重要通知
关闭窗口

本刊全部实行网上在线投稿,稿件格式参见下载专区的论文模板。投稿时作者请实名注册,以便查询、管理稿件!

用户登录
期刊信息
  • 主管单位:
  • 上海市教育委员会
  • 主办单位:
  • 上海理工大学、上海市能源研究会、上海电气(集团)总公司
  • 主    编:
  • 陈康民
  • 地    址:
  • 上海市军工路516号
  • 邮政编码:
  • 200093
  • 联系电话:
  • 021-55272843
  • 电子邮件:
  • eribjb@usst.edu.cn
  • 国际标准刊号:
  • 1008-8857
  • 国内统一刊号:
  • 31-1410/TK
  • 邮发代号:
  • 单    价:
  • 5.00
  • 定    价:
  • 20.00
基于LSSVM的制冷系统故障诊断
Fault Diagnosis for Refrigeration System Based on LS-SVM
投稿时间:2015-12-08  
DOI:10.13259/j.cnki.eri.2017.01.001
中文关键词:  制冷系统  故障诊断  最小二乘支持向量机  误差反向传播  支持向量机
英文关键词:refrigeration system  fault diagnosis  least squares support vector machine  error back-propagation  support vector machine
基金项目:国家自然科学基金项目(51506125)
作者单位E-mail
卿红 上海理工大学 能源与动力工程学院, 上海 200093  
韩华 上海理工大学 能源与动力工程学院, 上海 200093  
崔晓钰 上海理工大学 能源与动力工程学院, 上海 200093 usstxy_cui@126.com 
摘要点击次数: 268
全文下载次数: 308
中文摘要:
      为了提高制冷系统故障诊断速度及准确性,提出了基于最小二乘支持向量机(LS-SVM)的制冷系统故障诊断模型,并采用ASHRAE制冷系统故障模拟实验数据进行模型训练与验证.对一台 90冷吨(约316 kW)的离心式冷水机组的7类制冷循环典型故障进行了实验.研究结果表明,LS-SVM模型对制冷系统七类故障的总体诊断正确率比支持向量机(SVM)诊断模型、误差反向传播(BP)神经网络诊断模型分别提高0.12%和1.32%;尽管对个别局部故障(冷凝器结垢、冷凝器水流量不足、制冷剂含不凝性气体)的诊断性能较SVM模型的略有下降,但对系统故障的诊断性能均有较大改善,特别是对制冷剂泄漏/不足故障;诊断耗时比SVM模型减少近一半,快速性亦有所改善.可见,LS-SVM模型在制冷系统故障诊断中具有良好的应用前景.
英文摘要:
      In order to improve the fault diagnosis speed and accuracy for refrigeration system,a fault diagnosis model based on least squares support vector machine(LS-SVM) was proposed.American Society of Heating,Refrigerating,and Air-conditioning Engineering(ASHRAE) refrigeration system fault simulation data was used for the model training and validation.The experiments of a centrifugal chiller of 90 tons with seven types of typical faults were conducted.The results showed that the overall diagnostic accuracy of LS-SVM model for seven types of faults increased by 0.12% and 1.32% respectively,compared with support vector machine(SVM) diagnosis model and error back-propagation(BP) neural network model.Although diagnostic performance of LS-SVM model for individual component-level fault(ConFoul/ReduCF/NonCon) was low slightly compared with SVM model,the diagnosis performance for system-level were greatly improved,especially for refrigerant leakage or lack of refrigerant.The diagnosis time of LS-SVM model reduced nearly half than that of SVM model.At the same time,its rapidity improved.Therefore,LS-SVM diagnostic model had good application in the fault diagnosis of refrigeration system.
HTML   查看全文  查看/发表评论  下载PDF阅读器
关闭