인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This study proposes a vibration signal-based fault classification algorithm for the vacuum circuit breaker (VCB) of cubicle type gas-insulated switchgear (C-GIS), considering both sensor type and installation location. Vibration impacts generated during close and open operations are detected, transformed via FFT, and only the low-frequency components are used as input to a fully connected (FC) classifier. Compared with prior models such as 1D CNN, ResNet, and ResNet+DSC, the proposed FFT+FC approach achieved the highest classification accuracy when using an appropriate sensor type that does not suffer from overload and placing it at the optimal location determined by the Optimal Sensor Placement (OSP) algorithm. Experiments demonstrated that sensor type and placement have a substantial influence on the performance of FFT+FC, with overload, caused by the limited measurement range of certain sensor, leading to waveform distortion and accuracy degradation. These findings underscore the importance of both sensor selection and placement strategy in achieving reliable C-GIS fault diagnosis. Focusing on model type of fault classifier, using datasets collected from two structurally different C-GIS units, the results confirmed that the FFT+FC model not only provides competitive classification accuracy under optimal conditions but also requires significantly lower activation memory than convolution based models, making it suitable for resource limited industrial applications.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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