Fault Diagnosis Method of Power System Based on Bayesian Network

Author(s):  
Zheng Li ◽  
Qingyang Liu ◽  
Chenxing Na ◽  
Yannian Wu ◽  
Zhiying Tang
2014 ◽  
Vol 670-671 ◽  
pp. 1179-1183
Author(s):  
Yu Zhao ◽  
Wei Xiong ◽  
Huang Qiang Li ◽  
Shi Yong Yang

Combined with the power system fault diagnosis current situation, fault diagnosis methods are important to shorten fault outage time, prevent accident expanding and restore power quickly. We summed up expert system, artificial neural network, Petri network and Bayesian network fault diagnosis methods. The diagnosis principle, advantages and disadvantages of different methods were discussed.


2014 ◽  
Vol 556-562 ◽  
pp. 3134-3138
Author(s):  
He Jia Li ◽  
Yan Wei Cheng ◽  
Cheng Yao ◽  
Hai Feng Xu ◽  
Zhao Yao ◽  
...  

The fault diagnosis of vehicle power system that the structure and characteristics of components are complex, each module and internal modules exist coupling, cross-linked mutual relations and the uncertainties, the system status and working conditions are difficult to describe by precisely mathematical model, and test cost expensive, less fault samples. Thus its fault diagnosis is the decision problem of uncertain information in a small sample. it is proposed that combining multi-signal flow graph model with Bayesian network fault diagnosis method. The fault diagnosis model of power system and the corresponding Bayesian network structure are built, which achieve the fault diagnosis of power system, Diagnosis example shows that the method of the vehicle power has a higher failure troubleshooting capabilities of the system single and multiple faults.


2013 ◽  
Vol 470 ◽  
pp. 683-688
Author(s):  
Hai Yang Jiang ◽  
Hua Qing Wang ◽  
Peng Chen

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.


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