Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications

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

Authors
Xiujuan Qie, Jing Zhang, Jiangya Zhang
Corresponding Author
Xiujuan Qie
Available Online November 2015.
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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Volume Title
Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications
Series
Advances in Computer Science Research
Publication Date
November 2015
ISBN
10.2991/icmmita-15.2015.123
ISSN
2352-538X
DOI
10.2991/icmmita-15.2015.123How to use a DOI?
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/).

Cite this article

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  -