Proceedings of the Korean Society for Noise and Vibration Engineering Conference (한국소음진동공학회:학술대회논문집)
- 2003.11a
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- Pages.537-543
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- 2003
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- 1598-2548(pISSN)
Fault diagnosis of rotating machinery using multi-class support vector machines
Multi-class SVM을 이용한 회전기계의 결함 진단
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 vibration 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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