GEARBOX FAULT DIAGNOSIS USING ADAPTIVE WAVELET FILTER

2003 ◽  
Vol 17 (6) ◽  
pp. 1259-1269 ◽  
Author(s):  
J. LIN ◽  
M.J. ZUO
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Filter ◽  
Adaptive Wavelet

Refinement Envelope Analysis for Small Failures of Rolling Bearing

2011 ◽  
Vol 383-390 ◽  
pp. 2622-2627
Author(s):  
Shu Shang Zhao ◽  
Juan Juan Pan
Keyword(s):  
Fault Diagnosis ◽  
Hilbert Transform ◽  
Vibration Spectrum ◽  
Rolling Bearing ◽  
Bearing Failure ◽  
Wavelet Filter ◽  
Bearing Defect ◽  
Small Fault ◽  
Bearing Vibration ◽  
Envelope Spectrum

In the rotating machinery, rolling bearing is used widespread in many places. Due to various reasons, there is great dispersion in the life of bearing. Therefore, it is very important to have fault diagnosis of rolling bearing, especially the small fault diagnosis of rolling bearing. According to the characteristics of rolling bearing defect signals and the features integrated with wavelet transform, Hilbert transform and envelope spectrum detailed analysis, this text proposed a method to judge the bearing failure. At first, bearing vibration signals are reconstructed from wavelet filter and envelope signals are obtained by Hilbert transform and then vibration spectrum is obtained from the refining envelope spectrum. Bearing failure is judged from the refining frequency spectrum. Bearing failure is also estimated by experiment to verify the correctness of theoretical analysis.

scholarly journals Bearing Fault Signal Analysis Based on an Adaptive Multiscale Combined Morphological Filter

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chun Lv ◽  
Peilin Zhang ◽  
Dinghai Wu ◽  
Bing Li ◽  
Yunqiang Zhang
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Morphological Filter ◽  
Wavelet Filter ◽  
Bearing Fault ◽  
Singular Value Decomposition Method ◽  
Bearing Fault Diagnosis ◽  
Important Means ◽  
Value Decomposition ◽  
Better Than

Bearing fault signal analysis is an important means of bearing fault diagnosis. To effectively eliminate noise in a fault signal, an adaptive multiscale combined morphological filter is proposed based on the theory of mathematical morphology. Both simulation and experimental results show that the adaptive multiscale combined morphological filter can remove noise more thoroughly and retain details of the fault signal better than the dual-tree complex wavelet filter, traditional morphological filter, adaptive singular value decomposition method (ASVD), and improved switching Kalman filter (ISKF). The adaptive multiscale combined morphological filter considers both positive and negative impulses in the signal; therefore, it has strong adaptability to complex noise in the environment, making it an effective new method for bearing fault diagnosis.

Turbine Generator Fault Diagnosis Based on Time Domain Average Laplace Wavelet

2012 ◽  
Vol 220-223 ◽  
pp. 701-704
Author(s):  
Xue Ping Ren ◽  
Jin Lei Wang
Keyword(s):  
Fourier Transform ◽  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Time Domain ◽  
Wavelet Filter ◽  
Turbine Generator ◽  
Filter Theory ◽  
Diagnosis Method ◽  
The Time Domain ◽  
Correlation Filtering

In view of turbine generator vibration abnormal,introduces fault diagnosis method based on time domain average Laplace wavelet analysis, and successfully applied to the site. Briefly introduced the time domain average and Laplace wavelet filter theory and algorithms, first use time domain average extract the cycle signal component from complex signal.then use Laplace wavelet correlation filtering to get correlation coefficient, then get characteristic frequency from the correlation coefficient of Fourier transform in order to achieve fault diagnosis. Finally it verified the effectiveness of this method through an instance of the fault diagnosis of turbine generator bearings not verify.

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