[1]张龙,陈宸,韩宁,等.压缩感知理论中的建筑电气系统故障诊断[J].智能系统学报,2014,9(2):204-209.[doi:10.3969/j.issn.1673-4785.201310026]
ZHANG Long,CHEN Chen,HAN Ning,et al.Fault diagnosis of electrical systems in buildingsbased on compressed sensing[J].CAAI Transactions on Intelligent Systems,2014,9(2):204-209.[doi:10.3969/j.issn.1673-4785.201310026]
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ZHANG Long,CHEN Chen,HAN Ning,et al.Fault diagnosis of electrical systems in buildingsbased on compressed sensing[J].CAAI Transactions on Intelligent Systems,2014,9(2):204-209.[doi:10.3969/j.issn.1673-4785.201310026]
压缩感知理论中的建筑电气系统故障诊断
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
9
期数:
2014年第2期
页码:
204-209
栏目:
学术论文—智能系统
出版日期:
2014-04-25
- Title:
- Fault diagnosis of electrical systems in buildingsbased on compressed sensing
- Keywords:
- electric breakdown; fault diagnosis; support vector machines; compressed sensing; classification; feature extraction; signal reconstruction; optimization
- 分类号:
- TP18;TM743
- 摘要:
- 针对目前建筑电气系统缺少有效诊断故障方法的问题,提出一种基于压缩感知理论的建筑电气系统故障分析诊断方法,其中的关键是将故障的分类归结为一个求解待测样本对于整体训练样本的稀疏表示问题。使用建筑电气故障模拟平台数据建立了其故障诊断模型,分别采用支持向量机、l1分类器和l2分类器对系统的5种故障状态进行了诊断对比,结果表明,利用稀疏表示算法可以达到很好的诊断效果,分类准确率为96.4%,诊断运行时间0.260 1 s,可以满足小样本数据的建筑电气故障诊断工程应用的需求。
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[1]马大中,张化光,冯 健,等.一种基于多传感器信息融合的故障诊断方法[J].智能系统学报,2009,4(1):72.
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备注/Memo
收稿日期:2013-10-15。
基金项目:北京市自然科学基金资助项目(8111002)
作者简介:韩宁,女,1956年生,教授,主要研究方向为控制网络技术、智能检测与数据处理技术、建筑设备节能控制技术和森林火灾视频监测技术等。主持和参与省、部、局级纵向课题13项,目前承担国家级科研项目2项。近年来,发表学术论文30余篇,出版论著、教材多部。
通讯作者:张龙,男,1988年生,硕士研究生,主要研究领域为智能建筑、基于人工智能的故障诊断与容错控制,发表学术论文4篇。陈宸,男,1987年生,博士研究生,美国电子工程协会会员。主要研究方向为图像处理、模式识别、计算机视觉等。发表学术论文10余篇,多篇被SCI、EI收录。E-mail:long1988iacf@163.com.
更新日期/Last Update:
1900-01-01