SIA OpenIR  > 工业控制网络与系统研究室
传感器信号特征提取和故障诊断研究
Alternative TitleResearch of Feature Extraction and Fault Diagnosis for Sensor Signal
单玉刚; 胡卫国; 王宏; 袁杰
Department工业控制网络与系统研究室
Conference Name2012 31th Chinese Control Conference
Conference DateJuly 25-27, 2012
Conference PlaceHefei, China
Author of SourceTechnical Committee on Control Theory,Chinese Association of Automation
Source PublicationProceedings of the 31st Chinese Control Conference
PublisherChinese Association of Automation
Publication PlaceBeijing
2012
Pages5412-5417
Indexed ByEI ; CPCI(ISTP)
EI Accession number20130716024385
WOS IDWOS:000393905905098
Contribution Rank1
Keyword核fisher Dagsvm 传感器故障 模极大值
Abstract提出一种综合的传感器信号特征提取和故障诊断方法。利用小波包三层分解传感器信号提取各个频带的能量以及信号的模极大值构成初始特征,使用核Fisher方法将原始特征空间映射到高维特征空间,增强了样本可分性。采用可分性判别准则组织多个两分类支持向量机(SVM),构成优化的有向无环图支持向量机(DAGSVM)多分类器判别传感器状态。将该方法应用到FDT/DTM(Field device tool/device type manager)设备管理系统中,对NCS4000水循环控制系统的压力传感器进行故障诊断,数值实验表明该算法具有实效性。
Other AbstractThe paper presents an integrated approach of feature extraction and fault diagnosis of sensor.Taking the sensor signal energies and modulus maxima of wavelet packet decomposition in three bands as initial features,valid features are extracted according to kernel fisher transform of initial feature vector,enhancing the signal characteristics.In accordance with inter-class separability a set of binary SVM classifiers are combined to construct optimal Decision Directed Acyclic Graph. The approach is applied to...
Language中文
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Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/10248
Collection工业控制网络与系统研究室
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Airforce 93303, Shenyang 110043, China
3.Microcyber Inc., Shenyang 110179, China
4.School of Electrical Engineering, Xinjiang University, Urumuqi 830047, China
5.Graduate School of the Chinese Academy of Science, Beijing 100039, China
Recommended Citation
GB/T 7714
单玉刚,胡卫国,王宏,等. 传感器信号特征提取和故障诊断研究[C]//Technical Committee on Control Theory,Chinese Association of Automation. Beijing:Chinese Association of Automation,2012:5412-5417.
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