Sensor Fault Detection with Online Sparse Least Squares Support Vector Machine

被引：0
|
作者：
Guo Su [1 ,2 ]
Deng Fang [1 ,2 ]
Sun Jian [1 ,2 ]
Li Fengmei [1 ,2 ]
机构：
[1] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
[2] Key Lab Intelligent Control & Decis Complex Syst, Beijing 100081, Peoples R China
来源：
2013 32ND CHINESE CONTROL CONFERENCE (CCC) | 2013年
关键词：
sensor fault detection; OS-LSSVM; LSSVM;
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：
In this paper, we present the theory of online sparse least squares support vector machine (OS-LSSVM) for prediction and propose a predictor with OS-LSSVM to detect sensor fault. The principle of the predictor and its online algorithm are introduced. Compared with the traditional least squares support vector machine (LSSVM), OS-LSSVM has an advantage on training speed owing to the online training algorithm based on the base vector set. The real-time output data of sensor is employed as the training vector to establish the regression model. This method is compared with the LSSVM predictor in the experiment. Three typical faults of sensors are investigated and the simulation result indicates that the OS-LSSVM predictor can diagnose sensor fault accurately and rapidly, thus it is especially suitable for online sensor fault detection.
引用
收藏
页码：6220 / 6224
页数：5
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