A novel feature representation method based on deep neural networks for gear fault diagnosis

2017 ◽  
Author(s):  
Jinrui Wang ◽  
Xingxing Jiang ◽  
Shunming Li ◽  
Yu Xin
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Deep Neural Networks ◽  
Feature Representation ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Representation Method

Shallow Versus Deep Neural Networks in Gear Fault Diagnosis

2020 ◽  
Vol 35 (3) ◽  
pp. 1338-1347 ◽  
Author(s):  
Giansalvo Cirrincione ◽  
Rahul Ranjeev Kumar ◽  
Ali Mohammadi ◽  
Shahin Hedayati Kia ◽  
Pietro Barbiero ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Deep Neural Networks ◽  
Gear Fault ◽  
Gear Fault Diagnosis

The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis

2014 ◽  
Vol 1014 ◽  
pp. 510-515 ◽  
Author(s):  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
Min Gou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Predictive Model ◽  
Feature Vector ◽  
Local Mean Decomposition ◽  
Outer Race ◽  
Model Based ◽  
Gear Fault ◽  
Local Mean ◽  
Gear Fault Diagnosis ◽  
Non Stationary Signal

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

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