Application of Variable Precision Rough Set and Integrated Neural Network to Bearing Fault Diagnosis

2013 ◽  
Vol 373-375 ◽  
pp. 1060-1063
Author(s):  
Xiao Ling Niu ◽  
Bo Liu ◽  
Ke Zhang Lin
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rough Set ◽  
Rough Set Theory ◽  
Optimal Decision ◽  
Design System ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Variable Precision ◽  
Variable Precision Rough Set

The integration of variable precision rough set and neural network is introduced into the bearing fault diagnosis. VPRS-INN fault diagnosis method is proposed: First, utilize the information entropy method for discretization of continuous attributes, and then use attribute dependence degree of the variable precision rough set theory for heuristic reduction. based on the reduction, obtain the optimal decision support system. Finally according to the optimal design system, we design a integrated neural network for fault diagnosis. instances have proved the feasibility and high fault diagnosis rate of the method.

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.

Fault Diagnosis Based on Rough Set and BP Neural Network (RS-BP) for Gas Turbine Engine

2013 ◽  
Vol 732-733 ◽  
pp. 397-401 ◽  
Author(s):  
Ning Bo Zhao ◽  
Shu Ying Li ◽  
Shuang Yi ◽  
Yun Peng Cao ◽  
Zhi Tao Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Rough Set ◽  
Bp Neural Network ◽  
Rough Set Theory ◽  
Original Data ◽  
Optimal Decision ◽  
Identification Rate ◽  
Diagnosis Model

This paper presents a new fusion diagnosis based on rough set and BP neural network for the fault diagnosis of gas turbine. The frame is designed to fusion fault diagnosis, which is composed by three parts: the rough set data pre-processor, rough set diagnosis model and BP neural network diagnosis model. Aiming at the difficulty in getting adequate fault samples in fault diagnosis, rough set theory is first used to process the original data, establish the decision table and generate rules, which can eliminate the redundant information and build the rough set diagnosis model. After that, according to the optimal decision attribute pre-treated by rough set, BP neural network is designed for fault diagnosis, which can reduce the scale of neural network, improve the identification rate, and improve the efficiency of the whole fusion diagnosis system. Finally, an example of gas turbine generator sets fuel system is taken as a case study to demonstrate the feasibility and validity of the proposed method in this paper.

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