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:
    目的 面向舰船智能机舱的发展需求,提出一种基于长短时记忆(long short-term memory, 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的健康状态进行评估。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。

     

    Abstract:
    Objectives 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 short-term memory (LSTM) neural network prediction and cloud barycenter evaluation, aiming to enhance the operational and maintenance 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.
    Conclusions The proposed method can be applied to the health status assessment of ship diesel engines and other power equipment, offering practical application value.

     

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