Researching | Health status assessment for ship diesel engines based on LSTM prediction and cloud barycenter model

Chinese Journal of Ship Research, Volume. 20, Issue 2, 99(2025)

Health status assessment for ship diesel engines based on LSTM prediction and cloud barycenter model

Nanyang ZHAO¹, Chao LIU¹, Wenlong DU², and Dongxiang JIANG¹

Author Affiliations

¹Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China

²China Ship Development and Design Center, Wuhan 430064, China

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References(4)

[1] LIU Z C, ZHANG C X, DONG E Z et al. Research progress and development trend of prognostics and health management key technologies for equipment diesel engine[J]. Processes, 11, 1972(2023).

[2] TSITSILONIS K M, THEOTOKATOS G, PATIL C et al. Health assessment framework of marine engines enabled by digital twins[J]. International Journal of Engine Research, 24, 3264-3281(2023).

[7] LIU Y S, KANG J S, WEN L et al. Health status assessment of diesel engine valve clearance based on BFA-BOA-VMD adaptive noise reduction and multi-channel information fusion[J]. Sensors, 22, 8129(2022).

[8] EWEES A A, AL-QANESS M A A, ABUALIGAH L et al. HBO-LSTM: optimized long short term memory with heap-based optimizer for wind power forecasting[J]. Energy Conversion and Management, 268, 116022(2022).

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Nanyang ZHAO, Chao LIU, Wenlong DU, Dongxiang JIANG. Health status assessment for ship diesel engines based on LSTM prediction and cloud barycenter model[J]. Chinese Journal of Ship Research, 2025, 20(2): 99

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Paper Information

Category: Ship Intelligent O&M, and Fault Diagnosis

Received: Jul. 24, 2023

Accepted: --

Published Online: May. 15, 2025

The Author Email:

DOI:10.19693/j.issn.1673-3185.04077

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