Power Transformer Fault Diagnosis Based on Integrated of Rough Set Theory and Neural Network

2012 ◽  
Author(s):  
Ai-hua Zhou ◽  
Hong Song ◽  
Hui Xiao ◽  
Xiao-hui Zeng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Power Transformer ◽  
Transformer Fault

Application of Variable Precision Rough Set in Power Transformer Fault Diagnosis

2013 ◽  
Vol 373-375 ◽  
pp. 824-828
Author(s):  
Shu Chuan Gan ◽  
Ai Hua Zhou ◽  
Hui Guo ◽  
Ling Tang
Keyword(s):  
Fault Diagnosis ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Power Transformer ◽  
Fault Classification ◽  
Hidden Information ◽  
Variable Precision ◽  
Variable Precision Rough Set ◽  
Transformer Fault

The variable precision rough set theory is introduced into the fault diagnosis of power transformer. Using the reduction method of the variable precision rough set,the hidden information in power transformer faults data is reduced , and the information which plays a major role in fault classification can be obtained. This approach can overcome the defects of the classical rough set, such as the sensitivity to noise of input information, and accordingly improves the accuracy of fault diagnosis. The example shows that the variable precision rough set used in the power transformer fault diagnosis, enhance the robustness of the data analysis and processing, so, the proposed approach has a more effective diagnostic performance.

Diesel Engine Fault Diagnosis Based on Rough Sets

2013 ◽  
Vol 722 ◽  
pp. 276-281
Author(s):  
Hong Xia Pan ◽  
Jing Yi Tian
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Set Theory ◽  
Rough Set ◽  
Cylinder Head ◽  
Rough Set Theory ◽  
Vibration Signal ◽  
Fault Classification ◽  
The Neural Network

This paper introduces the rough set theory and ROSETTA software characteristics, gives a diesel engine fault diagnosis system based on rough set theory and the vibration signal of cylinder head. Taking a certain type large power diesel engine as an example, the first to be extracted from the cylinder head vibration signal wavelet packet de-noising and time-frequency domain analysis, constructed eigenvalue for fault diagnosis, then use ROSETTA software reduction feature attributes, finally completed fault pattern classification through the neural network. By comparing the output results of the neural network before and after processing by the ROSETTA software, show that rough set theory can optimize the feature attributes, effectively reduce the input of the neural network nodes, and improve the fault classification accuracy.

The fault diagnosis of power transformer based on rough set theory

2008 ◽  
Author(s):  
Fu Ying-shuan ◽  
Liu Fa-zhan ◽  
Zhang Wei-zheng ◽  
Zhang Qing ◽  
Zhang Gui-xin
Keyword(s):  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Power Transformer
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