Fault Diagnosis of Rotating Machinery Using Data Mining and CLIPS-Based Expert System

Author(s):  
Dongyang Dou ◽  
Yingkai Zhao
2011 ◽  
Vol 460-461 ◽  
pp. 821-826
Author(s):  
Yun Feng Lin ◽  
Xiao Ping Hu

This article first introduced the survey of mechanical fault diagnosis technology development and the data mining technology theory. Then its application situation at present and the main questions that exist are elaborated. Its development trend is analyzed. The application feasibility of using data mining technology in mechanical fault diagnosis is discussed. Next, the naissance, the development and the future development tendency of data mining technology are introduced. The four algorithms are analyzed and the framework is built too. Intelligent Diagnosis is a major development direction of the fault diagnosis. Knowledge acquisition is the bottleneck of Intelligent Diagnosis development. It comprehensive use of many kinds of advanced technology, discover valuable and hidden knowledge from the large amounts of data mining.


1998 ◽  
Vol 14 (1-2) ◽  
pp. 37-42 ◽  
Author(s):  
Manuel Mejía-Lavalle ◽  
Guillermo Rodríguez-Ortiz

IJARCCE ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 106-108
Author(s):  
Suhas A Bhyratae ◽  
Sumukha J Sharma ◽  
Tarun Kumar K

2008 ◽  
Vol 07 (01) ◽  
pp. 41-44
Author(s):  
PING CHEN ◽  
ZHIJIANG XIE

The knowledge representation of multi-symptom fuzzy production rules based on machinery configuration model, and the establishment and maintenance mechanism of knowledge base based on relational database are studied in the paper. With the support of ADO technique, the access to knowledge base and fault reasoning are realized. Application shows that the expert system has the merits of being simple to construct and of high reasoning efficiency. And, the adaptability and universality of fault diagnosis expert system to rotate machinery are greatly increased.


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