A Study on the Condition-Based Monitoring of Rivetin Electric Doors using SVM
(주)코리아스칼라
- 최초 등록일
- 2023.08.28
- 최종 저작일
- 2023.06
- 7페이지/ 어도비 PDF
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서지정보
ㆍ발행기관 : 한국기계기술학회
ㆍ수록지정보 : 한국기계기술학회지 / 25권 / 3호
ㆍ저자명 : 김준우, 박성천
영어 초록
Electric doors have been applied in urban trains since 2007 and operated for a long time. Recently, the failure of mechanical devices in electric doors have been increasing. The door is a device that is directly related to the safety of passengers. The rivet breakage of a ball/nut assembly may occur to an accident during train operation. In this study, the operating voltage and acceleration data of the door were collected for rivet condition monitoring, and 4 features were extracted in the frequency domain using the acceleration data. The classification performance of the rivet condition according to the axial direction of the acceleration data and 4 kernel functions was evaluated using SVM algorithm. When the X-axis data and Gaussian kernel function were used, the highest classification performance was shown for the electric door’s rivet with 90% accuracy.
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