scholarly journals Multi-class Support Vector Machine for On-line Spectral Quality Monitoring of Tobacco Products

2013 ◽  
Vol 25 (7) ◽  
pp. 3668-3672 ◽  
Author(s):  
C. Tan ◽  
T. Wu ◽  
X. Qin
Keyword(s):  
Support Vector Machine ◽  
Quality Monitoring ◽  
Support Vector ◽  
Spectral Quality ◽  
Tobacco Products ◽  
On Line

On-line fault diagnosis model for locomotive traction inverter based on wavelet transform and support vector machine

2018 ◽  
Vol 88-90 ◽  
pp. 1274-1280 ◽  
Author(s):  
Mei Fei ◽  
Liu Ning ◽  
Miao Huiyu ◽  
Pan Yi ◽  
Sha Haoyuan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Support Vector ◽  
On Line ◽  
Diagnosis Model

Research on Indicator Diagram of Shock Absorber Fault Recognition Based on Support Vector Machine

2013 ◽  
Vol 765-767 ◽  
pp. 2195-2198
Author(s):  
Wei Dong Xie ◽  
Kan Gao ◽  
Ji Sheng Shen
Keyword(s):  
Support Vector Machine ◽  
Shock Absorber ◽  
Recognition Rate ◽  
Support Vector ◽  
Indicator Diagram ◽  
Fault Recognition ◽  
Average Recognition Rate ◽  
Rbf Kernel ◽  
On Line ◽  
Rbf Kernel Function

In order to meet the development of shock absorber on-line detection, a new method of indicator diagrams recognition for shock absorber based on support vector machine (SVM) is proposed. Different fault patterns of shock absorber indicator diagram are discussed, including their main causes. The recognition model is constructed each with Linear, Polynomial and Radial Basis Function (RBF) kernel function. The experimental results show that the best average recognition rate is 96.4%. This method is effective in indicator diagram fault recognition of shock absorber.

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