摘要
船舶动力系统是船舶运行的心脏,船舶动力系统的可靠性和使用寿命对航行安全和经济性有直接影响。以船舶动力系统多故障诊断和寿命预测为研究重点,对动力系统构成、工作原理和常见故障类型进行系统分析,确定关键子系统和零件,讨论故障影响因素。鉴于此,基于机器学习、深度学习、集成学习等,建立多种故障诊断模型,以增强故障识别的精度与鲁棒性;另外,结合退化趋势分析,构建基于统计方法、机器学习与深度学习相结合的寿命预测模型,为系统的运行与维护提供理论依据。研究结果对提高船舶动力系统的可靠性和安全性有重要的指导意义。
The ship power system is the heart of the ship operation,and the reliability and life of the ship power system have a direct impact on the navigation safety and economy.This paper focuses on the multi-fault diagnosis and life prediction of ship power system,systematically analyzes the power system composition,working principle and common fault types,determines the key subsystems and parts,and discusses the influencing factors of faults.In view of this,this paper establishes various fault diagnosis models based on machine learning,deep learning ensemble learning,etc.to enhance the accuracy and robustness of fault identification.In addition,by combining degradation trend analysis,a lifespan prediction model based on statistical methods,machine learning,and deep learning is constructed to provide a theoretical basis for the operation and maintenance of the system.The results have important guiding significance for improving the reliability and safety of ship power system.
作者
冯知超
FENG Zhichao(Zouxi Port and Shipping Service Station,Jining Port and Shipping Development Center,Shandong Province,Jining,Shandong Province,273517 China)
出处
《科技资讯》
2025年第20期106-108,共3页
Science & Technology Information
关键词
船舶动力系统
故障诊断
寿命预测
故障识别
Ship power system
Fault diagnosis
Life prediction
Fault identification
