摘要
以一台制冷量为90冷t(约316 kW)、制冷剂为R134a的离心式制冷机组为实验对象,从理论上分析该制冷系统的7种典型故障,分析故障征兆与故障间的理论关系,运用基于顺序向前选择(SFFS)算法的封装模型进行特征选择,降低乃至消除特征间的相关度,去除信息冗余,获得不同的能较好表征故障的特征子集.结果显示:运用SFFS算法时选择了22个特征,诊断正确率为89.63%,与原特征集的诊断正确率90.36%基本相当,极大地减少了原特征集的特征数,从64维降为22维;在保证故障检测与诊断正确率的前提下,减少了诊断所需传感器种类和数量,节约了初始投入成本.
An refrigeration system with a 90t centrifugal chiller using R134 a as refrigerant and its seven typical faults were analyzed theoretically.The relationship between the symptoms and faults was attained.The encapsulation model based on sequential forward order feature selection（SFFS）algorithm was adopted for feature selection,which could find better feature subset for reducing or even removing the feature correlation and eliminating the redundancy.The results showed that 22 features were selected by SFFS algorithm and diagnosis accuracy of 89.63% was achieved,which was close to the diagnosis accuracy of 90.36% for original feature set.But it could significantly eliminate the features of original feature set from 64 to 22.Due to the guarantee of the accuracy of fault detection and diagnosis,the type and quantity of sensor could be reduced.The first investment cost could be saved.
出处
《能源研究与信息》
2016年第2期90-95,共6页
Energy Research and Information
关键词
制冷系统
顺序向前选择算法
故障诊断
refrigeration system
sequential forward order feature selection algorithm
fault diagnosis
