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 investigates the feasibility of applying artificial intelligence (AI) techniques for monitoring railway impedance bonds, which play a critical role in isolating traction return currents from track circuit signals. Four sensing indicators—terminal voltage, traction return current, frequency-domain response, and thermal gradient between the interior and exterior of the bond housing—were used as representative parameters. To reproduce both normal and fault conditions, a Simulink-based simulation model was implemented, generating 400 datasets for AI model training. The generated dataset was utilized to evaluate four representative AI architectures: CNN, LSTM, MLP, and a hybrid CNN-LSTM model. Through 10-fold cross-validation, CNN and CNN-LSTM exhibited the highest accuracy and AUC values, both approaching 1.0, indicating superior classification performance. In contrast, the MLP model, designed as a lightweight baseline, showed limited discriminative power, while the LSTM effectively captured temporal dependencies but required significantly longer training time. These results demonstrate that AI-driven monitoring is applicable to impedance bond condition assessment, complementing conventional threshold-based diagnostic techniques. Moreover, the proposed approach can serve as a foundation for predictive maintenance systems and contribute to enhancing the overall safety and operational reliability of railway signaling infrastructure.
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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