2D and 3D adaptive signal processing in the frequency domain

2002 ◽  
Author(s):  
H. Yu ◽  
W.B. Mikhael
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Adaptive Signal Processing ◽  
Adaptive Signal ◽  
2D And 3D

Design of Adaptive Filter for Health Monitoring on a Gearbox

2006 ◽  
Vol 321-323 ◽  
pp. 1237-1240
Author(s):  
Sang Kwon Lee ◽  
Jung Soo Lee
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Adaptive Filter ◽  
Health Monitoring ◽  
Background Noise ◽  
Adaptive Signal Processing ◽  
Vibration Signals ◽  
Time Frequency ◽  
Frequency Representation ◽  
Adaptive Signal

Impulsive vibration signals in gearbox are often associated with faults, which lead to due to irregular impacting. Thus these impulsive vibration signals can be used as indicators of machinery faults. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the measurement of impulsive signal embedded in background noise, we enhance the impulsive signals using adaptive signal processing and then analyze them in time and frequency domain by using time-frequency representation. This technique is applied to the diagnosis of faults within laboratory gearbox.

LOCALLY IMPLEMENTABLE MATRIX FLOWS AND THEIR APPLICATION TO NEURAL NETWORK LEARNING

1994 ◽  
Vol 04 (01) ◽  
pp. 23-51 ◽  
Author(s):  
JEROEN DEHAENE ◽  
JOOS VANDEWALLE
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Network Structure ◽  
Adaptive Signal Processing ◽  
External Signal ◽  
Neural Network Learning ◽  
Network Learning ◽  
Adaptive Signal

A number of matrix flows, based on isospectral and isodirectional flows, is studied and modified for the purpose of local implementability on a network structure. The flows converge to matrices with a predefined spectrum and eigenvectors which are determined by an external signal. The flows can be useful for adaptive signal processing applications and are applied to neural network learning.

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