神经网络在船舶柴油机故障诊断中的应用
闫江鹏 1陆安山1
作者信息
- 1. 北部湾大学 机械与船舶海洋工程学院,广西 钦州 535000
- 折叠
摘要
在船舶航行的过程中,利用加速度传感器和动态信号采集仪,将船舶柴油机航行实时数据进行采集,对其分析处理,从而判断船舶动力装置将会出现的问题,并及时解决.文章的船舶柴油机故障诊断系统利用主成分分析(PCA)降维对数据进行预处理,将降维后的数据通过K-means聚类分析,再将聚类后的数据输送入径向基函数(RBF)神经网络进行训练,从而得到具体的故障类型,提升了故障诊断的效率和准确性.
Abstract
This paper used the acceleration sensor and dynamic signal acquisition instrument during the course of ship sailing to collect,analyze and process the real-time sailing data of marine diesel engine,judge the problems that will occur in the marine power plant and solve them in time.The fault diagnosis system of the marine diesel en-gines used the dimensionality reduction technology of principal component analysis(PCA)to preprocess the data,analyze them with K-means clustering method after the dimensionality reduction,and input the clustered data into radial basis function(RBF)neural network for training to obtain detailed information of fault type,which improved the efficiency and accuracy of fault diagnosis.
关键词
径向基函数神经网络/主成分分析/K-means聚类Key words
radial basis function neural network/principal component analysis/K-means clustering引用本文复制引用
基金项目
钦州市科技开发计划项目(202014804)
出版年
2024
NETL
NSTL
万方数据