新小波阈值法与VMD相结合的滚动轴承特征提取

Feature Extraction of Rolling Bearings Based on the Combinationof New Wavelet Threshold Method and VMD

针对滚动轴承故障信号弱以及难提取等问题,提出了一种新小波阈值方法与VMD相结合的轴承故障信号特征提取方法。首先,利用一种改进的指数小波阈值函数来优化传统小波降噪方法,克服其存在间断点和恒定偏差等问题;然后,结合VMD提取滚动轴承的有效故障特征;最后,以6205-RS号轴承内圈故障数据作为原始信号进行实验验证。实验结果表明,该方法能够有效提高降噪信号的信噪比,降低均方根误差,保证滚动轴承微弱故障信号特征提取的完整性和有效性。

For the problems of rolling bearing fault signals such as weak signal intensity and difficulty in extracting,a new wave-let threshold combined with VMD fault signal feature extraction method is proposed.Firstly,a new exponential wavelet threshold function is used to improve the traditional wavelet denoising method and the problems of discontinuous points and constant devia-tions are overcame;Secondly,the variational modal decomposition(VMD)is used to extract the effective fault features of the roll-ing bearing;Finally,the vibration data of the 6205-RS bearing inner ring failure is used for experimental verification.The exper-imental results show that the proposed method can effectively improve the signal-to-noise ratio(SNR)of noise reduction signals and reduce the root mean square error(RMSE),and the completeness and effectiveness of the fault feature extraction of rolling bearings can be guaranteed.