Computation of continuous wavelet transform via a new wavelet function for visualization of power system disturbances

2002 ◽  
Author(s):  
Shyh-Jier Huang ◽  
Cheng-Tao Hsieh
Keyword(s):  
Wavelet Transform ◽  
Power System ◽  
Continuous Wavelet Transform ◽  
Wavelet Function ◽  
Continuous Wavelet ◽  
Power System Disturbances

scholarly journals Damping identification in multi-degree-of-freedom systems using the continuous wavelet transform

2005 ◽  
Vol 27 (1) ◽  
pp. 41-50 ◽  
Author(s):  
Nguyen Phong Dien
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Damping Ratio ◽  
Morlet Wavelet ◽  
Wavelet Function ◽  
Degree Of Freedom ◽  
Damping Coefficient ◽  
Steel Beam ◽  
Continuous Wavelet ◽  
Modal Damping

The identification of damping in multi-degree-of-freedom vibration systems is a well-known problem and appears to be of crucial interest. Compared to an estimation of the stiffness and mass, the damping coefficient or, alternatively, damping ratio is the most difficult quantity to determine. In this paper, the continuous wavelet transform based on the Morlet-wavelet function is used to identify the modal damping ratios of multi-degree-of-freedom vibration systems. A new wavelet-based method for the damping identification from measured free responses is presented. The proposed method was also tested by experiments on a steel beam.

Defects Diagnosis of Bearing by Means of Acoustic Emission and Continuous Wavelet Transform

2009 ◽  
Vol 413-414 ◽  
pp. 651-657 ◽  
Author(s):  
Ru Jiang Hao ◽  
Zhi Peng Feng ◽  
Fu Lei Chu
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Rolling Bearing ◽  
Morlet Wavelet ◽  
Wavelet Function ◽  
Continuous Wavelet ◽  
Test Rig ◽  
Ae Signal ◽  
Transform Coefficients

The acoustic emission signals of rolling bearing with different type of defects are de-noised and illustrated by the continuous wavelet transform and scalogram. Morlet wavelet function is selected and the wavelet parameters are optimized based on the principle of minimal wavelet entropy. The soft-threshold de-noising is used to filter the wavelet transform coefficients. The de-noised signals obtained by reconstructing the wavelet coefficients show the obvious impulsive features. Based on the optimized waveform parameters, the wavelet scalogram is used to analyze the real AE signal from the defective rolling bearing in experimental test rig. The results indicate that the proposed method is useful and efficient for signal purification and features extraction.

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