现代科学仪器Issue(3)：116-122,7.
RSGWPT、MPE和ELM相结合的风电机组轴承诊断新方法
A New Diagnosis Method for Wind Turbine Bearing Combining RSGWPT,MPE with ELM
车一鸣 1王冬梅 1宋慧欣1
作者信息
- 1. 国网冀北电力有限公司技能培训中心 河北 保定 071051
- 折叠
摘要
Abstract
Aiming at solving the problems of feature extraction and condition judgment for wind turbine bearings, a new diagnosis method combining redundant second generation wavelet package transformation(RSGWPT), multiscale permutation entropy(MPE) and extreme learning machine(ELM) was proposed. Firstly, the acquired signal samples were processed by using redundant second generation wavelet package, and the corresponding subband signal components could be obtained. Then, the multiscale permutation entropy of each obtained subband signal components was calculated, and the feature vectors are constructed, which could characterize the conditions of the bearings. Finally, the feature vectors were inputted into the extreme learning machine, and the different fault types and injury degrees of the bearings could be identified automatically. The analysis results of the measured data showed that the proposed method could effectively identify the work condition of the rolling bearings, and has a certain value for engineering application.关键词
冗余第二代小波包变换/多尺度排列熵/极限学习机/风机轴承/故障诊断Key words
RSGWPT/MPE/ELM/Wind turbine bearing/Fault diagnosis分类
信息技术与安全科学引用本文复制引用
车一鸣,王冬梅,宋慧欣..RSGWPT、MPE和ELM相结合的风电机组轴承诊断新方法[J].现代科学仪器,2017,(3):116-122,7.
