graph (EEG) Signals for Epilepsy Using Discrete Wavelet Transform and K-Nearest Neighbor Methods

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Maulana Angga Pribadi
Keyword(s):  
Signal Analysis ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Epilepsy Classification ◽  
Non Stationary Signal ◽  
The Brain

Epileps is a disorder of the contents of the nervous system of the human brain resulting in the presence of abnormal activity that is the excessive activity of neuron cells in the brain. In Indonesia there are more than 1,400,000 cases of Epilepsy each year with 70,000 additional cases each year. About 4050% occurs in children. A widely used method for assessing brain activity is through a sephalogram (EEG) Electrone signal. The Epilepsy classification system is built with extraction and identifikas stages. Wavelet exctraction is suitable for non-stationary signal analysis such as EEG signals. Wavelet tranformation can extract signal components only at the required frequency. So that it can also reduce the amount of data but without losing meaningful information. But to make it work and can be used on a system needs to be done classification in order to be able to distinguish between commands from each other. So it is used K-Nearest Neighbour (K-NN) classification method so that the signal that has been eliminated buzz can be directly entered into the classification to determine the correct wrongness of a data. In this study obtained the results of data accuracy value that K = 1 has the largest percent of 100% and the smallest percent is found in K = 7 and K = 11 namely 14.2% and 18.2% it is caused by the presence of classes that do not match the test data so as to reduce the percentage of accuracy in the K.

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