scholarly journals Advanced Rolling Bearing Fault Diagnosis Using Ensemble Empirical Mode Decomposition, Principal Component Analysis and Probabilistic Neural Network

2018 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Caixia Gao ◽  
Tong Wu ◽  
Ziyi Fu
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition

The Rolling Bearing Fault Diagnosis Research Based on Improved Hilbert-Huang Transformation

2013 ◽  
Vol 300-301 ◽  
pp. 344-350 ◽  
Author(s):  
Zhou Wan ◽  
Xing Zhi Liao ◽  
Xin Xiong ◽  
Jin Chuan Han
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Hilbert Huang Transform ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Eemd Method

For empirical mode decomposition (EMD) of Hilbert-Huang transform (HHT) exists the problem of mode mixing. An analysis method based on ensemble empirical mode decomposition (EEMD) is proposed to apply to fault diagnosis of rolling bearing. This paper puts forward, after signal pretreatment, applying EEMD method to acquire the intrinsic mode function (IMF) of fault signal. Then according to correlation coefficient for IMFs and the signal before decomposing by EEMD method, some redundant low frequency IMFs produced in the process of decomposition can be eliminated, then the effective IMF components are selected to perform a local Hilbert marginal spectrum analysis, then fault characteristics are extracted. Through the vibration analysis of inner-race fault bearing it shows that this method can be effectively applied to extract fault characteristics of rolling bearing.

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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