Fault Diagnosis of High Voltage Circuit Breaker Based on Multi-classification Relevance Vector Machine

2019 ◽  
Vol 15 (1) ◽  
pp. 413-420 ◽  
Author(s):  
Yingjie Zhang ◽  
Yuan Jiang ◽  
Yan Chen ◽  
Ying Zhang
Keyword(s):  
Fault Diagnosis ◽  
High Voltage ◽  
Circuit Breaker ◽  
Relevance Vector Machine ◽  
High Voltage Circuit Breaker ◽  
Multi Classification

Extreme Learning Machine in the Breaker Fault Diagnosis

2014 ◽  
Vol 960-961 ◽  
pp. 896-899
Author(s):  
Dan Jiang ◽  
Shu Tao Zhao ◽  
Jian Feng Ren ◽  
Yu Tao Xu
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
High Voltage ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
High Voltage Circuit Breaker ◽  
Vibration Signal Analysis ◽  
Learning Machine ◽  
Hidden Layer ◽  
New Diagnosis

In order to improve the diagnosis method of the existing high-voltage circuit breaker fault, demonstrated a new diagnosis methord of mechanical failure of high voltage circuit breaker based on vibration signal. According to the factors of high voltage circuit breaker failure and the features of Single-hidden Layer Feedforward Neural Network, SLFN, a method of high voltage circuit breaker fault diagnosis proposed based on Extreme Learning Machine (ELM). Finally, the experiment proves the effectiveness of this method for breaker fault diagnosis based on vibration signal analysis and ELM.

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