
해군 함정 추진계통의 상태기반정비 적용을 위한 요소 체계 분석연구
Ⓒ 2025 Korea Society for Naval Science & Technology
초록
해군 함정 추진계통은 가혹한 운용 환경으로 인해 기존 예방정비 체계의 한계와 과도정비ㆍ돌발고장 문제가 공존한다. 본 연구는 가스터빈, 감속기어, 추진전동기의 열화 메커니즘과 물리적 신호의 대응 관계를 분석하여 핵심 상태지표와 CBM 계층적 구조를 정립하였다. 이를 바탕으로 장비군별 우선 적용 순서와 단계적으로 도입 로드맵을 제안하고, CBM 도입의 타당성을 제시하였다.
Abstract
Naval ship propulsion systems operate under harsh conditions, where the limitations of conventional preventive maintenance lead to the coexistence of over-maintenance and unexpected failures. This study analyzes the relationship between degradation mechanisms and physical signals in gas turbines, reduction gears, and propulsion motors, establishing key condition indicators and a hierarchical CBM framework. Based on this framework, a prioritized application order and a phased implementation roadmap are proposed to demonstrate the practical feasibility of CBM adoption.
Keywords:
Condition-based Maintenance, Naval Propulsion System, Degradation Mechanism, Condition lndicator, Maintenance Decision-Making키워드:
상태기반정비, 함정 추진계통, 열화 메커니즘, 상태지표, 정비 의사결정Acknowledgments
이 논문은 2025년도 한화시스템(주)의 재원을 지원받아 수행된 연구임.
References
-
Cipollini, F., Oneto, L., Coraddu, A., Murphy, A. J., & Anguita, D. (2018). Condition-based Maintenance of Naval Propulsion Systems: Data Analysis with Minimal Feedback. Reliability Engineering & System Safety, 177, 12–23.
[https://doi.org/10.1016/j.ress.2018.04.018]
-
Sahraoui, M. A., Al-Khawli, T., Akilu, Y., Ameen, M. M., & Al-Azzawi, M. N. (2024). Advancing Condition-based Maintenance of Naval Propulsion Systems with Ensemble Learning Techniques. Advances in Mechanical Engineering, 16(11), 16878132241298372.
[https://doi.org/10.1177/16878132241298373]
-
Maione, F., Martino, G., & Palmieri, L. (2024). A Machine Learning Framework for Condition-based Maintenance. Algorithms, 17(9), 411.
[https://doi.org/10.3390/a17090411]
-
Elmdoost-Gashti, M., Shafiee, M., & Bozorgi-Amiri, A. (2024). Enhancing Resilience in Marine Propulsion Systems by Adopting Machine Learning Technology for Predicting Failures and Prioritising Maintenance Activities. Journal of Marine Engineering & Technology. 23(1), 18-32.
[https://doi.org/10.1080/20464177.2023.2243748]
-
Anantharaman, M. (2019). Development of a Condition Based Maintenance Model for a Vessel’s Main Propulsion Engine. Master’s Thesis, University of Tasmania.
[https://doi.org/10.25959/23238047]
-
Southgate, J. M., Groth, K. M., Sandborn, P. A., & Azarm, S. (2024). Cost-benefit Analysis Using Modular Dynamic Fault Tree Analysis and Monte Carlo Simulations for Condition-based Maintenance of Unmanned Systems. arXiv preprint, arXiv:2405.09519, .
[https://doi.org/10.48550/arXiv.2405.09519]
-
Zhu, Z., & Xiang, Y. (2019). Condition-based Maintenance for Multi-component Systems: Modeling, Structural Properties, and Algorithms. arXiv preprint, arXiv:1907.01031, .
[https://doi.org/10.48550/arXiv.1907.01031]
-
Zhu, T., Ran, Y., Zhou, X., & Wen, Y. (2019). A Survey of Predictive Maintenance: Systems, Purposes and Approaches. arXiv preprint, arXiv:1912.07383, .
[https://doi.org/10.48550/arXiv.1912.07383]
- E. Kplim (2020s). Maintenance of Naval Propulsion Plants Data Set. Kaggle Dataset. https://www.kaggle.com/datasets/elikplim/maintenance-of-naval-propulsion-plants-data-set
-
Javadnejad, F. (2025). Predictive Maintenance in Naval Vessel Propulsion Systems for Enhanced Marine Operations Using a BiGMM-HMM Framework with Divergence-based Clustering. Journal of Marine Science and Application. 2, 33.
[https://doi.org/10.1007/s44289-025-00069-2]