基于CNN-SVM的船舶电力推进系统故障诊断技术研究  

Research on Fault Diagnosis Technology of Marine Electric Propulsion System Based on CNN-SVM

作　　者：任世超 邢高举 李浩 REN Shichao;XING Gaoju;LI Hao

机构地区：[1]郑州机电工程研究所,郑州450000

出　　处：《科技创新与应用》2024年第22期1-4,共4页Technology Innovation and Application

摘　　要：综合电力推进系统是现代船舶技术的跨越式发展,对解决船舶动力平台问题具有重要意义。为避免因电气设备运行故障对船舶运行安全性的影响,该文研究基于卷积神经网络和支持向量机融合的故障诊断方法。通过CNN来提取船舶电力推进系统故障信号的深层特征,将其作为故障分类器的输入,然后由SVM分类器进行故障分类。通过仿真实验后,发现学习率为0.001时,惩罚因子为1.5时,对应的故障诊断准确率最高,抗干扰能力较强。利用CNN和SVM融合的故障诊断方法,可有效提升船舶电力推进系统电气设备运行的可靠性,根据船舶电气系统运行特点,不断完善故障诊断方法,进一步推进我国船舶技术的发展进程。Integrated electric propulsion system is a great-leap-forward development of modern ship technology,which is of great significance to solve the problem of ship power platform.In order to avoid the influence of electrical equipment failure on the safety of ship operation,this paper studies the fault diagnosis method based on the fusion of convolution neural network and support vector machine.The deep features of the fault signals of the marine electric propulsion system are extracted by CNN and used as the input of the fault classifier,and then the fault is classified by the SVM classifier.Through the simulation experiment,it is found that when the learning rate is 0.001 and the penalty factor is 1.5,the corresponding fault diagnosis accuracy is the highest and the anti-interference ability is strong.The fault diagnosis method based on the integration of CNN and SVM can effectively improve the reliability of the operation of electrical equipment in marine electric propulsion system.According to the operation characteristics of marine electrical system,the fault diagnosis method can be continuously improved,and the development process of ship technology in our country can be further promoted.

关 键 词：船舶 电力推进系统 故障诊断 特征提取 信号 

分 类 号：U672[交通运输工程—船舶及航道工程]