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Induction Machine Rotor Diagnosis Using Support Vector Machines and Rough Set

  • Ruiming Fang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

A fault diagnosis system based on integration of rough set theory (RST) and support vector machine (SVM) is developed for induction machine rotor faults detection. The proposed algorithm uses the stator current spectrum as inputs. By RST attribute reduction, redundant attributes are identified and removed. Then the reduction results are used as the input of SVM based classifiers to distinguish different motor conditions. A series of experiments using a three phase 1.5KW induction machine performed in different conditions are used to provide training and test data. The diagnosis results demonstrated that the solution can reduce the cost and raise the efficiency of the diagnosis.

Keywords

Support Vector Machine Fault Diagnosis Induction Machine Fault Diagnosis System Static Eccentricity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ruiming Fang
    • 1
  1. 1.Colleage of Information Science and Engineering, National Huaqiao University, Quanzhou City, Fujian Province, 362021China

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