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
- 2024.3
- 수록면
- 56 - 63 (8page)
- DOI
- 10.33162/JAR.2024.3.24.1.056
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초록· 키워드
오류제보하기
Purpose: This study proposes a gas-absorbing device with high reliability and availability. To detect anomalies in the device, we applied an anomaly-detection algorithm based on artificial intelligence.
Methods: We used an LSTM-based autoencoder model to train normal data, and the model was processed to detect anomalies when the mean average error (MAE) deviated more than three times the standard deviation of its mean. To handle false alarms, we introduced a method that assigns increment and decrement scores for all anomaly and normal points and diagnoses an anomaly point when the accumulated score exceeds a designated threshold.
Results: The model could diagnose anomalies approximately four months before failure, and it could correctly diagnose a return-to-normal after repair.
Conclusion: This algorithm is suitable for systems that are employed in environments that must operate continuously every day.
Methods: We used an LSTM-based autoencoder model to train normal data, and the model was processed to detect anomalies when the mean average error (MAE) deviated more than three times the standard deviation of its mean. To handle false alarms, we introduced a method that assigns increment and decrement scores for all anomaly and normal points and diagnoses an anomaly point when the accumulated score exceeds a designated threshold.
Results: The model could diagnose anomalies approximately four months before failure, and it could correctly diagnose a return-to-normal after repair.
Conclusion: This algorithm is suitable for systems that are employed in environments that must operate continuously every day.
목차
1. 서론
2. 데이터 특성 선정
3. 이상 진단모델 설계 및 구현
4. 결과 및 기대효과
References
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