Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering

The rotor fault prediction based on support vector regression and phase space reconstruction

Authors
Xiao Han
Corresponding Author
Xiao Han
Available Online October 2016.
DOI
https://doi.org/10.2991/mmme-16.2016.196How to use a DOI?
Keywords
support vector regression; phase-space reconstruction; feature selection; particle swarm optimization; state forecast
Abstract

Support vector regression (SVR) is a popular machine learning method that develops these years and has been widely used in the prediction field. But the input feature vectors largely affect the accuracy of the forecast er-ror, so the feature vector choice has been the hot issues of attention and research scholars. For these problems, some scholars have proposed a characteristics selection method of support vector regression machine based on the phase space reconstruction, but the value of the time delay and embedding dimension became discussion hotspot. For these problems, some scholars have proposed characteristics selection method of the support vec-tor regression machine based on phase space reconstruction, but the value of the time delay and embedding dimension became discussion hotspot. So the optimization method of particle swarm optimization (PSO) is proposed. This method is able to quickly identify the best combination of parameters ( , m, C, ) and improve forecast accuracy. This method is applied to the prediction of the rotor misalignment of rotating machinery fault data. The experiment proved that the method is feasible.

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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Volume Title
Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering
Series
Advances in Engineering Research
Publication Date
October 2016
ISBN
978-94-6252-221-3
ISSN
2352-5401
DOI
https://doi.org/10.2991/mmme-16.2016.196How to use a DOI?
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/).

Cite this article

TY  - CONF
AU  - Xiao Han
PY  - 2016/10
DA  - 2016/10
TI  - The rotor fault prediction based on support vector regression and phase space reconstruction
BT  - Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmme-16.2016.196
DO  - https://doi.org/10.2991/mmme-16.2016.196
ID  - Han2016/10
ER  -