基于RF特征优选和WOA-ELM的风电齿轮箱故障诊断

Fault diagnosis of wind turbine gearbox based on RF feature optimization and WOA-ELM

针对风电机组齿轮箱故障特征提取不足,故障诊断率低问题,提出了一种基于RF特征优选,结合WOA-ELM特征识别的风电齿轮箱故障诊断方法。首先,提取风电齿轮箱时域、频域、时频域特征,构建多域高维特征集;其次,利用RF进行特征重要度排序并提取10维优选特征;最后,利用WOA优化调整ELM模型的输入权值和隐含层阈值,实现风电齿轮箱故障分类识别。将本文方法应用于风电齿轮箱故障诊断,实验结果表明,本文方法平均诊断率能达到99.81%,诊断准确率均高于对比方法且诊断用时最少,能够有效地进行风电齿轮箱故障诊断。

Aiming at the problem of insufficient wind turbine gearbox fault feature extraction and low fault diagnosis rate,a wind turbine gearbox fault diagnosis method based on RF feature optimization combined with WOA-ELM feature identification is proposed.Firstly,the wind turbine gearbox time domain,frequency domain,and time-frequency domain features are extracted to construct a multi-domain high-dimensional feature set.Secondly,the RF is used to rank the feature importance and extract 10-dimensional preferred features.Finally,the input weights and implied layer thresholds of the ELM model are optimally adjusted using WOA to achieve wind turbine gearbox fault classification and identification.The experimental results show that the average diagnosis rate of this method can reach 99.81%,and the diagnosis accuracy is higher than that of the comparison methods and the diagnosis time is the least,which can effectively diagnose the wind power gear box faults.