Enhancement of rolling bearing fault diagnosis based on improvement of empirical mode decomposition denoising method

2018 ◽  
Vol 97 (5-8) ◽  
pp. 3099-3117 ◽  
Author(s):  
Rabah Abdelkader ◽  
Abdelhafid Kaddour ◽  
Ziane Derouiche
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Rolling Bearing ◽  
Denoising Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition

scholarly journals A deep feature extraction method for bearing fault diagnosis based on empirical mode decomposition and kernel function

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879825 ◽  
Author(s):  
Fengtao Wang ◽  
Gang Deng ◽  
Chenxi Liu ◽  
Wensheng Su ◽  
Qingkai Han ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Rolling Bearing ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Deep Feature ◽  
Deep Feature Extraction

To avoid catastrophic failures in rotating machines, it is of great significance to continuously monitor and diagnose the running state of rolling bearings. In this article, a deep feature extraction method for rolling bearing fault diagnosis based on empirical mode decomposition and kernel function is proposed. First, the vibration signals under different states of rolling bearing are decomposed by empirical mode decomposition. Second, to extract more representative high-level features, the obtained intrinsic mode functions are preprocessed with singular value decomposition to acquire singular value parameters, which are regarded as the inputs of the proposed stacked kernel sparse autoencoder network. The proposed method does not depend on prior knowledge of fault diagnosis and even does not need the signal denoising processing, simplifying the traditional process of feature extraction of rolling bearing fault diagnosis. To validate the superiority of the proposed diagnosis network, experiments and comparisons have been made as well. The achieved results demonstrated that the proposed empirical mode decomposition and stacked kernel sparse autoencoder–based diagnosis method has a superior performance in rolling bearing fault diagnosis.

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