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

作　　者：赵南洋 刘超[1] 杜文龙 蒋东翔[1] ZHAO Nanyang;LIU Chao;DU Wenlong;JIANG Dongxiang(Department of Energy and Power Engineering,Tsinghua University,Beijing 100084,China;China Ship Development and Design Center,Wuhan 430064,China)

机构地区：[1]清华大学能源与动力工程系,北京100084 [2]中国舰船研究设计中心,湖北武汉430064

出　　处：《中国舰船研究》2025年第2期99-106,共8页Chinese Journal of Ship Research

基　　金：国家科技重大专项(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)。

摘　　要：[目的]面向舰船智能机舱的发展需求,提出一种基于长短期记忆(LSTM)神经网络预测与云重心评判的舰船柴油机健康状态评估方法,以提升舰船柴油机运维能力。[方法]该方法首先基于LSTM预测参数与实测参数的偏差,构建评估指标参数集。然后,采用层次分析法确定各参数的权重,并使用云重心评判法对柴油机健康状态进行评估。最后,采用实际舰船柴油机前期正常数据和后期退化数据进行测试。[结果]测试结果表明,柴油机在前期正常运行状态下的评价值为99.94,对应健康状态,而在后期退化状态下的评价值为81.71,对应良好状态。这表明该方法能够有效实现柴油机健康状态的评估。[结论]所提方法可用于舰船柴油机和其他动力设备健康状态评估,具有实际应用价值。[Objective]In response to the development needs of smart engine rooms on ships,this paper proposes an assessment method for the health status of ship diesel engines.The method is based on long shortterm memory(LSTM)neural network prediction and cloud barycenter evaluation,aiming to enhance the operation and maintenance(O&M)capabilities of the engines.[Methods]First,an evaluation indicator parameter set is constructed based on the deviation between LSTM-predicted and measured parameters.Then,the analytic 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 practical 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 

分 类 号：U664.121[交通运输工程—船舶及航道工程] U672.7[交通运输工程—船舶与海洋工程]