Transactions of the Korean Society for Noise and Vibration Engineering (한국소음진동공학회논문집)

The Korean Society for Noise and Vibration Engineering (한국소음진동공학회)
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Fault Diagnosis of Rotating Machinery Using Multi-class Support Vector Machines

Multi-class SVM을 이용한 회전기계의 결함 진단

  • 황원우 (부경대학교 대학원 기계공학부) ;
  • 양보석 (부경대학교 기계공학부)
  • Published : 2004.12.01
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Abstract

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the nitration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

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References

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