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.
초록·키워드
This paper presents an Intelligent Digital Twin (IDT) framework designed for real-time Structural Health Monitoring (SHM) and optimization of mechanical systems. The proposed approach integrates high-frequency sensor data with a physics-based digital model to provide accurate, continuous assessment of structural integrity. Sensor inputs from vibration, strain, and temperature measurements are preprocessed and transformed into damage-sensitive features, which are assimilated into a high-fidelity finite element digital twin using Kalman filtering for precise state estimation. An anomaly detection module evaluates residuals between measured and predicted responses to identify potential faults, while a reinforcement learning (RL) agent operates within the updated digital twin to learn optimal maintenance and control strategies that minimize structural degradation and operational costs. The framework is implemented and tested on a scaled mechanical testbed subjected to dynamic loading. Experimental results demonstrate significant performance improvements compared to conventional SHM methods, achieving 96.8% detection accuracy, 95.6% precision, 96.1% recall, and a 14.7% optimization gain, along with reduced response latency of 135 ms. These outcomes highlight the effectiveness of combining digital twin technology with RL-driven decision-making to create adaptive, proactive, and efficient SHM systems suitable for long-term industrial deployment.
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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