A new structuring element for multi-scale morphology analysis and its application in rolling element bearing fault diagnosis

2013 ◽  
Vol 21 (4) ◽  
pp. 765-789 ◽  
Author(s):  
Qiong Chen ◽  
Zhaowen Chen ◽  
Wei Sun ◽  
Guoan Yang ◽  
Ahmet Palazoglu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Rolling Element Bearing ◽  
Scale Morphology ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Structuring Element ◽  
Multi Scale ◽  
Morphology Analysis ◽  
Rolling Element ◽  
Element Bearing

Rolling element bearing fault diagnosis based on multi-scale global fuzzy entropy, multiple class feature selection and support vector machine

2019 ◽  
Vol 41 (14) ◽  
pp. 4013-4022 ◽  
Author(s):  
Keheng Zhu ◽  
Liang Chen ◽  
Xiong Hu
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Entropy ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration

Multi-scale fuzzy entropy (MFE) is a recently developed non-linear dynamic parameter for measuring the complexity of vibration signals of rolling element bearing over different scales. However, the calculation of fuzzy entropy (FuzzyEn) in each scale ignores the sequence’s global characteristics while the bearing vibration signals’ global fluctuation may vary as the bearing runs under different states. Therefore, in this paper, the multi-scale global fuzzy entropy (MGFE) method is put forward for extracting the fault features from the bearing vibration signals. After the feature extraction, multiple class feature selection (MCFS) method is introduced to select the most informative features from the high-dimensional feature vector. Then, a new rolling element bearing fault diagnosis approach is proposed based on MGFE, MCFS and support vector machine (SVM). The experimental results indicate that the proposed approach can effectively fulfill the fault diagnosis of rolling element bearing and has good classification performance.

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