双树复小波算法在滚动轴承故障信号特征提取中的应用

现有滚动轴承故障特征提取算法的性能会随着故障集规模扩大而出现衰减。针对故障信号间存在的干扰和模态混叠等问题，提出一种基于双树复小波的特征提取算法。双树复小波结构包含两个独立的滤波器组，在含噪混合信号的分解和重构中形成互补关系，提升信号采样的平稳性；优化双树复小波滤波器组的结构，降低故障信号平移敏感性，利用门限阈值处理高频小波系数，达到降噪的目的，并基于时间序列样本熵提取子带信号的能量特征。实验结果显示：提出的算法能够准确提取滚动轴承各部分的故障特征信息，算法的在线故障识别率达到99.56%。

Application of Dual Tree Complex Wavelet Algorithm in Feature Extraction of Rolling Bearing Fault Signal

The performance of existing rolling bearing fault feature extraction algorithms would be attenuated with the expansion of fault set.Aiming at the problems of interference and mode aliasing between fault signals,a feature extraction algorithm based on dual tree complex wavelet was proposed.Dual tree complex wavelet structure contained two independent filter banks,which formed a complementary relationship in the decomposition and reconstruction of noisy mixed signal,to improve the smoothness of signal sampling;the structure of dual tree complex wavelet filter bank was optimized to reduce the translation sensitivity of fault signal,threshold was used to process high-frequency wavelet coefficients to achieve the purpose of noise reduction,and the energy characteristics of the subband signal was extracted based on time series sample entropy.The experimental results show that the proposed algorithm can be used to accurately extract the fault feature information of each part of the rolling bearing,and the online fault recognition rate of the algorithm reaches 99.56%.