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
2011 ◽  
Vol 130-134 ◽  
pp. 1681-1685 ◽  
Author(s):  
Guang Tian ◽  
Hao Tian ◽  
Guang Sheng Liu ◽  
Jin Hui Zhao ◽  
Li Ping Luo

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.


2014 ◽  
Vol 1049-1050 ◽  
pp. 665-668
Author(s):  
Hong Li Lv

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.


2012 ◽  
Vol 433-440 ◽  
pp. 6319-6324 ◽  
Author(s):  
Hai Ying Kang ◽  
Ren Fa Shen ◽  
Yan Jie Qi ◽  
Wen Yan ◽  
Hai Qi Zheng

The diagnosis of compound-fault is always a difficult point, and there is not an effective method in equipment diagnosis field. Rough set theory is a relatively new soft computing tool to deal with vagueness and uncertainty. Condition attribute reduce algorithm is the key point of rough set research. However, it has been proved that finding the best reduction is the NP-hard problem. For the purpose of getting the reduction of systems effectively, an improved algorithm is put forward. The worst Fisher criterion was adopted as heuristic information to improve the searching efficiency and Max-Min Ant System was selected. Simplify the fault diagnosis decision table, then clear and concise decision rules can be obtained by rough sets theory. This method raises the accuracy and efficiency of fault diagnosis of bearing greatly.


2012 ◽  
Vol 241-244 ◽  
pp. 405-409 ◽  
Author(s):  
Yan Qin Su ◽  
Ji Hong Cheng ◽  
Ting Xue Xu

There is the advantage of Rough Sets Theory for redundant condition reduction and D-S Theory for combination rules reasoning, one fusion approach based on the two theories was given. Firstly, the test data was discretizated and attribution reduced to get the reduction decision table. Then, the basic probability assignment was gotten through calculating the condition attribution of the decision table while the condition attribution was regarded as evidence input and the decision attribution as discernment frame. Finally, the evidence was combination reasoned and the fault diagnosis results were gotten, and the application example was verified its validity.


2012 ◽  
Vol 490-495 ◽  
pp. 1226-1230
Author(s):  
Yan Qin Su ◽  
Shan Gao ◽  
Ting Xue Xu

There are redundant, incomplete and incorrect data in the equipment test data gained by the test equipments. The complete algorithms and attribution reduction algorithms were analyzed and the equipment fault diagnosis model based on Rough Sets Theory was given. Then, some equipment was diagnosed, and the results indicate that the diagnosis is effective and efficient.


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