One Fault Diagnosis Method Based on Fuzzy Equal Relationship and Rough Set Theory

2014 ◽  
Vol 631-632 ◽  
pp. 175-179
Author(s):  
Si Jie Yang ◽  
Jing Hui Li ◽  
Xiao Ni Liu
Keyword(s):  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Clustering ◽  
Rough Set Theory ◽  
Relationship Matrix ◽  
Reduction Algorithm ◽  
Diagnosis Method

In order to process the abundant information in fuzzy clustering, one fault diagnosis method was proposed based on Rough Set reduction algorithm and Fuzzy equal relationship clustering. Not only the iteration numbers was reduced in the fuzzy equal relationship matrix, but also the clustering numbers was lower. Then the examples were applied to test its validity.

Binary Resolution Matrix Based Incremental Attribute Reduction for Fault Diagnosis

2016 ◽  
Vol 693 ◽  
pp. 1346-1349
Author(s):  
Xiao Yu Chen ◽  
Wen Liao Du ◽  
An Sheng Li ◽  
Kun Li ◽  
Chun Hua Qian
Keyword(s):  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Rotating Machinery ◽  
Reduction Algorithm

Rough set theory is a useful tool for attribute reduction of fault diagnosis for rotating machinery, but cannot be efficiently used to sample increased areas. Aiming at the problem of incremental attribute reduction, a novel attribute reduction algorithm was put forward based on the binary resolution matrix for the two updating situations and the algorithm had a low space complex. Finally, with the fault diagnosis experiments of the bearing, the attribute reduction method was proved to be correct.

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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