@inproceedings{8342999eb70c4af685cc7c3a9fae844c,
title = "Sensor fault detection with online sparse least squares support vector machine",
abstract = "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.",
keywords = "LSSVM, OS-LSSVM, sensor fault detection",
author = "Guo Su and Deng Fang and Sun Jian and Fengmei Li",
year = "2013",
month = oct,
day = "18",
language = "English",
isbn = "9789881563835",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "6220--6224",
booktitle = "Proceedings of the 32nd Chinese Control Conference, CCC 2013",
address = "United States",
note = "32nd Chinese Control Conference, CCC 2013 ; Conference date: 26-07-2013 Through 28-07-2013",
}