Large Rotating Machinery Fault Diagnosis and Knowledge Rules Acquiring Based on Improved RIPPER

Author

Jiang´Hong, Sun ; Xiao´Li, Xu

Author_Institution

Sch. of Electromech. Eng., Beijing Inf. Sci. & Technol. Univ., Beijing, China

Volume

2

fYear

2009

fDate

10-11 Oct. 2009

Firstpage

549

Lastpage

552

Abstract

The data of fault monitoring for large rotating machine are large and noisy, there are some relationships between properties and property values, which are coincide with the rules of data mining technology. The data mining technology was studied in order to obtain the laws and classify the faults. The improved RIPPER (Repeated Incremental Pruning to Produce Error Reduction) data mining rule learning algorithm was studied for large rotating machine, the rules set files were obtained by analyzing the fault samples and updated in time. The extracted knowledge rules could also be used as the real time diagnosis of common faults.

Keywords

data mining; electric machine analysis computing; electric machines; fault diagnosis; learning (artificial intelligence); RIPPER; data mining; fault monitoring; knowledge rules; large rotating machinery fault diagnosis; learning algorithm; real time diagnosis; repeated incremental pruning to produce error reduction; Condition monitoring; Data engineering; Data mining; Electronic mail; Fault diagnosis; Information science; Libraries; Machine intelligence; Machinery; Rotating machines; data mining; fault monitoring; improved RIPPER; large rotating machine; real time diagnosis;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on

Conference_Location

Changsha, Hunan

Print_ISBN

978-0-7695-3804-4

Type

conf

DOI

10.1109/ICICTA.2009.367

Filename

5287739