科技创新与应用2024，Vol.14Issue(22)：1-4,4.DOI:10.19981/j.CN23-1581/G3.2024.22.001
基于CNN-SVM的船舶电力推进系统故障诊断技术研究
摘要
Abstract
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Key words
ship/electric propulsion system/fault diagnosis/feature extraction/signal分类
交通工程引用本文复制引用
任世超,邢高举,李浩..基于CNN-SVM的船舶电力推进系统故障诊断技术研究[J].科技创新与应用,2024,14(22):1-4,4.
