논문 상세보기
Development of a New Prediction Alarm Algorithm Applicable to Pumped Storage Power Plant KCI 등재
양수발전 설비에 적용 가능한 새로운 고장 예측경보 알고리즘 개발
- 언어KOR
- URLhttps://db.koreascholar.com/Article/Detail/423147
pp.133-142
The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.
2. 이론적 배경
2.1 예측경보 시스템의 정의
2.2 예측경보 알고리즘 종류
2.3 기존 연구의 고찰
3. 새로운 고장 예측경보 알고리즘 개발
3.1 데이터 분석구간 설정
3.2 기존 알고리즘 적용 가능성 탐색
3.3 새로운 알고리즘 개발
4. 결론
Acknowledgement
References
