Power System Fault Diagnosis Method Summary

2014 ◽  
Vol 670-671 ◽  
pp. 1179-1183
Author(s):  
Yu Zhao ◽  
Wei Xiong ◽  
Huang Qiang Li ◽  
Shi Yong Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Expert System ◽  
Power System ◽  
Bayesian Network ◽  
Current Situation ◽  
Advantages And Disadvantages ◽  
Diagnosis Method ◽  
Network Fault

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.

Fault Diagnosis Method of Vehicle Power System Using Bayesian Network

2014 ◽  
Vol 556-562 ◽  
pp. 3134-3138
Author(s):  
He Jia Li ◽  
Yan Wei Cheng ◽  
Cheng Yao ◽  
Hai Feng Xu ◽  
Zhao Yao ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Bayesian Network ◽  
Graph Model ◽  
Small Sample ◽  
Uncertain Information ◽  
Multiple Faults ◽  
Diagnosis Method ◽  
Mutual Relations ◽  
Network Fault

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.

Orbit Shape Automatic Recognition Based on Artificial Neural Network

2005 ◽  
Author(s):  
Dongmei Du ◽  
Qing He
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Expert System ◽  
Recognition Performance ◽  
Shape Recognition ◽  
Vibration Monitoring ◽  
Moment Invariants ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Orbit is a significant symptom in the fault diagnosis of rotating machine. The orbit is a 2-D image and can be described by moment invariants, the shape property of 2-D image, which is a description with translating-, rotating-, and scaling-invariants for 2-D image. The descriptive method of orbit image is investigated and an automatic orbit shape recognition based on artificial neural network (ANN) with moment invariants is proposed in this paper. The ANN of orbit shape recognition is trained by the training patterns generated by computer simulation for plenty of orbit shapes. It is shown that the trained ANN is of good recognition performance and generalization capability when applied to recognition of the measured orbits. This method can be used to the intelligent expert system of fault diagnosis to obtain automatically online orbit symptom in shafts vibration monitoring of turbine generator, which will improve the automatization of obtaining fault symptom and the automatic diagnosis in the expert system.

scholarly journals Research on Fault Diagnosis Method Based on Rule Base Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Zheng Ni ◽  
Zhang Lin ◽  
Wang Wenfeng ◽  
Zhang Bo ◽  
Liu Yongjin ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Location ◽  
Fuzzy Theory ◽  
Learning Rule ◽  
Rule Base ◽  
Fuzzy Neural ◽  
Diagnosis Method ◽  
Construction Model ◽  
Network Fault

The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

scholarly journals Fault Diagnosis of a Hydraulic Power System Using an Artificial Neural Network

2006 ◽  
Vol 17 (1) ◽  
pp. 115-136 ◽  
Author(s):  
Ahemd El-Betar ◽  
Magdy Abdelhamed ◽  
Ahmed El-Assal ◽  
Roubi Abdelsatar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Power System ◽  
Hydraulic Power ◽  
Artificial Neural
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