Comparison of principal component analysis and multi-dimensional ensemble empirical mode decomposition for impact damage segmentation in square pulse shearography phase images

2020 ◽  
Author(s):  
Herberth Birck Fröhlich ◽  
Bernardo C. F. de Oliveira ◽  
Estiven S. Barrera ◽  
Tiago J. De Bortoli ◽  
Armando Albertazzi Gonçalves
Keyword(s):  
Principal Component Analysis ◽  
Empirical Mode Decomposition ◽  
Impact Damage ◽  
Principal Component ◽  
Component Analysis ◽  
Ensemble Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Phase Images

Multi-fault diagnosis of rolling bearing using fuzzy entropy of empirical mode decomposition, principal component analysis, and SOM neural network

2018 ◽  
Vol 233 (9) ◽  
pp. 3317-3328 ◽  
Author(s):  
Mohamed Zair ◽  
Chemseddine Rahmoune ◽  
Djamel Benazzouz
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Principal Component ◽  
Rolling Bearing ◽  
Component Analysis ◽  
Fuzzy Entropy ◽  
Self Organizing Map ◽  
Mode Function ◽  
Mode Decomposition

The condition monitoring and multi-fault diagnosis of rolling bearing is a very important research content in the field of the rotating machinery health management. Most researches widely used empirical mode decomposition in tandem with principal component analysis which is applied for feature extraction. But this method may lead to imprecise classification. In this paper, we propose a new method of rolling bearing multi-fault diagnosis, by combining the fuzzy entropy of empirical mode decomposition, principal component analysis, and self-organizing map neural network. The empirical mode decomposition process allows the vibration signal to be decomposed into a series of intrinsic mode functions. For each intrinsic mode function, we obtained the fault feature information. The proposed approach combines the fuzzy function and sample entropy to obtain fuzzy entropy. By this combination, we can reflect the complexity and the irregularity in each intrinsic mode function component. The fuzzy entropy of empirical mode decomposition used to construct the vectors is defined as the input of the principal component analysis. This principal component analysis is used to reduce the dimension of the feature vectors. Finally, the reduced feature vectors are chosen as input of self-organizing map network for automatic fault diagnosis and fault classification. The obtained results show that the proposed approach makes it possible to correctly assess the degradation of rolling bearing and to obtain recognition of high-sensitivity defects for different types of bearing faults.

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