Power Transformer Fault Diagnosis Based on Rough Set Theory and Support Vector Machine

2007 ◽  
Author(s):  
Wenqing Zhao ◽  
Yongli Zhu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Power Transformer ◽  
Support Vector ◽  
Transformer Fault

Fault Diagnosis of Drilling Process Based on Rough Set and Support Vector Machine

2013 ◽  
Vol 709 ◽  
pp. 266-272
Author(s):  
Xiao Liang Wang ◽  
Xiao Yuan Lian ◽  
Lu Yao
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Support Vector ◽  
Drilling Process ◽  
Time Varying ◽  
Large Samples ◽  
Main Factors

Drilling process is a complicated system with characteristics of uncertainty, fuzziness and time-varying. A new way of the fault diagnosis based on RS-SVM (Rough Set and Support Vector Machine) was proposed in this paper. The related engineering factors were reduced by Rough Set theory and the main factors of the drilling process were obtained. Then the Support Vector Machine was used to establish the diagnosis models, and then the problems that the traditional SVM cannot deal with dynamic data and are prone to dimension disasters with large samples were avoided. The application in Ha35 well, Liao He Oilfield indicates that the system can diagnose the type of faults quickly and accurately. So the method can be used to diagnose the drilling process.

PREDICTING FINANCIAL DISTRESS OF CHINESE LISTED COMPANIES USING ROUGH SET THEORY AND SUPPORT VECTOR MACHINE

2011 ◽  
Vol 28 (01) ◽  
pp. 95-109 ◽  
Author(s):  
YU CAO ◽  
GUANGYU WAN ◽  
FUQIANG WANG
Keyword(s):  
Support Vector Machine ◽  
Set Theory ◽  
Rough Set ◽  
Financial Distress ◽  
Rough Set Theory ◽  
Listed Companies ◽  
Support Vector ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Chinese Listed Companies

Effectively predicting corporate financial distress is an important and challenging issue for companies. The research aims at predicting financial distress using the integrated model of rough set theory (RST) and support vector machine (SVM), in order to find a better early warning method and enhance the prediction accuracy. After several comparative experiments with the dataset of Chinese listed companies, rough set theory is proved to be an effective approach for reducing redundant information. Our results indicate that the SVM performs better than the BPNN when they are used for corporate financial distress prediction.

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