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.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2026.6
- 수록면
- 760 - 767 (8page)
- DOI
- 10.5302/J.ICROS.2026.26.0037
이용수
초록· 키워드
Unmanned systems operating in extreme and unstructured environments-such as caves, underwater domains, disaster sites, and battlefields-are continuously exposed to sensor degeneracy, unpredictable disturbances, and physical faults. In particular, micro-electro-mechanical-systems-based inertial measurement units (IMUs), which are critical entities for autonomous motion and state estimation, are inherently vulnerable to these adverse factors. While multi-IMU redundancy offers a practical approach to improve robustness, it requires a lightweight and real-time fault detection and isolation (FDI) capability to prevent faulty measurements from degrading the overall system reliability. This study proposes a sliding-window multivariate-kernel-density-estimation-based statistical FDI framework. By pooling all sensor data within sliding windows to construct a reference distribution, the proposed method estimates probability densities to compute the per-axis sensor reliability index and the aggregated reliability index (ARI). Furthermore, an adaptive thresholding scheme combining the interquartile range of the ARI in conjunction with a median-based auxiliary threshold is employed to dynamically define the normal operating range, enabling robust detection under both single and simultaneous multisensor fault conditions. Simulation results in four fault-injection scenarios (bias step, noise amplification, gradual drift, and stuck-at), including a dual-sensor simultaneous fault case, quantitatively evaluated using the proposed reliability indices and detection metrics (detection delay, false alarm rate, missed detection rate), verify that the proposed technique rapidly identifies performance degeneracy, thereby ensuring the data integrity of unmanned vehicles. Parameter sensitivity analysis and computational cost evaluation further demonstrate the practical applicability of the proposed framework.
#kernel density estimation
#multi-IMU
#fault detection and isolation (FDI)
#reliability index
#adaptive thresholding
상세정보 수정요청해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
목차
- Abstract
- I. 서론
- II. KDE 기반 신뢰도 지수 산출 및 고장 탐지·격리 기법
- III. 시뮬레이션 및 성능 평가
- IV. 결론
- REFERENCES
참고문헌
참고문헌 신청최근 본 자료
