中国舰船研究2025，Vol.20Issue(2)：99-106,8.DOI:10.19693/j.issn.1673-3185.04077
基于LSTM预测与云重心评判的舰船柴油机健康状态评估
Health status assessment for ship diesel engines based on LSTM prediction and cloud barycenter model
摘要
Abstract
[Objective]In response to the development needs of smart engine rooms on ships,this paper pro-poses an assessment method for the health status of ship diesel engines.The method is based on long short-term memory(LSTM)neural network prediction and cloud barycenter evaluation,aiming to enhance the oper-ation and maintenance(O&M)capabilities of the engines.[Methods]First,an evaluation indicator paramet-er set is constructed based on the deviation between LSTM-predicted and measured parameters.Then,the ana-lytic hierarchy process is used to construct parameter weights,and the cloud barycenter evaluation method is employed to assess the health status of the diesel engine.Finally,tests are conducted using actual ship diesel engine data from both the early normal and later degradation periods.[Results]The results indicate that the evaluation value of the diesel engine in the early normal state is 99.94(healthy),while in the later degradation state,it is 81.71(good),achieving the goal of health status assessment.[Conclusion]The proposed method can be applied to the health status assessment of ship diesel engines and other power equipment,offering prac-tical application value.关键词
柴油机/船用发动机/健康状态评估/参数预测/云重心评判Key words
diesel engines/marine engines/health status assessment/parameter prediction/cloud bary-center model分类
交通运输引用本文复制引用
赵南洋,刘超,杜文龙,蒋东翔..基于LSTM预测与云重心评判的舰船柴油机健康状态评估[J].中国舰船研究,2025,20(2):99-106,8.基金项目
国家科技重大专项(Y2019-I-0002-0003) （Y2019-I-0002-0003）
航空发动机及燃气轮机基础科学中心资助项目(P2022-C-I-002-001) （P2022-C-I-002-001）
