Sparsity-based fractional spline wavelet denoising via overlapping group shrinkage with non-convex regularization and convex optimization for bearing fault diagnosis

2020 ◽  
Vol 31 (5) ◽  
pp. 055003
Author(s):  
Lei Wang ◽  
Xin Zhang ◽  
Zhiwen Liu ◽  
Jinglin Wang
2020 ◽  
Vol 69 (7) ◽  
pp. 4863-4872 ◽  
Author(s):  
Weiguo Huang ◽  
Ning Li ◽  
Ivan Selesnick ◽  
Juanjuan Shi ◽  
Jun Wang ◽  
...  

2014 ◽  
Vol 556-562 ◽  
pp. 2677-2680 ◽  
Author(s):  
Ling Jie Meng ◽  
Jia Wei Xiang

A new rolling bearing fault diagnosis approach is proposed. The original vibration signal is purified using the second generation wavelet denoising. The purified signal is further decomposed by an improved ensemble empirical mode decomposition (EEMD) method. A new selection criterion, including correlation analysis and the first two intrinsic mode functions (IMFs) with the maximum energy, is put forward to eliminate the pseudo low-frequency components. Experimental investigation show that the rolling bearing fault features can be effectively extracted.


2018 ◽  
Vol 65 (9) ◽  
pp. 7332-7342 ◽  
Author(s):  
Shibin Wang ◽  
Ivan Selesnick ◽  
Gaigai Cai ◽  
Yining Feng ◽  
Xin Sui ◽  
...  

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