摘要
针对风机转盘轴承振动信号的低频率、非平稳、非线性且微弱的特点,提出了一种新的轴承故障诊断方法。该方法将小波分析和EEMD-HHT相结合,既能够提高信号的信噪比,又能抑制经验模态分解过程中的模式混叠现象,提高故障诊断的准确性。试验证明在风电轴承的故障诊断中该方法非常有效。
Aiming at the characteristics of vibration signal of slewing bearings in wind turbines, such as low frequency, non - stationary, non - linearity and weak, a new fault diagnosis method for the bearings is proposed. The method combining the wavelet analysis and EEMD - HHT is able to improve the SNR, to inhibit the mode mixing phenomenon in the EMD decomposition process, and to improve the accuracy of fault diagnosis. The experiments show that the method is very effective in the fault diagnosis of wind turbine bearings.
出处
《轴承》
北大核心
2014年第6期45-49,共5页
Bearing
基金
国家"十二五"科技支撑计划项目(2011BAF09B02)
