Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)

Application of LS-SVM in fault Diagnosis for Diesel Generator Set of Marine Power Station

Authors
Li wei Chen, Huan Liu
Corresponding Author
Li wei Chen
Available Online August 2013.
DOI
https://doi.org/10.2991/icacsei.2013.25How to use a DOI?
Keywords
LS-SVM, fault diagnosis, marine power station.
Abstract
In this paper support vector machine (SVM) algorithm being discussed, then discussed least squares support vector machine(LS-SVM) algorithm, at the same time, the applications of SVM in the fault diagnosis of turbocharger system for diesel generator set of marine power station being discussed, the least squares support vector machine algorithm being used in the research of fault diagnosis, being compared with BP neural network, experiments result show the operation speed of the least squares support vector machine algorithm is fast, its generalization ability is stronger, SVM can solve small sample learning problems as well as no-linear, high dimension and local minimization problems in the fault diagnosis of turbocharger system for diesel generator set of marine power station.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)
Part of series
Advances in Intelligent Systems Research
Publication Date
August 2013
ISBN
978-90-78677-74-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/icacsei.2013.25How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Li wei Chen
AU  - Huan Liu
PY  - 2013/08
DA  - 2013/08
TI  - Application of LS-SVM in fault Diagnosis for Diesel Generator Set of Marine Power Station
BT  - 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/icacsei.2013.25
DO  - https://doi.org/10.2991/icacsei.2013.25
ID  - Chen2013/08
ER  -