The rotor fault prediction based on support vector regression and phase space reconstruction
- 10.2991/mmme-16.2016.196How to use a DOI?
- support vector regression; phase-space reconstruction; feature selection; particle swarm optimization; state forecast
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.
- © 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 - 10.2991/mmme-16.2016.196 ID - Han2016/10 ER -