Fault diagnosis of rotating machinery based on kernel density estimation and Kullback-Leibler divergence

2014 ◽  
Vol 28 (11) ◽  
pp. 4441-4454 ◽  
Author(s):  
Fan Zhang ◽  
Yu Liu ◽  
Chujie Chen ◽  
Yan-Feng Li ◽  
Hong-Zhong Huang
Keyword(s):  
Fault Diagnosis ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Rotating Machinery ◽  
Leibler Divergence

An Circuit Fault Diagnosis Method with K-Means Kernel Density Estimation

2011 ◽  
Vol 66-68 ◽  
pp. 203-206
Author(s):  
Jing Tang ◽  
Xian Jun Shi ◽  
Wen Guang Zhang
Keyword(s):  
Fault Diagnosis ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Singular Problem ◽  
Support Vector ◽  
Division Problem ◽  
Fuzzy Support Vector Machine ◽  
Machine Method ◽  
Circuit Fault Diagnosis

A K-Means kernel density estimation was proposed and it was used in the pretreatment process of circuit fault diagnosis. The unequal division and losing division problem caused by the traditional method are solved by this method. It also avoid the singular problem which is usually caused by the high dimension of characteristic data. A kernel function is designed and it was integrated with fuzzy support vector machine method to solve the classification problem of multi-faults . At last, a solution of optimal bandwidth is given to improve the proposed method.

The fault diagnosis of gear based on kernel density estimation

2018 ◽  
Vol 18 (3) ◽  
pp. 779-791
Author(s):  
Hong Song ◽  
Cheng Sun ◽  
Chunlin Zhang ◽  
Tao Ren
Keyword(s):  
Fault Diagnosis ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density
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