摘要
现有的舰船电气系统无法准确区分不同的故障特征,为提高故障检测结果的准确率,设计模糊C均值聚类算法在舰船电气系统故障自动检测中的应用方法。建立舰船电气系统故障特征模型,基于模糊C均值聚类算法重分类故障特征分量,设计电气系统故障检测算法,实现舰船电气系统的故障自动检测。在实验中将其与传统的2种方法对比,实验数据显示,该检测方法在5种特征中的检测准确率均高于98%,明显高于其他2种方法,因此可知该方法实现了检测准确率的优化。
In order to improve the accuracy of fault detection results,an application method of fuzzy c-means clustering algorithm in automatic fault detection of ship electrical system is designed.The fault characteristic model of ship electrical system is established,the fault characteristic components are reclassified based on fuzzy c-means clustering algorithm,and the electrical system fault detection algorithm is designed to realize the automatic fault detection of ship electrical system.In the experiment,it is compared with the traditional two methods.The experimental data show that the detection accuracy of this method in the five features is higher than 98%,which is significantly higher than the other two methods.Therefore,it can be seen that this method realizes the optimization of detection accuracy.
作者
卫敏
杨华
WEI Min;YANG Hua(Shanxi Polytechnic College,Taiyuan 030006,China;Shanxi Agricultural University,Jinzhong 030801,China)
出处
《舰船科学技术》
北大核心
2021年第24期214-216,共3页
Ship Science and Technology
基金
国家自然科学基金面上项目(31671571)
关键词
模糊C均值聚类算法
舰船
电气系统
故障检测
故障自动检测
fuzzy c-means clustering algorithm
warship
electrical system
fault detection
automatic fault detection
