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Multi-class SVM을 이용한 회전기계의 결함 진단
황원우(Hwang, Won-Woo),양보석(Yang, Bo-Suk) 한국소음진동공학회 2004 한국소음진동공학회 논문집 Vol.14 No.12
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.