Generalized Gaussian Noise Distribution Enabled Sparse Representation Model for Bearing Fault Diagnosis

2020 ◽  
Author(s):  
Botao An ◽  
Shibin Wang ◽  
Ruqiang Yan ◽  
Weihua Li ◽  
Xuefeng Chen
Keyword(s):  
Fault Diagnosis ◽  
Sparse Representation ◽  
Gaussian Noise ◽  
Noise Distribution ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Representation Model ◽  
Generalized Gaussian Noise ◽  
Sparse Representation Model

A Recursive Sparse Representation Strategy for Bearing Fault Diagnosis

Measurement ◽  
2021 ◽  
pp. 110360
Author(s):  
Changkun Han ◽  
Wei Lu ◽  
Pengxin Wang ◽  
Liuyang Song ◽  
Huaqing Wang
Keyword(s):  
Fault Diagnosis ◽  
Sparse Representation ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering-Enhanced Multiscale Entropy Features

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Jinbao Zhang ◽  
Yongqiang Zhao ◽  
Xinglin Li ◽  
Ming Liu
Keyword(s):  
Fault Diagnosis ◽  
Sparse Representation ◽  
Multiscale Entropy ◽  
Small Samples ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Sparse Representation Classification ◽  
Iterative Filtering

To improve the bearings diagnosis accuracy considering multiple fault types with small samples, a new approach that combined adaptive local iterative filtering (ALIF), multiscale entropy features, and kernel sparse representation classification (KSRC) is put forward in this paper. ALIF is used to adaptively decompose the nonlinear, nonstationary vibration signals into a sum of intrinsic mode functions (IMFs). Multiple entropy features such as sample entropy, fuzzy entropy, and permutation entropy with multiscale are computed from the first three IMFs and a total of one hundred and eighty features are obtained. After normalization, the features are employed to train and test the classifier KSRC, respectively. Finally, the proposed approach is evaluated with two experimental tests. One is concerned with different types of bearing faults from the centrifugal pump; and the other is from Case Western Reserve University (CWRU) considering 12 bearing fault states. Experimental results have proved that the proposed approach is efficient for bearing fault diagnosis, and high accuracy will be obtained with high dimensional features through small samples.

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