A fault diagnosis modeling method combined RBF neural network with rough set theory

2009 ◽  
Author(s):  
Zhou Liuyang ◽  
Shi Yuwen ◽  
Tang Pengcheng ◽  
Zhang Hui
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Rbf Neural Network ◽  
Modeling Method

A Rough-Fuzzy RBF Neural Network Based on PSO Algorithm

2013 ◽  
Vol 710 ◽  
pp. 617-622
Author(s):  
Jing Zhao
Keyword(s):  
Neural Network ◽  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Training Sample ◽  
Network Training ◽  
Acquisition Method

A Rough-Fuzzy RBF Neural Network was raised based on PSO Algorithm. In this model,gives a knowledge acquisition method that based on rough set theory,the Rough-Fuzzy RBF neural network are constructed according to the results of the knowledge acquisition,the PSO are used to optimize the network parameters.This paper take number plate for example to conduct a simulation experiment.The results shows that the model can simplify the network training sample,optimize the network structure and enhance the systems study efficiency and the precision.

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 Application of Tolerant Rough Set Neural Network to Fighter Fault Diagnosis

2013 ◽  
Vol 341-342 ◽  
pp. 809-814
Author(s):  
Guo Qiang Sun ◽  
Hong Li Wang ◽  
Jun Tao ◽  
Xu Bing Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Useful Knowledge ◽  
The Neural Network ◽  
Catch Up ◽  
Simulation Results ◽  
Adaptive Ability

Conventional rough set theory is based on indiscernibility relation, which lacks the adaptive ability to data noise or data missing. Furthermore, it may present qualitatively whether or not the faults exist, but it cant compute accurately the value of the faults. Though the neural network has ability of approximating unknown nonlinear systems, but it cant distinguish the redundant knowledge from useful knowledge, so its classification ability cant catch up with the rough set classifier. This paper combines the rough set theory and the tolerant rough set neural network to diagnose the rudder faults of fighter, which solves well the problem of fault diagnosis and fault degree computation. Simulation results demonstrate the effectiveness of the proposed method.

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