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
- 학술대회자료
- 저자정보
- 발행연도
- 2023.10
- 수록면
- 700 - 705 (6page)
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초록· 키워드
오류제보하기
Fault detection and identification (FDI) is essential for ensuring the safety of mechanical systems. Typically, FDI algorithms begin by establishing fault detection methods and subsequently identifying the faults. The fault detection process aims to detect abnormalities within the system. To initiate the fault detection process, a mathematical model is required for comparison. However, this mathematical model may not be fully known in advance. In this study, we propose the implementation of Gaussian process regression (GPR) to learn the current nominal model using sensor datasets. The learned model is then compared with newly acquired data to detect any occurrence of faults. GPR learns the model using a nonparametric Bayesian kernel defined in the reproducing kernel Hilbert space. Consequently, the kernel linear independence (KLI) test is employed to examine the new data set for fault detection. The simulation environment incorporates the NASA-developed generic fixed-wing aircraft transport model (GTM), specifically considering cases involving severed left-wing configurations for simulation purposes.
목차
Abstract
1. INTRODUCTION
2. AIRCRAFT MODEL
3. FAULT DIAGNOSIS
4. SIMULATION RESULTS
5. CONCLUSION
REFERENCES
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