Research on Extracting Vehicle Fault Symptoms Based on Rough Sets Theory in Mechanical Engineering

2013 ◽  
Vol 644 ◽  
pp. 110-114
Author(s):  
Chang Shun Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Network Theory ◽  
Rough Sets ◽  
Fuzzy Theory ◽  
Rough Sets Theory ◽  
Combined Application ◽  
Neural Network Theory ◽  
Sets Theory

This paper gives a brief introduction of rough sets theory. Taking the fault diagnosis of the diesel engine valve clearance for example, it discusses about the basic algorithms and methods to extract fault symptoms based on rough sets theory. The conclusion shows the possibility to develop intelligent fault diagnosis with the combined application of fuzzy theory and neural network theory.

Study on Fault Diagnosis of Power Transformer with Reduction Method of Attribute Significance

2014 ◽  
Vol 1049-1050 ◽  
pp. 665-668
Author(s):  
Hong Li Lv
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rough Sets ◽  
Power Transformer ◽  
Reduction Algorithm ◽  
Rough Sets Theory ◽  
Fault Diagnosis System ◽  
Transformer Fault ◽  
Sets Theory ◽  
Quality Diagnosis

This paper studies the power transformer fault quality diagnosis using rough sets theory and neural network. It is rough sets reduction as the pre-unit of neural network based on reduction algorithm with the attribute significance. The paper describes the reduction algorithm and implementation method detailed. Through the training and testing results with practical data, it is proved that the reduction algorithm with the attribute significance can make the number of input samples shorter, the training speed faster and the diagnostic accuracy higher. The algorithm is feasible and effective for applying to the fault diagnosis system of power transformer.

Compound-Fault Diagnosis of Bearing Based on Order Tracking Wavelet Packet and Rough Sets

2011 ◽  
Vol 130-134 ◽  
pp. 1681-1685 ◽  
Author(s):  
Guang Tian ◽  
Hao Tian ◽  
Guang Sheng Liu ◽  
Jin Hui Zhao ◽  
Li Ping Luo
Keyword(s):  
Fault Diagnosis ◽  
Rough Sets ◽  
Wavelet Packet ◽  
Decision Rules ◽  
Vibration Signals ◽  
Rough Sets Theory ◽  
Equal Angle ◽  
Order Tracking ◽  
Start Up ◽  
Sets Theory

The diagnosis of compound-fault is always a difficult point, and there is not an effective method in equipment diagnosis field, then a new method of compound-fault diagnosis was presented. The vibration signals at start-up in the gearbox are non-stationary signals, and traditional ways of diagnosis have low precision. Order tracking and wavelet packet and rough sets theory are introduced in the compound-fault diagnosis of bearing. First, the vibration signals at start-up were resampled using computer order tracking arithmetic and equal angle distributed vibration signals were obtained, and wavelet packet has been used for equal angle distributed vibration signals decomposition and reconstruction. Then, energy distribution of every frequency band can be calculated according to normalization process. A new feature vector can be obtained, then clear and concise decision rules can be obtained by rough sets theory. Finally, the result of compound-fault example proves that the proposed method has high validity and more amplitude appliance foreground.

Fault diagnosis using Rough Sets Theory

2000 ◽  
Vol 43 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Lixiang Shen ◽  
Francis E.H. Tay ◽  
Liangsheng Qu ◽  
Yudi Shen
Keyword(s):  
Fault Diagnosis ◽  
Rough Sets ◽  
Rough Sets Theory ◽  
Sets Theory

DECISION MAKING USING HYBRID ROUGH SETS AND NEURAL NETWORKS

2002 ◽  
Vol 12 (06) ◽  
pp. 435-446 ◽  
Author(s):  
YASSER HASSAN ◽  
EIICHIRO TAZAKI ◽  
SHIN EGAWA ◽  
KAZUHO SUYAMA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Rough Sets ◽  
Decision Problem ◽  
Combined System ◽  
Network Systems ◽  
Rough Sets Theory ◽  
Sets Theory

A methodology for using rough sets theory for preference modeling in decision problem is presented in this paper. We will introduce a new method where neural network systems and rough sets theory are completely integrated into a hybrid system and are used cooperatively for decision and classification support. At the first glance, the two methods we discuss have not much in common. But, in spite of the differences between them, it is interesting to try to incorporate both into one combined system, and apply it in the building of a decision support system.

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