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
Purpose: This study focuses on monitoring the functional safety and fault health of hydrogen fueling station (HRS) components, including coolers, compressors, and dispensers. Using the HRS input power quality monitoring (PQM) device, this study aims to predict anomalies in the operations of these systems.
Method: A fault detection and diagnosis model based on long short-term memory (LSTM)-autoencoder was developed. Power data for model training was collected through the GEMS monitoring system at Jingok Station. Among the condition-based maintenance (CBM) functional safety monitoring conditions, error measurement parameters such as sound, vibration, and current were analyzed to detect and diagnose errors. The LSTM-autoencoder processes this data to predict anomaly occurrences.
Results: By integrating the PQM device with the HRS power input, the CBM+ of hydrogen compressors was enhanced. Data collected over three months from the developed PQM measurement device installed in the Gwangju HRS input power source revealed that the LSTM-autoencoder model could predict HRS failures approximately seven days in advance.
Conclusion: The LSTM-autoencoder model, trained on power data from the GEMS monitoring system at Jinkok Station in Gwangju, demonstrated accurate failure detection and early warning
capabilities. It provides alerts 1–7 days before potential failures, enhancing the reliability and safety of HRS operations.
Method: A fault detection and diagnosis model based on long short-term memory (LSTM)-autoencoder was developed. Power data for model training was collected through the GEMS monitoring system at Jingok Station. Among the condition-based maintenance (CBM) functional safety monitoring conditions, error measurement parameters such as sound, vibration, and current were analyzed to detect and diagnose errors. The LSTM-autoencoder processes this data to predict anomaly occurrences.
Results: By integrating the PQM device with the HRS power input, the CBM+ of hydrogen compressors was enhanced. Data collected over three months from the developed PQM measurement device installed in the Gwangju HRS input power source revealed that the LSTM-autoencoder model could predict HRS failures approximately seven days in advance.
Conclusion: The LSTM-autoencoder model, trained on power data from the GEMS monitoring system at Jinkok Station in Gwangju, demonstrated accurate failure detection and early warning
capabilities. It provides alerts 1–7 days before potential failures, enhancing the reliability and safety of HRS operations.
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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UCI(KEPA) : I410-151-25-02-091261996
