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
- 학술저널
- 저자정보
- 발행연도
- 2024.12
- 수록면
- 354 - 367 (14page)
- DOI
- 10.33162/JAR.2024.12.24.4.354
이용수
초록· 키워드
오류제보하기
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.
목차
1. 서론
2. 수소충전소 운영 및 CBM 모니터링
3. LSTM-AE
4. 실험 및 결과
5. 결론
References
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