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
Condition-based diagnostic analysis of air compressors of railway vehicles is essential for passenger safety and maintenance cost reduction. When conventional long short-term memory (LSTM) autoencoders are used for detecting anomalies in time series data, the input data cannot be reconstructed appropriately when the sequence length is large. Recently, the attention mechanism was proposed to solve this problem encountered in LSTM autoencoders. In the current study, the anomaly detection performance of railway vehicle air compressors was improved using the attention mechanism. The data reconstruction performance of the LSTM autoencoder model was compared with that of the LSTM autoencoder model with the attention mechanism and the transformer model comprising only the attention mechanism by using the sensor data of an air compressor of the Airport Railroad Express train in Seoul, Korea. For artificially generated abnormal data, three anomaly scenarios were successfully detected using the transformer model, which exhibited the best data reconstruction performance among the three models. The results confirmed that transformer models with attention mechanisms can detect anomalies in the air compressors of railway vehicles in a timely manner.
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.
최근 본 자료 전체보기
UCI(KEPA) : I410-ECN-0101-2023-003-001319603
