Research of the Machinery Fault Diagnosis and Prediction Based on Support Vector Machine

DOI

10.2991/icmmita-15.2015.123How to use a DOI?

Keywords

Machinery fault diagnosis; Fault trend prediction; Support vector machine

Abstract

This paper analyzes the theory of support vector machines-SVM and discusses the algorithms of SVM classification and regression. After overviewed the SVM application research on machinery fault diagnosis and prediction recently, it discusses the ,erits and deficiencies of SVM and the points out the bright application research on machinery fault diagnosis and prediction. It presents the SVM model for machine condition trend prediction. It is proved that SVM model has good predict ability for long time period by comparing the AR model and SVM model for a test system vibration signal.

Copyright

© 2015, 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  - Xiujuan Qie
AU  - Jing Zhang
AU  - Jiangya Zhang
PY  - 2015/11
DA  - 2015/11
TI  - Research of the Machinery Fault Diagnosis and Prediction Based on Support Vector Machine
BT  - Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 635
EP  - 639
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-15.2015.123
DO  - 10.2991/icmmita-15.2015.123
ID  - Qie2015/11
ER  -