Fault detection of oil pump based on classify support vector machine

被引：0
作者：
Tian, Jingwen [1 ]
Gao, Meijuan [1 ]
Li, Kai [2 ]
Zhou, Hao [2 ]
机构：
[1] Beijing Union Univ, Dept Automat Control, Beijing, Peoples R China
[2] Beijing Univ Chem Technol, Sch Informat Sci, Beijing, Peoples R China
来源：
2007 IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION, VOLS 1-7 | 2007年
关键词：
statistical learning theory; support vector machine; oil pump; fault detection;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Statistical learning theory is introduced to fault detection of off pump. Considering the issues that the relationship between the fault of oil pump existent and fault information is a complicated and nonlinear system, and it is very difficult to found the process model to describe it The support vector machine (SVM) has the ability of strong nonlinear function approach and the ability of strong generalization and also has the feature of global optimization. In this paper, a fault detection method of oil pump based on SVM is presented, moreover, the genetic algorithm(GA) was used to optimize SVM parameters. With the ability of strong self-learning and well generalization of SVM, the detection method can truly diagnosticate the fault of oil pump by learning the fault information of oil pump. The real detection results show that this method is feasible and effective.
引用
收藏
页码：3040 / +
页数：2
相关论文
共 50 条
[11]   Efficient fault detection using support vector machine based hybrid expert system [J].
Kishore B. ;
Satyanarayana M.R.S. ;
Sujatha K. .
International Journal of System Assurance Engineering and Management, 2016, 7 (Suppl 1) :34-40
[12]   Support Vector Machine Observer Based Fault Detection for Reconfigurable Manipulators [J].
Zhao Bo ;
Dong Bo ;
Li Yuanchun .
2011 30TH CHINESE CONTROL CONFERENCE (CCC), 2011, :3979-3984
[13]   Fault detection based on a robust one class support vector machine [J].
Yin, Shen ;
Zhu, Xiangping ;
Jing, Chen .
NEUROCOMPUTING, 2014, 145 :263-268
[14]   EFFECT OF KERNEL FUNCTION IN SUPPORT VECTOR MACHINE FOR THE FAULT DIAGNOSIS OF PUMP [J].
Sakthivel, N. R. ;
Saravanamurugan, S. ;
Nair, Binoy B. ;
Elangovan, M. ;
Sugumaran, V. .
JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY, 2016, 11 (06) :826-838
[15]   Intelligent fault detection and analysis based on support vector machine and applications to Aeroengine [J].
Ren, Hongquan ;
Fan, Quan-Yong ;
Song, Xuekui ;
Li, Hongxia .
2022 34TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2022, :2680-2685
[16]   Research of Web Classification Mining Based on Classify Support Vector Machine [J].
Gao, Meijuan ;
Tian, Jingwen ;
Zhou, Shiru .
2009 ISECS INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT, VOL II, 2009, :21-24
[17]   Fed-batch fermentation penicillin process fault diagnosis and detection based on support vector machine [J].
Yang, Chengming ;
Hou, Jian .
NEUROCOMPUTING, 2016, 190 :117-123
[18]   Spline regression based feature extraction for semiconductor process fault detection using support vector machine [J].
Park, Jonghyuck ;
Kwon, Ick-Hyun ;
Kim, Sung-Shick ;
Baek, Jun-Geol .
EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (05) :5711-5718
[19]   Oil spill detection by a support vector machine based on polarization decomposition characteristics [J].
ZOU Yarong ;
SHI Lijian ;
ZHANG Shengli ;
LIANG Chao ;
ZENG Tao .
Acta Oceanologica Sinica, 2016, 35 (09) :86-90
[20]   Oil spill detection by a support vector machine based on polarization decomposition characteristics [J].
Zou Yarong ;
Shi Lijian ;
Zhang Shengli ;
Liang Chao ;
Zeng Tao .
ACTA OCEANOLOGICA SINICA, 2016, 35 (09) :86-90
12345