인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
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.
초록·키워드
Purpose: Gear teeth in the rotary geared actuator of the unmanned aerial vehicle, experience crack propagation because of its harsh operating conditions. To prevent the failure of catastrophic events, this study proposes a diagnostic approach for various gear crack levels based on the built-in and add-on sensor signal.
Methods: A downsized planetary gearbox test rig was prepared, in which the motor position, current, and vibration signals were acquired for the normal and 4 different crack-induced states. Signals were filtered around the region of resonance frequencies by spectral kurtosis and the features for the health state were extracted. Then, feature selection was conducted based on the correlation with fault levels. Finally, the Artificial Neural Network (ANN) model was constructed to identify different fault sizes of the cracks, and K-fold validation was adopted to optimize the parameters of the ANN model.
Results: Among the various signals, the vibration from the add-on sensor and a position from the built-in sensor exhibited high performance compared to the current signal. The features after band-pass filtering yielded a high correlation with fault severity.
Conclusion: The proposed method successfully diagnosed different fault severities of gear cracks in the planetary gearbox by using both the built-in and add-on signals.
Methods: A downsized planetary gearbox test rig was prepared, in which the motor position, current, and vibration signals were acquired for the normal and 4 different crack-induced states. Signals were filtered around the region of resonance frequencies by spectral kurtosis and the features for the health state were extracted. Then, feature selection was conducted based on the correlation with fault levels. Finally, the Artificial Neural Network (ANN) model was constructed to identify different fault sizes of the cracks, and K-fold validation was adopted to optimize the parameters of the ANN model.
Results: Among the various signals, the vibration from the add-on sensor and a position from the built-in sensor exhibited high performance compared to the current signal. The features after band-pass filtering yielded a high correlation with fault severity.
Conclusion: The proposed method successfully diagnosed different fault severities of gear cracks in the planetary gearbox by using both the built-in and add-on signals.
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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