Research on Power System Fault Diagnosis Based on Bayesian Networks

2013 ◽  
Vol 347-350 ◽  
pp. 1930-1934 ◽  
Author(s):  
Guo Feng Yang ◽  
Qing Ming Xiao ◽  
Hong Ouyang ◽  
Jia Kui Zhao ◽  
Ting Shun Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Structure Learning ◽  
Parameter Learning ◽  
Conditional Probability Table ◽  
Probability Table ◽  
Diagnosis Model ◽  
Diagnosis Approach

Aiming at the incompleteness and uncertainty of information existing in power system fault diagnosis, a new fault diagnosis approach based on Bayesian network is proposed in this paper. Through the Bayesian network of structure learning and parameter learning, a power system fault diagnosis model based on Bayesian network has been proposed. Conditional probability table describes the connection degree between various factors in quantity. Diagnostic results of instance proved the effectiveness and superiority of the proposed method.

scholarly journals Information Value-Based Fault Diagnosis of Train Door System under Multiple Operating Conditions

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3952
Author(s):  
Seokgoo Kim ◽  
Nam Ho Kim ◽  
Joo-Ho Choi
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Fault Isolation ◽  
Information Value ◽  
Operating Conditions ◽  
Data Driven ◽  
Conditional Probability Table ◽  
Probability Table ◽  
Operating Regimes

While there are many data-driven diagnosis algorithms for fault isolation of complex systems, a new challenge arises in the case of multiple operating regimes. In this case, the diagnosis is usually carried out for each regime for better accuracy. However, the problem is that different results can be derived from each regime and they can conflict with each other, which may invalidate the performance of fault diagnosis. To address this challenge, a methodology for selecting the most reliable one among the different diagnostic results is proposed, which combines the Bayesian network (BN) and the information value (IV). The BN is trained for each regime and a conditional probability table is obtained for probabilistic fault diagnosis. The IV is then employed to evaluate the value of several diagnostic results. The proposed approach is applied to the fault diagnosis of a train door system and its effectiveness is proven.

The Intelligent Fault Diagnosis Based on Bayesian Network Inference Algorithm Solution

2015 ◽  
Vol 727-728 ◽  
pp. 884-887 ◽  
Author(s):  
Hao Wang ◽  
Sha Sha
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Wireless Network ◽  
Data Transmission ◽  
Network Inference ◽  
Inference Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Fault Diagnosis System ◽  
Diagnosis Model ◽  
Bayesian Network Inference

In this thesis a remote intelligent fault diagnosis system based on wireless network is put forward. A method of data-transmission based on wireless network is analyzed and a scheme for intelligent fault diagnosis on the basis of Bayesian network is laid out.This thesis also researches on how to build a fault diagnosis model taking advantage of Bayesian network and how to improve the system's resolving ability and reasoning quality in the process of intelligent fault diagnosis.

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