A New Power System Fault Diagnosis Method Based on Rough Set Theory and Quantum Neural Network

2009 ◽  
Author(s):  
Zhengyou He ◽  
Jing Zhao ◽  
Jianwei Yang ◽  
Wei Gao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power System ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Quantum Neural Network ◽  
Diagnosis Method

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.

Research on Fault Diagnosis Method Based on CBR and Rough Set Theory

2012 ◽  
Vol 170-173 ◽  
pp. 3644-3648
Author(s):  
Chun Fei Yuan ◽  
Jing Cai ◽  
Yi Ming Xu
Keyword(s):  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Weighting Coefficient ◽  
Case Based Reasoning ◽  
Diagnosis Method ◽  
Maintenance Work ◽  
Fault Diagnosis System ◽  
Case Based

Modern fault diagnosis system always is a dynamic, flexible and uncertain complicated system, so many fault diagnosis methods are not effective to determine fault causes. Considering that abundant of fault diagnosis cases have been accumulated in daily maintenance work, a fault diagnosis method based on case-based reasoning (CBR) and rough set theory is proposed. Rough set theory is employed to process reduction on attributes and the weighting coefficient of case description attributes. This method makes full use of the advantage of" let the data speak". At last the method is testified by an example, and the result shows it is feasible and effective.

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