摘要
针对当前船舶柴油主机状态监测中故障诊断方法存在的诊断速度慢、诊断结果不稳定等缺陷,提出基于小波神经网络的船舶柴油主机状态监测中故障诊断方法。首先分析当前船舶柴油主机状态监测中故障诊断方法的研究现状,然后采用小波变换抽取船舶柴油主机状态监测中的故障特征,并采用神经网络对船舶柴油主机状态监测中故障特征进行学习,实现船舶柴油主机状态监测中故障诊断的分类决策,最后船舶柴油主机状态监测中故障诊断结果表明,本文方法不仅可以保证船舶柴油主机状态监测中故障诊断正确率,同时减少了船舶柴油主机状态监测中故障诊断时间,极大改善了船舶柴油主机状态监测中故障诊断效率。
Aiming at the shortcomings of slow diagnostic speed and unstable diagnostic results, a fault diagnosis method based on wavelet neural network for marine diesel engine condition monitoring is proposed. Firstly, the research status of fault diagnosis methods in marine diesel engine condition monitoring is analyzed. Then, wavelet transform is used to extract the fault features in marine diesel engine condition monitoring, and neural network is used to learn the fault features in marine diesel engine condition monitoring to realize fault diagnosis in marine diesel engine condition monitoring. Finally, the fault diagnosis results of marine diesel engine condition monitoring show that this paper can not only ensure the correct rate of fault diagnosis in marine diesel engine condition monitoring, but also reduce the time of fault diagnosis in marine diesel engine condition monitoring, and greatly improve the efficiency of fault diagnosis in marine diesel engine condition monitoring.
作者
李添翼
LI Tian-yi(Wujin Branch of Jiangsu Union Technical Institute, Changzhou 213161, China)
出处
《舰船科学技术》
北大核心
2018年第12X期91-93,共3页
Ship Science and Technology
关键词
船舶柴油
主机状态监测
故障诊断
故障特征
小波变换
marine diesel
host condition monitoring
fault diagnosis
fault characteristics
wavelet transform
作者简介
李添翼(1968–),男,硕士,高级讲师,主要研究方向为机械机电类专业教学与教学管理。
