Fault Diagnosis of Planetary Gearboxes Based on Improved EEMD and Frequency Demodulation Analysis

2018 ◽  
pp. 137-147
Author(s):  
Haohua Liu ◽  
Fangyi Li ◽  
Feng Yang ◽  
Yanyan Nie ◽  
Liming Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Frequency Demodulation ◽  
Improved Eemd

scholarly journals Wind Turbine Gearbox Fault Diagnosis Based on Improved EEMD and Hilbert Square Demodulation

2017 ◽  
Vol 7 (2) ◽  
pp. 128 ◽  
Author(s):  
Huanguo Chen ◽  
Pei Chen ◽  
Wenhua Chen ◽  
Chuanyu Wu ◽  
Jianmin Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbine Gearbox ◽  
Improved Eemd

Fault Diagnosis for Rolling Bearing Based on Genetic-SVM Classifier

2011 ◽  
Vol 199-200 ◽  
pp. 620-624 ◽  
Author(s):  
Yun Jie Xu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Support Vector ◽  
Svm Classifier ◽  
Diagnostic Model ◽  
Roller Bearings ◽  
Study Support ◽  
Frequency Demodulation

Fault diagnosis of roller bearings is very complex, so it is difficult to use the mathematical model to describe their faults. Whose developmental changes have dual trends of increase and fluctuation. In this study, support vector machine trained by genetic algorithm based on high frequency demodulation analysis is proposed to fault diagnosis of ball bearing. Genetic algorithm is used to determine training parameters of support vector machine in this model, which can optimize the support vector machine (SVM) an intelligent diagnostic model. The performance of the GSVM system proposed in this study is evaluated by roller bearings in the wood-wool production device. The experimental results indicate that the proposed support vector machine trained by genetic algorithm has good diagnosis results in the application.

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