Application of Optimal Morlet Wavelet Filter for Bearing Fault Diagnosis

2015 ◽  
Vol 8 (3) ◽  
pp. 817-824 ◽  
Author(s):  
Mohamed El Morsy ◽  
Gabriela Achtenova
Keyword(s):  
Fault Diagnosis ◽  
Morlet Wavelet ◽  
Wavelet Filter ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

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.

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