Application of Wavelet Analysis and Neural Network in Fault Diagnosis of Rolling Bearing
- 10.2991/jimet-15.2015.1How to use a DOI?
- rolling bearings; fault diagnosis; wavelet packet analysis; neural network
In this paper, a fault-diagnosis method is proposed for generator rolling bearings based on wavelet packet analysis and neural network. Acquisition of wind farm rolling bearings real-time signal under different conditions.Firstly, decomposes vibration acceleration signals use wavelet packets analysis, make the original vibration signal decomposed into different frequency bands, then calculate the energy values, so extracts energy values of various vibration signal to construct fault eigenvector; which use as the input of the neural network. Then, by the parameter setting created a BP neural network ; in order to make the network has memory classification function we need training the network.Finally, the test sample put into the already trained BP get the fault pattern recognition. Using the wind farm real-time data for simulation experimental, the results show that the fault diagnosis model of high precision, can make a fast and effective fault diagnosis for rolling bearings.
- © 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 - Li Xinli AU - Yao Wanye AU - Yang Xiao AU - Zhou Qingjie PY - 2015/12 DA - 2015/12 TI - Application of Wavelet Analysis and Neural Network in Fault Diagnosis of Rolling Bearing BT - Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference PB - Atlantis Press SP - 1 EP - 6 SN - 2352-538X UR - https://doi.org/10.2991/jimet-15.2015.1 DO - 10.2991/jimet-15.2015.1 ID - Xinli2015/12 ER -