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Automatic rule learning using roughset for fuzzy classifier in fault categorization of mono-block centrifugal pump
Sakthivel N.R, , Nair B.B.
Published in Elsevier BV
Volume: 12
Issue: 1
Pages: 196 - 203
Rule learning based approach to fault detection and diagnosis is becoming very popular, mainly due to their high accuracy when compared to older statistical methods. Fault detection and diagnosis of various mechanical components of centrifugal pump is essential to increase the productivity and reduce the breakdowns. This paper presents the use of rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a centrifugal pump. A fuzzy inference system (FIS) is built using rough set rules and tested using test data. The effect of different types of membership functions on the FIS performance is also presented. Finally, the performance of this classifier is compared to that of a fuzzy-antminer classifier and to multi-layer perceptron (MLP) based classifiers. © 2011 Elsevier B.V. All rights reserved.
About the journal
JournalData powered by TypesetApplied Soft Computing
PublisherData powered by TypesetElsevier BV
Open AccessNo