Vibration Fault Diagnosis of Hydroelectric Unit Based on LS-SVM and Information Fusion Technology

DOI

10.2991/iceeecs-16.2016.143How to use a DOI?

Keywords

Hydroelectric Unit; Vibration; Fault Diagnosis; Support Vector Machine; Information Fusion

Abstract

Vibration fault diagnosis of hydroelectric unit was investigated using method of least square support vector machine (LS-SVM) and Dempster-Shafer theory (D-S Theory). Spectrum and amplitude characteristic was acted as eigenvector of learning samples to train the constructed LS-SVM regression and classifier for realizing mapping relationship between the fault and the characteristic. Information fusion was realized after completing local diagnosis, and then fault diagnosis was achieved. Experiments show that the method has a rapidly diagnostic process and generalization performances. It is suitable for the vibration fault diagnosis of hydroelectric unit.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qiguo Yao
AU  - Yuliang Liu
PY  - 2016/12
DA  - 2016/12
TI  - Vibration Fault Diagnosis of Hydroelectric Unit Based on LS-SVM and Information Fusion Technology
BT  - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
PB  - Atlantis Press
SP  - 720
EP  - 725
SN  - 2352-538X
UR  - https://doi.org/10.2991/iceeecs-16.2016.143
DO  - 10.2991/iceeecs-16.2016.143
ID  - Yao2016/12
ER  -