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
相关论文
共 50 条
  • [1] Noninvasive arteriosclerosis detection by sparse Least squares Support vector Machine
    Wang, Chen
    Liu, Haikuan
    Gan, Liangzhi
    Xia, Haodi
    [J]. 2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 2424 - 2429
  • [2] Fault diagnosis method of deep sparse least squares support vector machine
    Zhang, Rui
    Li, Ke
    Su, Lei
    Li, Wen-Rui
    [J]. Zhendong Gongcheng Xuebao/Journal of Vibration Engineering, 2019, 32 (06): : 1104 - 1113
  • [3] Algorithm of Sparse Least Squares Support Vector Machine
    Zhang, Yongli
    Zhu, Yanwei
    Lin, Shufei
    Sun, Xiujuan
    Zhang, Qiuna
    Liu, Xiaohong
    [J]. SMART MATERIALS AND INTELLIGENT SYSTEMS, PTS 1 AND 2, 2011, 143-144 : 1229 - +
  • [4] A sparse least squares support vector machine classifier
    Valyon, J
    Horváth, G
    [J]. 2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS, 2004, : 543 - 548
  • [5] Battery Fault Diagnosis Method Based on Online Least Squares Support Vector Machine
    Zhang, Tongrui
    Li, Ran
    Zhou, Yongqin
    [J]. ENERGIES, 2023, 16 (21)
  • [6] Fast sparse approximation for least squares support vector machine
    Jiao, Licheng
    Bo, Liefeng
    Wang, Ling
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 2007, 18 (03): : 685 - 697
  • [7] Sparse least squares support vector machine for function estimation
    Gan, Liang-zhi
    Liu, Hai-kuan
    Sun, You-xian
    [J]. ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1, 2006, 3971 : 1016 - 1021
  • [8] Application of Least Squares Support Vector Machine in Fault Diagnosis
    Zhang, Yongli
    Zhu, Yanwei
    Lin, Shufei
    Liu, Xiaohong
    [J]. INFORMATION COMPUTING AND APPLICATIONS, PT II, 2011, 244 : 192 - +
  • [9] Sparse Least Squares Support Vector Machine With Adaptive Kernel Parameters
    Yang, Chaoyu
    Yang, Jie
    Ma, Jun
    [J]. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2020, 13 (01) : 212 - 222
  • [10] Sparse Least Squares Support Vector Machine With Adaptive Kernel Parameters
    Chaoyu Yang
    Jie Yang
    Jun Ma
    [J]. International Journal of Computational Intelligence Systems, 2020, 13 : 212 - 222
12345